Clausen I, Jakobsen M, Ertbjerg P and Madsen NT, 2009. Modified atmosphere packaging affects lipid oxidation, myofibrillar fragmentation index and eating quality of beef. Packaging Technology and Science, 22, 85-96. https://doi.org/10.1002/pts.828

Constantino C, de Azambuja Ribeiro EL, Bridi AM, Tarsitano MA, Koritiaki NA, Peres LM, Mizubuti IY, Pereira ES and Pimentel PG, 2012. Quality of aged ewe meat in vacuum-packaging system for different storage periods. Semina: Ciencias Agrarias, 33, 3437-3446. https://doi.org/10.5433/1679-0359.2012v33Supl2p3437

Cornu M, Billoir E, Bergis H, Beaufort A and Zuliani V, 2011. Modeling microbial competition in food: Application to the behavior of *Listeria monocytogenes* and lactic acid flora in pork meat products. Food Microbiology, 28, 639-647. https://doi.org/10.1016/j.fm.2010.08.007

Coroller L, Kan-King-Yu D, Leguerinel I, Mafart P and Membré J-M, 2012. Modelling of growth, growth/no-growth interface and nonthermal inactivation areas of *Listeria* in foods. International Journal of Food Microbiology, 152, 139-152. https://doi.org/10.1016/j.ijfoodmicro.2011.09.023

Dalgaard P and Mejlholm O, 2019. In: Bridier A (ed). Modeling Growth of *Listeria* and Lactic Acid Bacteria in Food Environments. Springer. pp. 247-264.

Dashdorj D, Amna T and Hwang I, 2015. Influence of specific taste-active components on meat flavor as affected by intrinsic and extrinsic factors: an overview. European Food Research and Technology, 241, 157-171. https://doi.org/10.1007/s00217-015-2449-3

Dashdorj D, Tripathi VK, Cho S, Kim Y and Hwang I, 2016. Dry aging of beef; Review. Journal of Animal Science and Technology, 58, 20. https://doi.org/10.1186/s40781-016-0101-9

DeGeer SL, Hunt MC, Bratcher CL, Crozier-Dodson BA, Johnson DE and Stika JF, 2009. Effects of dry aging of bone-in and boneless strip loins using two aging processes for two aging times. Meat Science, 83, 768-774. https://doi.org/10.1016/j.meatsci.2009.08.017

Delais PJ and McCurdy AR, 1990. Colonization of beef muscle surfaces by *Pseudomonas fluorescens* and *Pseudomonas fragi*. Journal of Food Science, 55(4), 898-902. https://doi.org/10.1111/j.1365-2621.1990.tb01560.x

Devine C and Dikeman M, 2014. In: C Devine and M Dikeman (eds). Encyclopedia of Meat Sciences. 2nd edn. Academic Press. pp. 1712.

Devlieghere F and Debevere J, 2000. Influence of dissolved carbon dioxide on the growth of spoilage bacteria. LWT - Food Science and Technology, 33(8), 531-537. https://doi.org/10.1006/fstl.2000.0705

Dikeman ME, Obuz E, Gok V, Akkaya L and Stroda S, 2013. Effects of dry, vacuum, and special bag aging; USDA quality grade; and end-point temperature on yields and eating quality of beef Longissimus lumborum steaks. Meat Science, 94, 228-233. https://doi.org/10.1016/j.meatsci.2013.02.002

Doulgeraki AI, Ercolini D, Villani F and Nychas G-JE, 2012. Spoilage microbiota associated to the storage of raw meat in different conditions. International Journal of Food Microbiology, 157, 130-141. https://doi.org/10.1016/j.ijfoodmicro.2012.05.020

Duraković, S. and Duraković, L. (2003). Mikologija u biotehnologiji, Zagreb, Croatia: Kugler. pp. 80-94.

Dykes GA and Moorhead SM, 2002. Combined antimicrobial effect of nisin and a listeriophage against *Listeria monocytogenes* in broth but not in buffer or on raw beef. International Journal of Food Microbiology, 73, 71-81. https://doi.org/10.1016/S0168-1605(01)00710-3

EFSA (European Food Safety Authority) and ECDC (European Centre for Disease Prevention and Control), 2021. The European Union One Health 2020 Zoonoses Report. EFSA Journal 2021;19(12):6971, 324 pp. https://doi.org/10.2903/j.efsa.2021.6971

EFSA (European Food Safety Authority), Martino, L, Aiassa, E, Halldórsson, TI, Koutsoumanis, PK; Naegeli, H, Baert, K, Baldinelli, F, Devos, Y, Lodi, F, Lostia, A, Manini, P, Merten, C, Messens, W, Rizzi, V, Tarazona, J, Titz, A and Vos, S, 2020. Draft framework for protocol development for EFSA's scientific assessments. EFSA supporting publication 2020;17(4):EN-1843, 46 pp. https://doi.org/10.2903/sp.efsa.2020.EN-1843

Ercolini D, Russo F, Torrieri E, Masi P and Villani F, 2006. Changes in the spoilage-related microbiota of beef during refrigerated storage under different packaging conditions. Applied and Environmental Microbiology, 72, 4663-4671. https://doi.org/10.1128/AEM.00468-06

Ertbjerg P and Puolanne E, 2017. Muscle structure, sarcomere length and influences on meat quality: A review. Meat Science, 132, 139-152. https://doi.org/10.1016/j.meatsci.2017.04.261

Espunyes J, Cabezón O, Dias-Alves A, Miralles P, Ayats T and Cerdà-Cuéllar M, 2021. Assessing the role of livestock and sympatric wild ruminants in spreading antimicrobial resistant *Campylobacter* and *Salmonella* in alpine ecosystems. BMC Veterinary Research, 17, 79. https://doi.org/10.1186/s12917-021-02784-2

Herman L, Van Royen G, Van Damme I and De Zutter L, 2016. FAVV Studieproject SP 2015-02: Microbiologische risico's van 'dry aged meat'. Available online: https://www.favv-afsca.be/professionelen/publicaties/studieprojecten/_documents/FAVVStudieprojectSP2015-02_dryagedmeatsamenvatting_V4_NL.pdf

FSA (Food Standards Agency), 2017. The safety and shelf-life of vacuum and modified atmosphere packed chilled foods with respect to non-proteolytic *Clostridium botulinum*. Available online: https://www.food.gov.uk/sites/default/files/media/document/vacpacguide.pdf

FSA (Food Standards Agency). 2020a. The safety and shelf-life of vacuum and modified atmosphere packed chilled foods with respect to non-proteolytic *Clostridium botulinum*. Available online: https://www.food.gov.uk/sites/default/files/media/document/the-safety-and-shelf-life-of-vacuum-and-modified-atmosphere-packed-chilled-foods-with-respect-to-non-proteolytic-clostridium-botulinum_1.pdf
Clausen I, Jakobsen M, Ertbjerg P and Madsen NT, 2009. Modified atmosphere packaging affects lipid oxidation, myofibrillar fragmentation index and eating quality of beef. Packaging Technology and Science, 22, 85-96. https://doi.org/10.1002/pts.828

Constantino C, de Azambuja Ribeiro EL, Bridi AM, Tarsitano MA, Koritiaki NA, Peres LM, Mizubuti IY, Pereira ES and Pimentel PG, 2012. Quality of aged ewe meat in vacuum-packaging system for different storage periods. Semina: Ciencias Agrarias, 33, 3437-3446. https://doi.org/10.5433/1679-0359.2012v33Supl2p3437

Cornu M, Billoir E, Bergis H, Beaufort A and Zuliani V, 2011. Modeling microbial competition in food: Application to the behavior of *Listeria monocytogenes* and lactic acid flora in pork meat products. Food Microbiology, 28, 639-647. https://doi.org/10.1016/j.fm.2010.08.007

Coroller L, Kan-King-Yu D, Leguerinel I, Mafart P and Membré J-M, 2012. Modelling of growth, growth/no-growth interface and nonthermal inactivation areas of *Listeria* in foods. International Journal of Food Microbiology, 152, 139-152. https://doi.org/10.1016/j.ijfoodmicro.2011.09.023

Dalgaard P and Mejlholm O, 2019. In: Bridier A (ed). Modeling Growth of *Listeria* and Lactic Acid Bacteria in Food Environments. Springer. pp. 247-264.

Dashdorj D, Amna T and Hwang I, 2015. Influence of specific taste-active components on meat flavor as affected by intrinsic and extrinsic factors: an overview. European Food Research and Technology, 241, 157-171. https://doi.org/10.1007/s00217-015-2449-3

Dashdorj D, Tripathi VK, Cho S, Kim Y and Hwang I, 2016. Dry aging of beef; Review. Journal of Animal Science and Technology, 58, 20. https://doi.org/10.1186/s40781-016-0101-9

DeGeer SL, Hunt MC, Bratcher CL, Crozier-Dodson BA, Johnson DE and Stika JF, 2009. Effects of dry aging of bone-in and boneless strip loins using two aging processes for two aging times. Meat Science, 83, 768-774. https://doi.org/10.1016/j.meatsci.2009.08.017

Delais PJ and McCurdy AR, 1990. Colonization of beef muscle surfaces by *Pseudomonas fluorescens* and *Pseudomonas fragi*. Journal of Food Science, 55(4), 898-902. https://doi.org/10.1111/j.1365-2621.1990.tb01560.x

Devine C and Dikeman M, 2014. In: C Devine and M Dikeman (eds). Encyclopedia of Meat Sciences. 2nd edn. Academic Press. pp. 1712.

Devlieghere F and Debevere J, 2000. Influence of dissolved carbon dioxide on the growth of spoilage bacteria. LWT - Food Science and Technology, 33(8), 531-537. https://doi.org/10.1006/fstl.2000.0705

Dikeman ME, Obuz E, Gok V, Akkaya L and Stroda S, 2013. Effects of dry, vacuum, and special bag aging; USDA quality grade; and end-point temperature on yields and eating quality of beef Longissimus lumborum steaks. Meat Science, 94, 228-233. https://doi.org/10.1016/j.meatsci.2013.02.002

Doulgeraki AI, Ercolini D, Villani F and Nychas G-JE, 2012. Spoilage microbiota associated to the storage of raw meat in different conditions. International Journal of Food Microbiology, 157, 130-141. https://doi.org/10.1016/j.ijfoodmicro.2012.05.020

Duraković, S. and Duraković, L. (2003). Mikologija u biotehnologiji, Zagreb, Croatia: Kugler. pp. 80-94.

Dykes GA and Moorhead SM, 2002. Combined antimicrobial effect of nisin and a listeriophage against *Listeria monocytogenes* in broth but not in buffer or on raw beef. International Journal of Food Microbiology, 73, 71-81. https://doi.org/10.1016/S0168-1605(01)00710-3

EFSA (European Food Safety Authority) and ECDC (European Centre for Disease Prevention and Control), 2021. The European Union One Health 2020 Zoonoses Report. EFSA Journal 2021;19(12):6971, 324 pp. https://doi.org/10.2903/j.efsa.2021.6971

EFSA (European Food Safety Authority), Martino, L, Aiassa, E, Halldórsson, TI, Koutsoumanis, PK; Naegeli, H, Baert, K, Baldinelli, F, Devos, Y, Lodi, F, Lostia, A, Manini, P, Merten, C, Messens, W, Rizzi, V, Tarazona, J, Titz, A and Vos, S, 2020. Draft framework for protocol development for EFSA's scientific assessments. EFSA supporting publication 2020;17(4):EN-1843, 46 pp. https://doi.org/10.2903/sp.efsa.2020.EN-1843

Ercolini D, Russo F, Torrieri E, Masi P and Villani F, 2006. Changes in the spoilage-related microbiota of beef during refrigerated storage under different packaging conditions. Applied and Environmental Microbiology, 72, 4663-4671. https://doi.org/10.1128/AEM.00468-06

Ertbjerg P and Puolanne E, 2017. Muscle structure, sarcomere length and influences on meat quality: A review. Meat Science, 132, 139-152. https://doi.org/10.1016/j.meatsci.2017.04.261

Espunyes J, Cabezón O, Dias-Alves A, Miralles P, Ayats T and Cerdà-Cuéllar M, 2021. Assessing the role of livestock and sympatric wild ruminants in spreading antimicrobial resistant *Campylobacter* and *Salmonella* in alpine ecosystems. BMC Veterinary Research, 17, 79. https://doi.org/10.1186/s12917-021-02784-2

Herman L, Van Royen G, Van Damme I and De Zutter L, 2016. FAVV Studieproject SP 2015-02: Microbiologische risico's van 'dry aged meat'. Available online: https://www.favv-afsca.be/professionelen/publicaties/studieprojecten/_documents/FAVVStudieprojectSP2015-02_dryagedmeatsamenvatting_V4_NL.pdf

FSA (Food Standards Agency), 2017. The safety and shelf-life of vacuum and modified atmosphere packed chilled foods with respect to non-proteolytic *Clostridium botulinum*. Available online: https://www.food.gov.uk/sites/default/files/media/document/vacpacguide.pdf

FSA (Food Standards Agency). 2020a. The safety and shelf-life of vacuum and modified atmosphere packed chilled foods with respect to non-proteolytic *Clostridium botulinum*. Available online: https://www.food.gov.uk/sites/default/files/media/document/the-safety-and-shelf-life-of-vacuum-and-modified-atmosphere-packed-chilled-foods-with-respect-to-non-proteolytic-clostridium-botulinum_1.pdf
FSA (Food Standards Agency). 2020b. Subgroup on non-proteolytic *Clostridium botulinum* and vacuum and modified atmosphere packaged foods. Final Report. Available online https://acmsf.food.gov.uk/sites/default/files/acmsf-vpmap-subgroup-report_1.pdf

Gagaoua M, Terlouw EC, Mullen AM, Franco D, Warner RD, Lorenzo JM, Purslow PP, Gerrard D, Hopkins DL, Troy D and Picard B, 2021. Molecular signatures of beef tenderness: Underlying mechanisms based on integromics of protein biomarkers from multi-platform proteomics studies. Meat Science, 172, 108311.

Garner CM, Unruh FA, Hun MC, Boyle EAE and Houser TA, 2014. Effects of subprimal type, quality grade, and aging time on display color of ground beef patties. Meat Science, 98(2), 301–309. https://doi.org/10.1016/j.meatsci.2014.05.004

Gill CO and Lowry PD, 1982. Growth at sub-zero temperatures of black spot fungi from meat. Journal of Applied Bacteriology, 52, 245–250. https://doi.org/10.1111/j.1365-2672.1982.tb04846.x

Gill CO and Reichel MP, 1989. Growth of the cold-tolerant pathogens *Yersinia enterocolitica*, *Aeromonas hydrophila* and *Listeria monocytogenes* on high-pH beef packaged under vacuum or carbon dioxide. Food Microbiology, 6, 223–230. https://doi.org/10.1016/S0740-0020(89)80003-6

Gill CO, Lowry PD and Di Menna ME, 1981. A note on the identities of organisms causing black spot spoilage of meat. Journal of Applied Bacteriology, 51, 1–16. https://doi.org/10.1111/j.1365-2672.1981.tb00922.x

Giménez B and Dalgaard P, 2004. Modelling and predicting the simultaneous growth of *Listeria monocytogenes* and spoilage micro-organisms in cold-smoked salmon. Journal of Applied Microbiology, 96, 96–109. https://doi.org/10.1046/j.1365-2672.2003.02137.x

Giménez B, Graiver N, Giannuzzi L and Zaritzky N, 2021. Treatment of beef with gaseous ozone: Physicochemical aspects and antimicrobial effects on heterotrophic microflora and *Listeria monocytogenes*. Food Control, 121, 107602. https://doi.org/10.1016/j.foodcont.2020.107602

Gowda TKGM, De Zutter L, Van Royen G and Van Damme I, 2022. Exploring the microbiological quality and safety of dry-aged beef: a cross-sectional study of loin surfaces during ripening and dry-aged beef steaks from commercial meat companies in Belgium. Food Microbiology, 102, 103919. https://doi.org/10.1016/j.fm.2021.103919

Gudjonsdottir M, Gacutan MD, Mendes AC, Chronakis IS, Jespersen L and Karlsson AH, 2015. Effects of electrospun chitosan wrapping for dry-ageing of beef, as studied by microbiological, physicochemical and low-field nuclear magnetic resonance analysis. Food Chemistry, 184, 167–175. https://doi.org/10.1016/j.foodchem.2015.03.088

Gürtler M, Alter T, Kasimir S, Linnebur M and Fehlhaber K, 2005. Prevalence of *Yersinia enterocolitica* in fattening pigs. Journal of Food Protection, 68, 850–854. https://doi.org/10.4315/0362-028X-68.4.850

Ha Y, Hwang I, Van Ba H, Ryu S, Kim Y, Kang SM, Kim J, Kim Y and Cho S, 2019. Effects of dry- and wet-ageing on flavor compounds and eating quality of low fat hanwoo beef muscles. Food Science of Animal Resources, 39, 655–667. https://doi.org/10.5851/kosfa.2019.e58

Hamad GM, Mehany T, Simal-Gandara J, Abou-Alella S, Esua OJ, Abdel-Wahhab MA and Hafez EE, 2022. A review of recent innovative strategies for controlling mycotoxins in foods. Food Control, 144, 109350.

Hiroi M, Kawamori F, Harada T, Sano Y, Miwa N, Sugiyama K, Hara-Kudo Y and Masuda T, 2012. Antibiotic resistance in bacterial pathogens from retail raw meats and food-producing animals in Japan. Journal of Food Protection, 75, 1774–1782. https://doi.org/10.4315/0362-028X.JFP-11-479

Hocking AD and Pitt JJ, 2003. Mycotoxigenic fungi. Foodborne microorganisms of public health significance. 6th edn. pp. 641–673 ref.330.

Holmer SF, McKeith RO, Boler DD, Dilger AC, Eggert JM, Petry DB, McKeith FK, Jones KL and Killefer J, 2009. The effect of pH on shelf-life of pork during aging and simulated retail display. Meat Science, 82, 86–93. https://doi.org/10.1016/j.meatsci.2008.12.008

Hopkins DL and Thompson JM, 2002. Factors contributing to proteolysis and disruption of myofibrillar proteins and the impact on tenderisation in beef and sheep meat. Australian Journal of Agricultural Research, 53, 149–166.

Hulankova R, Borilova G, Abdullah FAA and Buchtova H, 2018a. Microbiological quality of organic chicken meat during refrigerated storage in air and modified atmospheres. British Poultry Science, 59, 506–513. https://doi.org/10.1080/00071668.2018.1496399

Hulankova R, Kamenik J, Salakova A, Zavodsky D and Borilova G, 2018b. The effect of dry aging on instrumental, chemical and microbiological parameters of organic beef loin muscle. LWT- Food Science and Technology, 89, 559–565. https://doi.org/10.1016/j.lwt.2017.11.014

Hungaro H, Caturla MYR, Horita CN, Miranda M and Sant'Ana AS, 2016. Blown pack spoilage in vacuum-packaged meat: a review on clostridia as causative agents, sources, detection methods, contributing factors and mitigation strategies. Trends in Food Science & Technology, 52(1), 123–138. https://doi.org/10.1016/j.tifs.2016.04.010

Iacumin L, Chiesa L, Boscolo D, Manzano M, Cantoni C, Orlic S and Comi G, 2009. Moulds and ochratoxin A on surfaces of artisanal and industrial dry sausages. Food Microbiology, 26, 65–70. https://doi.org/10.1016/j.fm.2008.07.006

IARC, 2002. iarc monographs on the evaluation of carcinogenic risks to humans. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, 2002(82), 1–556.

IARC, 2012. IARC monographs on the evaluation of carcinogenic risks to humans. Chemical Agents and Related Occupations, 100F, 1–628.
FSA (Food Standards Agency). 2020b. Subgroup on non-proteolytic *Clostridium botulinum* and vacuum and modified atmosphere packaged foods. Final Report. Available online https://acmsf.food.gov.uk/sites/default/files/acmsf-vpmap-subgroup-report_1.pdf

Gagaoua M, Terlouw EC, Mullen AM, Franco D, Warner RD, Lorenzo JM, Purslow PP, Gerrard D, Hopkins DL, Troy D and Picard B, 2021. Molecular signatures of beef tenderness: Underlying mechanisms based on integromics of protein biomarkers from multi-platform proteomics studies. Meat Science, 172, 108311.

Garner CM, Unruh FA, Hun MC, Boyle EAE and Houser TA, 2014. Effects of subprimal type, quality grade, and aging time on display color of ground beef patties. Meat Science, 98(2), 301–309. https://doi.org/10.1016/j.meatsci.2014.05.004

Gill CO and Lowry PD, 1982. Growth at sub-zero temperatures of black spot fungi from meat. Journal of Applied Bacteriology, 52, 245–250. https://doi.org/10.1111/j.1365-2672.1982.tb04846.x

Gill CO and Reichel MP, 1989. Growth of the cold-tolerant pathogens *Yersinia enterocolitica*, *Aeromonas hydrophila* and *Listeria monocytogenes* on high-pH beef packaged under vacuum or carbon dioxide. Food Microbiology, 6, 223–230. https://doi.org/10.1016/S0740-0020(89)80003-6

Gill CO, Lowry PD and Di Menna ME, 1981. A note on the identities of organisms causing black spot spoilage of meat. Journal of Applied Bacteriology, 51, 1–16. https://doi.org/10.1111/j.1365-2672.1981.tb00922.x

Giménez B and Dalgaard P, 2004. Modelling and predicting the simultaneous growth of *Listeria monocytogenes* and spoilage micro-organisms in cold-smoked salmon. Journal of Applied Microbiology, 96, 96–109. https://doi.org/10.1046/j.1365-2672.2003.02137.x

Giménez B, Graiver N, Giannuzzi L and Zaritzky N, 2021. Treatment of beef with gaseous ozone: Physicochemical aspects and antimicrobial effects on heterotrophic microflora and *Listeria monocytogenes*. Food Control, 121, 107602. https://doi.org/10.1016/j.foodcont.2020.107602

Gowda TKGM, De Zutter L, Van Royen G and Van Damme I, 2022. Exploring the microbiological quality and safety of dry-aged beef: a cross-sectional study of loin surfaces during ripening and dry-aged beef steaks from commercial meat companies in Belgium. Food Microbiology, 102, 103919. https://doi.org/10.1016/j.fm.2021.103919

Gudjonsdottir M, Gacutan MD, Mendes AC, Chronakis IS, Jespersen L and Karlsson AH, 2015. Effects of electrospun chitosan wrapping for dry-ageing of beef, as studied by microbiological, physicochemical and low-field nuclear magnetic resonance analysis. Food Chemistry, 184, 167–175. https://doi.org/10.1016/j.foodchem.2015.03.088

Gürtler M, Alter T, Kasimir S, Linnebur M and Fehlhaber K, 2005. Prevalence of *Yersinia enterocolitica* in fattening pigs. Journal of Food Protection, 68, 850–854. https://doi.org/10.4315/0362-028X-68.4.850

Ha Y, Hwang I, Van Ba H, Ryu S, Kim Y, Kang SM, Kim J, Kim Y and Cho S, 2019. Effects of dry- and wet-ageing on flavor compounds and eating quality of low fat hanwoo beef muscles. Food Science of Animal Resources, 39, 655–667. https://doi.org/10.5851/kosfa.2019.e58

Hamad GM, Mehany T, Simal-Gandara J, Abou-Alella S, Esua OJ, Abdel-Wahhab MA and Hafez EE, 2022. A review of recent innovative strategies for controlling mycotoxins in foods. Food Control, 144, 109350.

Hiroi M, Kawamori F, Harada T, Sano Y, Miwa N, Sugiyama K, Hara-Kudo Y and Masuda T, 2012. Antibiotic resistance in bacterial pathogens from retail raw meats and food-producing animals in Japan. Journal of Food Protection, 75, 1774–1782. https://doi.org/10.4315/0362-028X.JFP-11-479

Hocking AD and Pitt JJ, 2003. Mycotoxigenic fungi. Foodborne microorganisms of public health significance. 6th edn. pp. 641–673 ref.330.

Holmer SF, McKeith RO, Boler DD, Dilger AC, Eggert JM, Petry DB, McKeith FK, Jones KL and Killefer J, 2009. The effect of pH on shelf-life of pork during aging and simulated retail display. Meat Science, 82, 86–93. https://doi.org/10.1016/j.meatsci.2008.12.008

Hopkins DL and Thompson JM, 2002. Factors contributing to proteolysis and disruption of myofibrillar proteins and the impact on tenderisation in beef and sheep meat. Australian Journal of Agricultural Research, 53, 149–166.

Hulankova R, Borilova G, Abdullah FAA and Buchtova H, 2018a. Microbiological quality of organic chicken meat during refrigerated storage in air and modified atmospheres. British Poultry Science, 59, 506–513. https://doi.org/10.1080/00071668.2018.1496399

Hulankova R, Kamenik J, Salakova A, Zavodsky D and Borilova G, 2018b. The effect of dry aging on instrumental, chemical and microbiological parameters of organic beef loin muscle. LWT- Food Science and Technology, 89, 559–565. https://doi.org/10.1016/j.lwt.2017.11.014

Hungaro H, Caturla MYR, Horita CN, Miranda M and Sant'Ana AS, 2016. Blown pack spoilage in vacuum-packaged meat: a review on clostridia as causative agents, sources, detection methods, contributing factors and mitigation strategies. Trends in Food Science & Technology, 52(1), 123–138. https://doi.org/10.1016/j.tifs.2016.04.010

Iacumin L, Chiesa L, Boscolo D, Manzano M, Cantoni C, Orlic S and Comi G, 2009. Moulds and ochratoxin A on surfaces of artisanal and industrial dry sausages. Food Microbiology, 26, 65–70. https://doi.org/10.1016/j.fm.2008.07.006

IARC, 2002. iarc monographs on the evaluation of carcinogenic risks to humans. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, 2002(82), 1–556.

IARC, 2012. IARC monographs on the evaluation of carcinogenic risks to humans. Chemical Agents and Related Occupations, 100F, 1–628.
ICMSF, 1996. Microorganisms in Foods 5: Microbiological Specifications of Food Pathogens. Chapman & Hall, London.

Iida F, Miyazaki Y, Tsuyuki R, Kato K, Egusa A, Ogoshi H and Nishimura T, 2016. Changes in taste compounds, breaking properties, and sensory attributes during dry aging of beef from Japanese black cattle. Meat Science, 112, 46–51. https://doi.org/10.1016/j.meatsci.2015.10.015

Ingham SC, Algino RJ, Ingham BH and Schell RF, 2010. Identification of *Escherichia coli* O157:H7 surrogate organisms to evaluate beef carcass intervention treatment efficacy. Journal of Food Protection, 73, 1864–1874. https://doi.org/10.4315/0362-028x-73.10.1864

Ismaiel AA and Papenbrock J, 2015. Mycotoxins: producing fungi and mechanisms of phytotoxicity. Agriculture, 5, 492–537. https://doi.org/10.3390/agriculture5030492

James C and James S, 2012. Quantification of the controls that should be placed on meat prior to mincing. FSA (Food Standards Agency) Project: M01054.

James C, Vincent C, Lima TIA and James SJ, 2006. The primary chilling of poultry carcasses – a review. International Journal of Refrigeration-Revue Internationale Du Froid, 29, 847–862.

Jameson J, 1962. A discussion of the dynamics of *Salmonella* enrichment. Journal of Hygiene, 60, 193–207.

Jiang T, Busboom JR, Nelson ML, O'Fallon J, Ringkob TP, Rogers-Klette KR, Joos D and Piper K, 2010. The influence of forage diets and aging on beef palatability. Meat Science, 86, 642–650. https://doi.org/10.1016/j.meatsci.2010.05.016

Joint Fao/Who Codex Alimentarius Commission F and Agriculture Organization of the United Nations WHO, 2001. Codex alimentarius. Rome, Food and Agriculture Organization of the United Nations: World Health Organization.

Kahraman H and Gurbuz U, 2019. Effects of three aging methods on the *Longissimus lumborum* muscle from Holstein-Friesian steers. Medycyna Weterynaryjna, 75, 6182–2019. https://doi.org/10.21521/mw.6182

Kang SM, Chung SG, Seong P-N, Kim Y, Kim Y, Ba HV, Kim J-H and Cho S, 2017. Effect of dry-ageing on sensory properties of loin and tritip muscle from Hanwoo Beef. Proceedings of the 63rd International Congress of Meat Science and Technology.

Khan M, Jung S, Nam K-C and Jo C, 2016. Postmortem aging of beef with a special reference to the dry aging. Korean Journal for Food Science of Animal Resources, 36, 159–169. https://doi.org/10.5851/kosfa.2016.36.2.159

Kim YHB, Kemp R and Samuelsson LM, 2016. Effects of dry-aging on meat quality attributes and metabolite profiles of beef loins. Meat Science, 111, 168–176. https://doi.org/10.1016/j.meatsci.2015.09.008

Kim J-H, Kim D-H, Ji D-s, Lee H-J, Yoon D-K and Lee C-H, 2017a. Effect of aging process and time on physicochemical and sensory evaluation of raw beef top round and shank muscles using an electronic tongue. Korean Journal for Food Science of Animal Resources, 37, 823–832. https://doi.org/10.5851/kosfa.2017.37.6.823

Kim YHB, Meyers B, Kim H-W, Liceaga AM and Lemenager RP, 2017b. Effects of stepwise dry/wet-aging and freezing on meat quality of beef loins. Meat Science, 123, 57–63. https://doi.org/10.1016/j.meatsci.2016.09.002

Kim YHB, Ma DY, Setyabrata D, Farouk MM, Lonerg SM, Huff-Lonergan E and Hunt MC, 2018. Understanding postmortem biochemical processes and post-harvest aging factors to develop novel smart-aging strategies. Meat Science, 144, 74–90. https://doi.org/10.1016/j.meatsci.2018.04.031

Kim M, Choe J, Lee HJ, Yoon Y, Yoon S and Jo C, 2019a. Effects of aging and aging method on physicochemical and sensory traits of different beef cuts. Food Science of Animal Resources, 39, 54–64. https://doi.org/10.5851/kosfa.2019.e3

Kim S, Lee HJ, Kim M, Yoon JW, Shin DJ and Jo C, 2019b. Storage stability of vacuum-packaged dry-aged beef during refrigeration at 4 degrees C. Food Science of Animal Resources, 39, 266–275. https://doi.org/10.5851/kosfa.2019.e21

Kim JH, Kim TK, Shin DM, Kim HW, Kim YB and Choi YS, 2020. Comparative effects of dry-aging and wet-aging on physicochemical properties and digestibility of Hanwoo beef. Asian-Australasian Journal of Animal Sciences, 33, 501–505.

King M-F, Matthews MA, Rule DC and Field RA, 1995. Effect of beef packaging method on volatile compounds developed by oven roasting or microwave cooking. Journal of Agricultural and Food Chemistry, 43, 773–778. https://doi.org/10.1021/jf00051a039

Knudsen GM, Sommer HM, Sorensen ND, Olsen JE and Aabo S, 2011. Survival of Salmonella on cuts of beef carcasses subjected to dry aging. Journal of Applied Microbiology, 111, 848–854. https://doi.org/10.1111/j.1365-2672.2011.05094.x

Koohmaraie M and Geesink GH, 2006. Contribution of postmortem muscle biochemistry to the delivery of consistent meat quality with particular focus on the calpain system. Meat Science, 74, 34–43. https://doi.org/10.1016/j.meatsci.2006.04.025

Koukou I, Mejlholm O and Dalgaard P, 2021. Cardinal parameter growth and growth boundary model for non-proteolytic *Clostridium botulinum* – effect of eight environmental factors. International Journal of Food Microbiology, 346, 109162. https://doi.org/10.1016/j.ijfoodmicro.2021.109162

Koutsoumanis K, Stamatiou A, Skandamis P and Nychas G-JE, 2006. Development of a microbial model for the combined effect of temperature and pH on spoilage of ground meat, and validation of the model under dynamic temperature conditions. Applied and Environmental Microbiology, 72, 124–134. https://doi.org/10.1128/AEM.72.1.124-134.2006
ICMSF, 1996. Microorganisms in Foods 5: Microbiological Specifications of Food Pathogens. Chapman & Hall, London.

Iida F, Miyazaki Y, Tsuyuki R, Kato K, Egusa A, Ogoshi H and Nishimura T, 2016. Changes in taste compounds, breaking properties, and sensory attributes during dry aging of beef from Japanese black cattle. Meat Science, 112, 46–51. https://doi.org/10.1016/j.meatsci.2015.10.015

Ingham SC, Algino RJ, Ingham BH and Schell RF, 2010. Identification of *Escherichia coli* O157:H7 surrogate organisms to evaluate beef carcass intervention treatment efficacy. Journal of Food Protection, 73, 1864–1874. https://doi.org/10.4315/0362-028x-73.10.1864

Ismaiel AA and Papenbrock J, 2015. Mycotoxins: producing fungi and mechanisms of phytotoxicity. Agriculture, 5, 492–537. https://doi.org/10.3390/agriculture5030492

James C and James S, 2012. Quantification of the controls that should be placed on meat prior to mincing. FSA (Food Standards Agency) Project: M01054.

James C, Vincent C, Lima TIA and James SJ, 2006. The primary chilling of poultry carcasses – a review. International Journal of Refrigeration-Revue Internationale Du Froid, 29, 847–862.

Jameson J, 1962. A discussion of the dynamics of *Salmonella* enrichment. Journal of Hygiene, 60, 193–207.

Jiang T, Busboom JR, Nelson ML, O'Fallon J, Ringkob TP, Rogers-Klette KR, Joos D and Piper K, 2010. The influence of forage diets and aging on beef palatability. Meat Science, 86, 642–650. https://doi.org/10.1016/j.meatsci.2010.05.016

Joint Fao/Who Codex Alimentarius Commission F and Agriculture Organization of the United Nations WHO, 2001. Codex alimentarius. Rome, Food and Agriculture Organization of the United Nations: World Health Organization.

Kahraman H and Gurbuz U, 2019. Effects of three aging methods on the *Longissimus lumborum* muscle from Holstein-Friesian steers. Medycyna Weterynaryjna, 75, 6182–2019. https://doi.org/10.21521/mw.6182

Kang SM, Chung SG, Seong P-N, Kim Y, Kim Y, Ba HV, Kim J-H and Cho S, 2017. Effect of dry-ageing on sensory properties of loin and tritip muscle from Hanwoo Beef. Proceedings of the 63rd International Congress of Meat Science and Technology.

Khan M, Jung S, Nam K-C and Jo C, 2016. Postmortem aging of beef with a special reference to the dry aging. Korean Journal for Food Science of Animal Resources, 36, 159–169. https://doi.org/10.5851/kosfa.2016.36.2.159

Kim YHB, Kemp R and Samuelsson LM, 2016. Effects of dry-aging on meat quality attributes and metabolite profiles of beef loins. Meat Science, 111, 168–176. https://doi.org/10.1016/j.meatsci.2015.09.008

Kim J-H, Kim D-H, Ji D-s, Lee H-J, Yoon D-K and Lee C-H, 2017a. Effect of aging process and time on physicochemical and sensory evaluation of raw beef top round and shank muscles using an electronic tongue. Korean Journal for Food Science of Animal Resources, 37, 823–832. https://doi.org/10.5851/kosfa.2017.37.6.823

Kim YHB, Meyers B, Kim H-W, Liceaga AM and Lemenager RP, 2017b. Effects of stepwise dry/wet-aging and freezing on meat quality of beef loins. Meat Science, 123, 57–63. https://doi.org/10.1016/j.meatsci.2016.09.002

Kim YHB, Ma DY, Setyabrata D, Farouk MM, Lonerg SM, Huff-Lonergan E and Hunt MC, 2018. Understanding postmortem biochemical processes and post-harvest aging factors to develop novel smart-aging strategies. Meat Science, 144, 74–90. https://doi.org/10.1016/j.meatsci.2018.04.031

Kim M, Choe J, Lee HJ, Yoon Y, Yoon S and Jo C, 2019a. Effects of aging and aging method on physicochemical and sensory traits of different beef cuts. Food Science of Animal Resources, 39, 54–64. https://doi.org/10.5851/kosfa.2019.e3

Kim S, Lee HJ, Kim M, Yoon JW, Shin DJ and Jo C, 2019b. Storage stability of vacuum-packaged dry-aged beef during refrigeration at 4 degrees C. Food Science of Animal Resources, 39, 266–275. https://doi.org/10.5851/kosfa.2019.e21

Kim JH, Kim TK, Shin DM, Kim HW, Kim YB and Choi YS, 2020. Comparative effects of dry-aging and wet-aging on physicochemical properties and digestibility of Hanwoo beef. Asian-Australasian Journal of Animal Sciences, 33, 501–505.

King M-F, Matthews MA, Rule DC and Field RA, 1995. Effect of beef packaging method on volatile compounds developed by oven roasting or microwave cooking. Journal of Agricultural and Food Chemistry, 43, 773–778. https://doi.org/10.1021/jf00051a039

Knudsen GM, Sommer HM, Sorensen ND, Olsen JE and Aabo S, 2011. Survival of Salmonella on cuts of beef carcasses subjected to dry aging. Journal of Applied Microbiology, 111, 848–854. https://doi.org/10.1111/j.1365-2672.2011.05094.x

Koohmaraie M and Geesink GH, 2006. Contribution of postmortem muscle biochemistry to the delivery of consistent meat quality with particular focus on the calpain system. Meat Science, 74, 34–43. https://doi.org/10.1016/j.meatsci.2006.04.025

Koukou I, Mejlholm O and Dalgaard P, 2021. Cardinal parameter growth and growth boundary model for non-proteolytic *Clostridium botulinum* – effect of eight environmental factors. International Journal of Food Microbiology, 346, 109162. https://doi.org/10.1016/j.ijfoodmicro.2021.109162

Koutsoumanis K, Stamatiou A, Skandamis P and Nychas G-JE, 2006. Development of a microbial model for the combined effect of temperature and pH on spoilage of ground meat, and validation of the model under dynamic temperature conditions. Applied and Environmental Microbiology, 72, 124–134. https://doi.org/10.1128/AEM.72.1.124-134.2006
Kristensen L and Purslow PP, 2001. The effect of ageing on the water-holding capacity of pork: role of cytoskeletal proteins. Meat Science, 58, 17-23.

Krupa P, Bystrón J, Bania J, Podkowik M, Empel J and Mroczkowska A, 2014. Genotypes and oxacillin resistance of *Staphylococcus aureus* from chicken and chicken meat in Poland. Poultry Science, 93, 3179-3186. https://doi.org/10.3382/ps.2014-04321

Kurtzman CP and Ciegler A, 1970. Mycotoxin from a blue eye mold of corn. Applied Microbiology, 20, 204-207.

Lasta JA, Pensel N, Masana M, Rodriguez HR and Garcia PT, 1995. Microbial growth and biochemical changes on naturally contaminated chilled-beef subcutaneous adipose tissue stored aerobically. Meat Science, 39, 149-158.

Laster MA, Smith RD, Nicholson KL, Nicholson JDW, Miller RK, Griffin DB, Harris KB and Savell JW, 2008. Dry versus wet aging of beef: retail cutting yields and consumer sensory attribute evaluations of steaks from ribeyes, strip loins, and top sirloins from two quality grade groups. Meat Science, 80, 795-804. https://doi.org/10.1016/j.meatsci.2008.03.024

Latorre ME, Palacio MI, Velázquez DE and Purslow PP, 2019. Specific effects on strength and heat stability of intramuscular connective tissue during long time low temperature cooking. Meat Science, 153, 109-116. https://doi.org/10.1016/j.meatsci.2019.03.016

Lautenschlaeger R, 2012. Latest trends in beef maturation - dry-aged versus wet-aged beef. Proceedings of the 58th International Congress of Meat Science and Technology, Montreal, Canada 12-17th August 2012s.

Lee MS, Apple JK, Yancey JWS, Sawyer JT and Johnson ZB, 2008. Influence of vacuum-aging period on bloom development of the beef gluteus medius from top sirloin butts. Meat Science, 80, 592-598. https://doi.org/10.1016/j.meatsci.2008.02.006

Lee YJ, Jung BS, Yoon HJ, Kim K-T, Paik H-D and Lee J-Y, 2014. Predictive model for the growth kinetics of Listeria monocytogenes in raw pork meat as a function of temperature. Food Control, 44, 16-21. https://doi.org/10.1016/j.foodcont.2014.03.024

Lee HJ, Choe J, Kim KT, Oh J, Lee DG, Kwon KM, Choi YI and Jo C, 2017. Analysis of low-marbled Hanwoo cow meat aged with different dry-aging methods. Asian-Australasian Journal of Animal Sciences, 30, 1733-1738. https://doi.org/10.5713/ajas.17.0318

Lee HJ, Choe J, Yoon LW, Oh H, Yoon Y and Jo C, 2018. Determination of Salable Shelf-life for Wrap-packaged Dry-aged Beef during Cold Storage. Korean Journal for Food Science of Animal, 38, 251-258. https://doi.org/10.5851/kosfa.2018.38.2.251

Lee HJ, Yoon JW, Kim M, Oh H, Yoon Y and Jo C, 2019. Changes in microbial composition on the crust by different air flow velocities and their effect on sensory properties of dry-aged beef. Meat Science, 153, 152-158. https://doi.org/10.1016/j.meatsci.2019.03.019

Lee D, Lee HJ, Yoon JW, Kim M and Jo C, 2021. Effect of different aging methods on the formation of aroma volatiles in beef strip loins. Food, 10. https://doi.org/10.3390/foods10010146

Lepper-Blilie AN, Berg EP, Buchanan DS and Berg PT, 2016. Effects of post-mortem aging time and type of aging on palatability of low marbled beef loins. Meat Science, 112, 63-68. https://doi.org/10.1016/j.meatsci.2015.10.017

Leroi F, Cornet J, Chevalier F, Cardinal M, Coeuret G, Chaillou S and Joffraud J-J, 2015. Selection of bioprotective cultures for preventing cold-smoked salmon spoilage. International Journal of Food Microbiology, 213, 79-87. https://doi.org/10.1016/j.ijfoodmicro.2015.05.005

Lewicki PP, 2004. Water as the determinant of food engineering properties. A review. Journal of Food Engineering, 61, 483-495. https://doi.org/10.1016/S0260-8774(03)00219-X

Li X, Babol J, Wallby A and Lundstrom K, 2013. Meat quality, microbiological status and consumer preference of beef gluteus medius aged in a dry-ageing bag or vacuum. Meat Science, 95, 229-234. https://doi.org/10.1016/j.meatsci.2013.05.009

Li X, Babol J, Bredie WLP, Nielsen B, Tomankova J and Lundstrom K, 2014. A comparative study of beef quality after ageing longissimus muscle using a dry-ageing bag, traditional dry-ageing or vacuum package ageing. Meat Science, 97, 433-442. https://doi.org/10.1016/j.meatsci.2014.03.014

Lindberg CW and Borch E, 1994. Predicting the aerobic growth of *Y. enterocolitica* O:3 at different pH-values, temperatures and L-lactate concentrations using conductance measurements. International Journal of Food Microbiology, 22, 141-153.

Little CL, Adams MR, Anderson WA and Cole MB, 1992. Comparison of a quadratic response surface model and a square root model for predicting the growth rate of *Yersinia enterocolitica*. Letters in Applied Microbiology, 15, 63-68. https://doi.org/10.1111/j.1472-765X.1992.tb00726.x

Lively FO, Keady TWJ, Moss BW, Farmer LG, Gault NFS, Tolland ELC, Patterson DCP and Gordon AG, 2006. The effect of beef genotype, pelvic hanging technique and aging period on the eating quality of some hindquarter muscles. Proceedings of the British Society of Animal Science, 2006, 20. https://doi.org/10.1017/S1752756200016975

Lonergan EH, Zhang W and Lonergan SM, 2010. Biochemistry of postmortem muscle - lessons on mechanisms of meat tenderization. Meat Science, 86, 184-195. https://doi.org/10.1016/j.meatsci.2010.05.004

López-Gómez A, Castaño-Villar AM, Palop A and Marín-Iniesta F, 2013. Hygienic design and microbial control of refrigeration and air conditioning systems for food processing and packaging plants. Food Engineering Reviews, 5, 18-35. https://doi.org/10.1007/s12393-012-9060-1
Kristensen L and Purslow PP, 2001. The effect of ageing on the water-holding capacity of pork: role of cytoskeletal proteins. Meat Science, 58, 17-23.

Krupa P, Bystrón J, Bania J, Podkowik M, Empel J and Mroczkowska A, 2014. Genotypes and oxacillin resistance of *Staphylococcus aureus* from chicken and chicken meat in Poland. Poultry Science, 93, 3179-3186. https://doi.org/10.3382/ps.2014-04321

Kurtzman CP and Ciegler A, 1970. Mycotoxin from a blue eye mold of corn. Applied Microbiology, 20, 204-207.

Lasta JA, Pensel N, Masana M, Rodriguez HR and Garcia PT, 1995. Microbial growth and biochemical changes on naturally contaminated chilled-beef subcutaneous adipose tissue stored aerobically. Meat Science, 39, 149-158.

Laster MA, Smith RD, Nicholson KL, Nicholson JDW, Miller RK, Griffin DB, Harris KB and Savell JW, 2008. Dry versus wet aging of beef: retail cutting yields and consumer sensory attribute evaluations of steaks from ribeyes, strip loins, and top sirloins from two quality grade groups. Meat Science, 80, 795-804. https://doi.org/10.1016/j.meatsci.2008.03.024

Latorre ME, Palacio MI, Velázquez DE and Purslow PP, 2019. Specific effects on strength and heat stability of intramuscular connective tissue during long time low temperature cooking. Meat Science, 153, 109-116. https://doi.org/10.1016/j.meatsci.2019.03.016

Lautenschlaeger R, 2012. Latest trends in beef maturation - dry-aged versus wet-aged beef. Proceedings of the 58th International Congress of Meat Science and Technology, Montreal, Canada 12-17th August 2012s.

Lee MS, Apple JK, Yancey JWS, Sawyer JT and Johnson ZB, 2008. Influence of vacuum-aging period on bloom development of the beef gluteus medius from top sirloin butts. Meat Science, 80, 592-598. https://doi.org/10.1016/j.meatsci.2008.02.006

Lee YJ, Jung BS, Yoon HJ, Kim K-T, Paik H-D and Lee J-Y, 2014. Predictive model for the growth kinetics of Listeria monocytogenes in raw pork meat as a function of temperature. Food Control, 44, 16-21. https://doi.org/10.1016/j.foodcont.2014.03.024

Lee HJ, Choe J, Kim KT, Oh J, Lee DG, Kwon KM, Choi YI and Jo C, 2017. Analysis of low-marbled Hanwoo cow meat aged with different dry-aging methods. Asian-Australasian Journal of Animal Sciences, 30, 1733-1738. https://doi.org/10.5713/ajas.17.0318

Lee HJ, Choe J, Yoon LW, Oh H, Yoon Y and Jo C, 2018. Determination of Salable Shelf-life for Wrap-packaged Dry-aged Beef during Cold Storage. Korean Journal for Food Science of Animal, 38, 251-258. https://doi.org/10.5851/kosfa.2018.38.2.251

Lee HJ, Yoon JW, Kim M, Oh H, Yoon Y and Jo C, 2019. Changes in microbial composition on the crust by different air flow velocities and their effect on sensory properties of dry-aged beef. Meat Science, 153, 152-158. https://doi.org/10.1016/j.meatsci.2019.03.019

Lee D, Lee HJ, Yoon JW, Kim M and Jo C, 2021. Effect of different aging methods on the formation of aroma volatiles in beef strip loins. Food, 10. https://doi.org/10.3390/foods10010146

Lepper-Blilie AN, Berg EP, Buchanan DS and Berg PT, 2016. Effects of post-mortem aging time and type of aging on palatability of low marbled beef loins. Meat Science, 112, 63-68. https://doi.org/10.1016/j.meatsci.2015.10.017

Leroi F, Cornet J, Chevalier F, Cardinal M, Coeuret G, Chaillou S and Joffraud J-J, 2015. Selection of bioprotective cultures for preventing cold-smoked salmon spoilage. International Journal of Food Microbiology, 213, 79-87. https://doi.org/10.1016/j.ijfoodmicro.2015.05.005

Lewicki PP, 2004. Water as the determinant of food engineering properties. A review. Journal of Food Engineering, 61, 483-495. https://doi.org/10.1016/S0260-8774(03)00219-X

Li X, Babol J, Wallby A and Lundstrom K, 2013. Meat quality, microbiological status and consumer preference of beef gluteus medius aged in a dry-ageing bag or vacuum. Meat Science, 95, 229-234. https://doi.org/10.1016/j.meatsci.2013.05.009

Li X, Babol J, Bredie WLP, Nielsen B, Tomankova J and Lundstrom K, 2014. A comparative study of beef quality after ageing longissimus muscle using a dry-ageing bag, traditional dry-ageing or vacuum package ageing. Meat Science, 97, 433-442. https://doi.org/10.1016/j.meatsci.2014.03.014

Lindberg CW and Borch E, 1994. Predicting the aerobic growth of *Y. enterocolitica* O:3 at different pH-values, temperatures and L-lactate concentrations using conductance measurements. International Journal of Food Microbiology, 22, 141-153.

Little CL, Adams MR, Anderson WA and Cole MB, 1992. Comparison of a quadratic response surface model and a square root model for predicting the growth rate of *Yersinia enterocolitica*. Letters in Applied Microbiology, 15, 63-68. https://doi.org/10.1111/j.1472-765X.1992.tb00726.x

Lively FO, Keady TWJ, Moss BW, Farmer LG, Gault NFS, Tolland ELC, Patterson DCP and Gordon AG, 2006. The effect of beef genotype, pelvic hanging technique and aging period on the eating quality of some hindquarter muscles. Proceedings of the British Society of Animal Science, 2006, 20. https://doi.org/10.1017/S1752756200016975

Lonergan EH, Zhang W and Lonergan SM, 2010. Biochemistry of postmortem muscle - lessons on mechanisms of meat tenderization. Meat Science, 86, 184-195. https://doi.org/10.1016/j.meatsci.2010.05.004

López-Gómez A, Castaño-Villar AM, Palop A and Marín-Iniesta F, 2013. Hygienic design and microbial control of refrigeration and air conditioning systems for food processing and packaging plants. Food Engineering Reviews, 5, 18-35. https://doi.org/10.1007/s12393-012-9060-1
Lowry PD and Gill CO, 1984. Temperature and water activity minima for growth of spoilage moulds from meat. Journal of Applied Bacteriology, 56, 193-199. https://doi.org/10.1111/j.1365-2672.1984.tb01339.x

Lynt RK, Kautter DA and Solomon HM, 1982. Heat resistance of proteolytic *Clostridium botulinum* type F in phosphate buffer and crabmeat. Journal of Food Science, 47, 204-206. https://doi.org/10.1111/j.1365-2621.1982.tb11059.x

Mano SB, De Fernando GDG, Lopez-Galvez D, Selgas MD, Garcia ML, Cambero MI and OrdoÑez JA, 1995. Growth/survival of natural flora and *Listeria monocytogenes* on refrigerated uncooked pork and turkey packaged under modified atmospheres. Journal of Food Safety, 15, 305-319. https://doi.org/10.1111/j.1745-4565.1995.tb00142.x

Marcos Lázaro P, Whyte P, Rogers T, McElroy M, Fanning S, Frías J and Bolton D, 2021. The prevalence of *Clostridioides difficile* on farms, in abattoirs and in retail foods in Ireland. Food Microbiology, 98, 103781. https://doi.org/10.1016/j.fm.2021.103781

Martínez PO, Fredriksson-Ahomaa M, Pallotti A, Rosmini R, Houf K and Korkeala H, 2011. Variation in the prevalence of enteropathogenic *Yersinia* in slaughter pigs from Belgium, Italy, and Spain. Foodborne Pathogens and Disease, 8, 445-450. https://doi.org/10.1089/fpd.2009.0461

Martins SIFS, Jongen WMF and van Boekel MAJS, 2000. A review of Maillard reaction in food and implications to kinetic modelling. Trends in Food Science & Technology, 11, 364-373. https://doi.org/10.1016/S0924-2244(01)00022-X

McKenna DR, Strachan DS, Miller RK, Acuff GR and Savell JW, 2003. Cranberry juice marinade improves sensory and microbiological properties of vacuum-packaged lamb chops. Journal of Muscle Foods, 14, 207-220. https://doi.org/10.1111/j.1745-4573.2003.tb00701.x

McSharry S, Koolman L, Whyte P and Bolton D, 2021. The microbiology of beef from carcass chilling through primal storage to retail steaks. Current Research in Food Science, 4, 150-162. https://doi.org/10.1016/j.crfs.2021.03.002

Mejlholm OLE and Dalgaard PAW, 2007. Modeling and predicting the growth of lactic acid bacteria in lightly preserved seafood and their inhibiting effect on *Listeria monocytogenes*. Journal of Food Protection, 70, 2485-2497. https://doi.org/10.4315/0362-028X-70.11.2485

Mejlholm OLE and Dalgaard PAW, 2009. Development and validation of an extensive growth and growth boundary model for *Listeria monocytogenes* in lightly preserved and ready-to-eat shrimp. Journal of Food Protection, 72, 2132-2143. https://doi.org/10.4315/0362-028X-72.10.2132

Mejlholm O and Dalgaard P, 2013. Development and validation of an extensive growth and growth boundary model for psychrotolerant *Lactobacillus* spp. in seafood and meat products. International Journal of Food Microbiology, 167, 244-260. https://doi.org/10.1016/j.ijfoodmicro.2013.09.013

Mejlholm O, Gunvig A, Borggaard C, Blom-Hanssen J, Mellefont L, Ross T, Leroi F, Else T, Visser D and Dalgaard P, 2010. Predicting growth rates and growth boundary of *Listeria monocytogenes* — An international validation study with focus on processed and ready-to-eat meat and seafood. International Journal of Food Microbiology, 141, 137-150. https://doi.org/10.1016/j.ijfoodmicro.2010.04.026

Mikami N, Toyotome T, Yamashiro Y, Sugo K, Yoshitomi K, Takaya M, Han K-H, Fukushima M and Shimada K, 2021. Dry-aged beef manufactured in Japan: microbiota identification and their effects on product characteristics. Food Research International, 140, 110020. https://doi.org/10.1016/j.foodres.2020.110020

Moschonas G, Bolton DJ, Sheridan JJ and McDowell DA, 2009. Isolation and sources of 'blown pack' spoilage clostridia in beef abattoirs. Journal of Applied Microbiology, 107, 616-624. https://doi.org/10.1111/j.1365-2672.2009.04229.x

Narvaez-Bravo C, Taboada NE, Mutschall SK and Aslma M, 2017. Epidemiology of antimicrobial resistant *Campylobacter* spp. isolated from retail meats in Canada. International Journal of Food Microbiology, 253, 43-47. https://doi.org/10.1016/j.ijfoodmicro.2017.04.019

Neumeyer K, Ross T, Thomson G and McMeekin TA, 1997. Validation of a model describing the effects of temperature and water activity on the growth of psychrotrophic pseudomonads. International Journal of Food Microbiology, 38(1), 55-63. https://doi.org/10.1016/s0168-1605(97)00090-1

Ninios T, Lundén J, Korkeala H and Fredriksson-Ahomaa M, 2014. Meat Inspection and Control in the Slaughterhouse. Wiley.

Nychas GJE, Marshall D and Sofos J, 2007. Food microbiology: fundamentals and frontiers. Meat Poultry and Seafood. ASM Press, Washington, DC. pp. 105-140.

Obuz E, Akkaya L, Gok V and Dikeman ME, 2014. Effects of blade tenderization, aging method and aging time on meat quality characteristics of *Longissimus lumborum* steaks from cull Holstein cows. Meat Science, 96, 1227-1232. https://doi.org/10.1016/j.meatsci.2013.11.015

Oh J, Lee HJ, Kim HC, Kim HJ, Yun YG, Kim KT, Choi YI and Jo C, 2018. The effects of dry or wet aging on the quality of the longissimus muscle from 4-year-old Hanwoo cows and 28-month-old Hanwoo steers. Animal Production Science, 58, 2344-2351. https://doi.org/10.1071/an17104

Oh H, Lee HJ, Lee J, Jo C and Yoon Y, 2019a. Identification of microorganisms associated with the quality improvement of dry-aged beef through microbiome analysis and DNA sequencing, and evaluation of their effects on beef quality. Journal of Food Science, 84, 2944-2954. https://doi.org/10.1111/1750-3841.14813

Oh J, Lee HJ, Yoon JW, Choe J and Jo C, 2019b. Electrical resistance and mold distribution on beef surface as indicators of dry aging. Journal of Food Process Engineering, 42, e13122. https://doi.org/10.1111/jfpe.13122
Lowry PD and Gill CO, 1984. Temperature and water activity minima for growth of spoilage moulds from meat. Journal of Applied Bacteriology, 56, 193-199. https://doi.org/10.1111/j.1365-2672.1984.tb01339.x

Lynt RK, Kautter DA and Solomon HM, 1982. Heat resistance of proteolytic *Clostridium botulinum* type F in phosphate buffer and crabmeat. Journal of Food Science, 47, 204-206. https://doi.org/10.1111/j.1365-2621.1982.tb11059.x

Mano SB, De Fernando GDG, Lopez-Galvez D, Selgas MD, Garcia ML, Cambero MI and OrdoÑez JA, 1995. Growth/survival of natural flora and *Listeria monocytogenes* on refrigerated uncooked pork and turkey packaged under modified atmospheres. Journal of Food Safety, 15, 305-319. https://doi.org/10.1111/j.1745-4565.1995.tb00142.x

Marcos Lázaro P, Whyte P, Rogers T, McElroy M, Fanning S, Frías J and Bolton D, 2021. The prevalence of *Clostridioides difficile* on farms, in abattoirs and in retail foods in Ireland. Food Microbiology, 98, 103781. https://doi.org/10.1016/j.fm.2021.103781

Martínez PO, Fredriksson-Ahomaa M, Pallotti A, Rosmini R, Houf K and Korkeala H, 2011. Variation in the prevalence of enteropathogenic *Yersinia* in slaughter pigs from Belgium, Italy, and Spain. Foodborne Pathogens and Disease, 8, 445-450. https://doi.org/10.1089/fpd.2009.0461

Martins SIFS, Jongen WMF and van Boekel MAJS, 2000. A review of Maillard reaction in food and implications to kinetic modelling. Trends in Food Science & Technology, 11, 364-373. https://doi.org/10.1016/S0924-2244(01)00022-X

McKenna DR, Strachan DS, Miller RK, Acuff GR and Savell JW, 2003. Cranberry juice marinade improves sensory and microbiological properties of vacuum-packaged lamb chops. Journal of Muscle Foods, 14, 207-220. https://doi.org/10.1111/j.1745-4573.2003.tb00701.x

McSharry S, Koolman L, Whyte P and Bolton D, 2021. The microbiology of beef from carcass chilling through primal storage to retail steaks. Current Research in Food Science, 4, 150-162. https://doi.org/10.1016/j.crfs.2021.03.002

Mejlholm OLE and Dalgaard PAW, 2007. Modeling and predicting the growth of lactic acid bacteria in lightly preserved seafood and their inhibiting effect on *Listeria monocytogenes*. Journal of Food Protection, 70, 2485-2497. https://doi.org/10.4315/0362-028X-70.11.2485

Mejlholm OLE and Dalgaard PAW, 2009. Development and validation of an extensive growth and growth boundary model for *Listeria monocytogenes* in lightly preserved and ready-to-eat shrimp. Journal of Food Protection, 72, 2132-2143. https://doi.org/10.4315/0362-028X-72.10.2132

Mejlholm O and Dalgaard P, 2013. Development and validation of an extensive growth and growth boundary model for psychrotolerant *Lactobacillus* spp. in seafood and meat products. International Journal of Food Microbiology, 167, 244-260. https://doi.org/10.1016/j.ijfoodmicro.2013.09.013

Mejlholm O, Gunvig A, Borggaard C, Blom-Hanssen J, Mellefont L, Ross T, Leroi F, Else T, Visser D and Dalgaard P, 2010. Predicting growth rates and growth boundary of *Listeria monocytogenes* — An international validation study with focus on processed and ready-to-eat meat and seafood. International Journal of Food Microbiology, 141, 137-150. https://doi.org/10.1016/j.ijfoodmicro.2010.04.026

Mikami N, Toyotome T, Yamashiro Y, Sugo K, Yoshitomi K, Takaya M, Han K-H, Fukushima M and Shimada K, 2021. Dry-aged beef manufactured in Japan: microbiota identification and their effects on product characteristics. Food Research International, 140, 110020. https://doi.org/10.1016/j.foodres.2020.110020

Moschonas G, Bolton DJ, Sheridan JJ and McDowell DA, 2009. Isolation and sources of 'blown pack' spoilage clostridia in beef abattoirs. Journal of Applied Microbiology, 107, 616-624. https://doi.org/10.1111/j.1365-2672.2009.04229.x

Narvaez-Bravo C, Taboada NE, Mutschall SK and Aslma M, 2017. Epidemiology of antimicrobial resistant *Campylobacter* spp. isolated from retail meats in Canada. International Journal of Food Microbiology, 253, 43-47. https://doi.org/10.1016/j.ijfoodmicro.2017.04.019

Neumeyer K, Ross T, Thomson G and McMeekin TA, 1997. Validation of a model describing the effects of temperature and water activity on the growth of psychrotrophic pseudomonads. International Journal of Food Microbiology, 38(1), 55-63. https://doi.org/10.1016/s0168-1605(97)00090-1

Ninios T, Lundén J, Korkeala H and Fredriksson-Ahomaa M, 2014. Meat Inspection and Control in the Slaughterhouse. Wiley.

Nychas GJE, Marshall D and Sofos J, 2007. Food microbiology: fundamentals and frontiers. Meat Poultry and Seafood. ASM Press, Washington, DC. pp. 105-140.

Obuz E, Akkaya L, Gok V and Dikeman ME, 2014. Effects of blade tenderization, aging method and aging time on meat quality characteristics of *Longissimus lumborum* steaks from cull Holstein cows. Meat Science, 96, 1227-1232. https://doi.org/10.1016/j.meatsci.2013.11.015

Oh J, Lee HJ, Kim HC, Kim HJ, Yun YG, Kim KT, Choi YI and Jo C, 2018. The effects of dry or wet aging on the quality of the longissimus muscle from 4-year-old Hanwoo cows and 28-month-old Hanwoo steers. Animal Production Science, 58, 2344-2351. https://doi.org/10.1071/an17104

Oh H, Lee HJ, Lee J, Jo C and Yoon Y, 2019a. Identification of microorganisms associated with the quality improvement of dry-aged beef through microbiome analysis and DNA sequencing, and evaluation of their effects on beef quality. Journal of Food Science, 84, 2944-2954. https://doi.org/10.1111/1750-3841.14813

Oh J, Lee HJ, Yoon JW, Choe J and Jo C, 2019b. Electrical resistance and mold distribution on beef surface as indicators of dry aging. Journal of Food Process Engineering, 42, e13122. https://doi.org/10.1111/jfpe.13122
Olivier S, 2018. Assessment of the mycological risks associated with the dry-ageing of meat. MLA project V.MFS.0426. CSIRO, Sydney, NSW.

Ostry V, Malir F, Toman J and Grosse Y, 2017. Mycotoxins as human carcinogens - the IARC Monographs classification. Mycotoxin Research, 33, 65–73.

Owczarek-Fendor A, Vermeulen A, Van Bree I, Eriksson M, Lescouhier S, De Smet S, De Meulenaer B and Devlieghere F, 2014. Effect of muscle, ageing time and modified atmosphere packaging conditions on the colour, oxidative and microbiological stability of packed beef. International Journal of Food Science and Technology, 49, 1090–1098. https://doi.org/10.1111/ijfs.12404

Özbaş ZY, Vural H and Aytaç SA, 1996. Effects of modified atmosphere and vacuum packaging on the growth of spoilage and inoculated pathogenic bacteria on fresh poultry. Zeitschrift für Lebensmittel Untersuchung und Forschung, 203, 326–332. https://doi.org/10.1007/BF01231070

Panella-Riera N, Possas A, Jofré A, Albano M, Comaposada J and Bover-Cid S, 2021. Assessment of the microbiological safety associated with the dynamics of the meat surface, aw, pH and temperature during beef dry aging process. Poster presented at the 67th International Congress of Meat Science and Technology, 23 27th August 2021, Kraków, Poland.

Park B, Yong HI, Choe J and Jo C, 2018. Utilization of the crust from dry-aged beef to enhance flavor of beef patties. Korean Journal for Food Science of Animal Resources, 38, 1019–1028. https://doi.org/10.5851/kosfa. 2018.e35

Parrish FC Jr, Boles JA, Rust RE and Olson DG, 1991. Dry and wet aging effects on palatability attributes of beef loin and rib steaks from three quality grades. Journal of Food Science, 56, 601–603. https://doi.org/ 10.1111/j.1365-2621.1991.tb05338.x

Peck MW, Webb MD and Goodburn KE, 2020. Assessment of the risk of botulism from chilled, vacuum/modified atmosphere packed fresh beef, lamb and pork held at 3°C–8°C. Food Microbiol, 91, 103544. https://doi.org/ 10.1016/j.fm.2020.103544

Perry N, 2012. Dry aging beef. International Journal of Gastronomy and Food Science, 1, 78–80. https://doi.org/ 10.1016/j.ijgfs.2011.11.005

Petsios S, Fredriksson-Ahomaa M, Sakkas H and Papadopoulou C, 2016. Conventional and molecular methods used in the detection and subtyping of *Yersinia enterocolitica* in food. International Journal of Food Microbiology, 237, 55–72. https://doi.org/10.1016/j.ijfoodmicro.2016.08.015

Phillips D, Bridger K, Jenson I and Sumner J, 2012. An Australian National Survey of the microbiological quality of frozen boneless beef and beef primal cuts. Journal of Food Protection, 75, 1862–1866. https://doi.org/10.4315/ 0362-028X.JFP-12-135

Pitt JI and Hocking AD (Eds), 2009. Aspergillus flavus link. Fungi and Food Spoilage. 3rd edn. Springer, Dordrecht, Netherlands. pp. 305–311.

Pleadin J, Vulic A, Perkovic I, Kudumija N, Lesic T, Kis M, Zadravec M and Mitak M, 2019a. Mycotoxins aflatoxins and ochratoxins - a threat to the safety of traditional meat products. Meso, 21, 186–197. https://doi. org/10.31727/m.21.2.2

Pleadin J, Frece J and Markov K, 2019b. Chapter eight: mycotoxins in food and feed. Advances in Food and Nutrition Research, 89, 297–345. https://doi.org/10.1016/bs.afnr.2019.02.007

Pócsi I, Giacometti F, Ambrus Á and Logrieco AF, 2020. Editorial: *Aspergillus*-derived mycotoxins in the feed and food chain. Frontiers in Microbiology, 11 project V.MFS.0426. CSIRO, Sydney, NSW.

Pugazhendhi A, Michael D, Prakash D, Priyadarshini Krishnamaurthy P, Shanmuganathan R, Abdullah Al-Dhabi N, Duraipandiyan V, Valan Arasu M and Kaliannan T, 2020. Antibiogram and plasmid profiling of beta-lactamase producing multi drug resistant *Staphylococcus aureus* isolated from poultry litter. Journal of King Saud University - Science, 32, 2723–2727. https://doi.org/10.1016/j.jksus.2020.06.007

R Core Team, 2021. R: A Language and Environment for Statistical Computing. Available online: https://www. r-project.org/

Regulation (EC) No 178/2002, 2002. Regulation (EC) No 178/2002 of the European Parliament and of the Council of 28 January 2002 laying down the general principles and requirements of food law, establishing the European Food Safety Authority and laying down procedures in matters of food safety. OJ L 31, 1.2.2002.

Reid R, Fanning S, Whyte P, Kerry J, Lindqvist R, Yu Z and Bolton D, 2017. The microbiology of beef carcasses and primals during chilling and commercial storage. Food Microbiology, 61, 50–57. https://doi.org/10.1016/j.fm. 2016.08.003

Rezende-de-Souza JH, Cardello FAB, de Paula APM, Ribeiro FA, Calkins CR and Pflanzer SB, 2021. Profile of producers and production of dry-aged beef in Brazil. Food, 10. https://doi.org/10.3390/foods10102447

Ribeiro FA, Lau SK, Furbeck RA, Herrera NJ, Henriott ML, Bland NA, Fernando SC, Subbiah J, Sullivan GA and Calkins CR, 2021a. Ultimate pH effects on dry-aged beef quality. Meat Science, 172, 108365. https://doi.org/ 10.1016/j.meatsci.2020.108365

Ribeiro JC Jr, Santos IGCD, Dias BP, Augusto WF, Rodrigues EM and Miotto FRC, 2021b. Influence of dry and wet beef maturation on the microbiological quality and safety. Semina: Ciencias Agrarias, 42, 155–166. https://doi. org/10.5433/1679-0359.2021v42n1p155

Ribeiro FA, Lau SK, Henriott ML, Herrera NJ, Bland NA, Subbiah J and Calkins CR, 2020. Effects of relative humidity on meat quality in dry-aged beef. Nebraska Beef Cattle Reports, 1082.
Olivier S, 2018. Assessment of the mycological risks associated with the dry-ageing of meat. MLA project V.MFS.0426. CSIRO, Sydney, NSW.

Ostry V, Malir F, Toman J and Grosse Y, 2017. Mycotoxins as human carcinogens - the IARC Monographs classification. Mycotoxin Research, 33, 65-73.

Owczarek-Fendor A, Vermeulen A, Van Bree I, Eriksson M, Lescouhier S, De Smet S, De Meulenaer B and Devlieghere F, 2014. Effect of muscle, ageing time and modified atmosphere packaging conditions on the colour, oxidative and microbiological stability of packed beef. International Journal of Food Science and Technology, 49, 1090-1098. https://doi.org/10.1111/ijfs.12404

Özbaş ZY, Vural H and Aytaç SA, 1996. Effects of modified atmosphere and vacuum packaging on the growth of spoilage and inoculated pathogenic bacteria on fresh poultry. Zeitschrift für Lebensmittel Untersuchung und Forschung, 203, 326-332. https://doi.org/10.1007/BF01231070

Panella-Riera N, Possas A, Jofré A, Albano M, Comaposada J and Bover-Cid S, 2021. Assessment of the microbiological safety associated with the dynamics of the meat surface, aw, pH and temperature during beef dry aging process. Poster presented at the 67th International Congress of Meat Science and Technology, 23 27th August 2021, Kraków, Poland.

Park B, Yong HI, Choe J and Jo C, 2018. Utilization of the crust from dry-aged beef to enhance flavor of beef patties. Korean Journal for Food Science of Animal Resources, 38, 1019-1028. https://doi.org/10.5851/kosfa. 2018.e35

Parrish FC Jr, Boles JA, Rust RE and Olson DG, 1991. Dry and wet aging effects on palatability attributes of beef loin and rib steaks from three quality grades. Journal of Food Science, 56, 601-603. https://doi.org/ 10.1111/j.1365-2621.1991.tb05338.x

Peck MW, Webb MD and Goodburn KE, 2020. Assessment of the risk of botulism from chilled, vacuum/modified atmosphere packed fresh beef, lamb and pork held at 3°C-8°C. Food Microbiol, 91, 103544. https://doi.org/ 10.1016/j.fm.2020.103544

Perry N, 2012. Dry aging beef. International Journal of Gastronomy and Food Science, 1, 78-80. https://doi.org/ 10.1016/j.ijgfs.2011.11.005

Petsios S, Fredriksson-Ahomaa M, Sakkas H and Papadopoulou C, 2016. Conventional and molecular methods used in the detection and subtyping of *Yersinia enterocolitica* in food. International Journal of Food Microbiology, 237, 55-72. https://doi.org/10.1016/j.ijfoodmicro.2016.08.015

Phillips D, Bridger K, Jenson I and Sumner J, 2012. An Australian National Survey of the microbiological quality of frozen boneless beef and beef primal cuts. Journal of Food Protection, 75, 1862-1866. https://doi.org/10.4315/ 0362-028X.JFP-12-135

Pitt JI and Hocking AD (Eds), 2009. Aspergillus flavus link. Fungi and Food Spoilage. 3rd edn. Springer, Dordrecht, Netherlands. pp. 305-311.

Pleadin J, Vulic A, Perkovic I, Kudumija N, Lesic T, Kis M, Zadravec M and Mitak M, 2019a. Mycotoxins aflatoxins and ochratoxins - a threat to the safety of traditional meat products. Meso, 21, 186-197. https://doi. org/10.31727/m.21.2.2

Pleadin J, Frece J and Markov K, 2019b. Chapter eight: mycotoxins in food and feed. Advances in Food and Nutrition Research, 89, 297-345. https://doi.org/10.1016/bs.afnr.2019.02.007

Pócsi I, Giacometti F, Ambrus Á and Logrieco AF, 2020. Editorial: *Aspergillus*-derived mycotoxins in the feed and food chain. Frontiers in Microbiology, 11 project V.MFS.0426. CSIRO, Sydney, NSW.

Pugazhendhi A, Michael D, Prakash D, Priyadarshini Krishnamaurthy P, Shanmuganathan R, Abdullah Al-Dhabi N, Duraipandiyan V, Valan Arasu M and Kaliannan T, 2020. Antibiogram and plasmid profiling of beta-lactamase producing multi drug resistant *Staphylococcus aureus* isolated from poultry litter. Journal of King Saud University - Science, 32, 2723-2727. https://doi.org/10.1016/j.jksus.2020.06.007

R Core Team, 2021. R: A Language and Environment for Statistical Computing. Available online: https://www. r-project.org/

Regulation (EC) No 178/2002, 2002. Regulation (EC) No 178/2002 of the European Parliament and of the Council of 28 January 2002 laying down the general principles and requirements of food law, establishing the European Food Safety Authority and laying down procedures in matters of food safety. OJ L 31, 1.2.2002.

Reid R, Fanning S, Whyte P, Kerry J, Lindqvist R, Yu Z and Bolton D, 2017. The microbiology of beef carcasses and primals during chilling and commercial storage. Food Microbiology, 61, 50-57. https://doi.org/10.1016/j.fm. 2016.08.003

Rezende-de-Souza JH, Cardello FAB, de Paula APM, Ribeiro FA, Calkins CR and Pflanzer SB, 2021. Profile of producers and production of dry-aged beef in Brazil. Food, 10. https://doi.org/10.3390/foods10102447

Ribeiro FA, Lau SK, Furbeck RA, Herrera NJ, Henriott ML, Bland NA, Fernando SC, Subbiah J, Sullivan GA and Calkins CR, 2021a. Ultimate pH effects on dry-aged beef quality. Meat Science, 172, 108365. https://doi.org/ 10.1016/j.meatsci.2020.108365

Ribeiro JC Jr, Santos IGCD, Dias BP, Augusto WF, Rodrigues EM and Miotto FRC, 2021b. Influence of dry and wet beef maturation on the microbiological quality and safety. Semina: Ciencias Agrarias, 42, 155-166. https://doi. org/10.5433/1679-0359.2021v42n1p155

Ribeiro FA, Lau SK, Henriott ML, Herrera NJ, Bland NA, Subbiah J and Calkins CR, 2020. Effects of relative humidity on meat quality in dry-aged beef. Nebraska Beef Cattle Reports, 1082.
Richardson EL, Fields B, Dilger AC and Boler DD, 2018. The effects of ultimate pH and color on sensory traits of pork loin chops cooked to a medium-rare degree of doneness. Journal of Animal Science, 96, 3768-3776. https://doi.org/10.1093/jas/sky258

Rodriguez A, Rodriguez M, Andrade MJ and Cordoba JJ, 2012. Development of a multiplex real-time PCR to quantify aflatoxin, ochratoxin A and patulin producing molds in foods. International Journal of Food Microbiology, 155, 10-18. https://doi.org/10.1016/j.ijfoodmicro.2012.01.007

Rodriguez A, Bernaldez V, Rodriguez M, Andrade MJ, Nunez F and Cordoba JJ, 2015. Effect of selected protective cultures on ochratoxin A accumulation in dry-cured Iberian ham during its ripening process. LWT- Food Science and Technology, 60, 923-928. https://doi.org/10.1016/j.lwt.2014.09.059

Rohatgi A, 2021. Webplotdigitizer: Version 4.5. Available online: https://automeris.io/WebPlotDigitizer

Ross T, 1999. Predictive food microbiology models in the meat industry. Meat and livestock. Australia, Sydney. pp. 196.

Russell L, Galindo CP, Whyte P and Bolton D, 2021. A preliminary study of *Salmonella* spp., *Listeria monocytogenes*, *Escherichia coli* O157, *Enterococcus faecalis* and *Clostridium* spp. in Irish cattle. Irish Journal of Agricultural and Food Research, 60. https://doi.org/10.15212/ijafr-2020-0122

Russo F, Ercolini D and Villani F, 2006. Behaviour of *Brochothrix thermosphacta* in presence of other meat spoilage microbial groups. Food Microbiology, 23(8), 797-802. https://doi.org/10.1016/j.fm.2006.02.004

Ryu S, Park MR, Maburutse BE, Lee WJ, Park DJ, Cho S, Hwang I, Oh S and Kim Y, 2018. Diversity and characteristics of the meat microbiological community on dry-aged beef. Journal of Microbiology and Biotechnology, 28(1), 105-108.

Sakala RM, Hayashidani H, Kato Y, Hirata T, Makino Y, Fukushima A, Yamada T, Kaneuchi C and Ogawa M, 2002. Change in the composition of the microflora on vacuum-packaged beef during chiller storage. International Journal of Food Microbiology, 74, 87-99.

Şanlibaba P, 2022. Prevalence, antibiotic resistance, and enterotoxin production of Staphylococcus aureus isolated from retail raw beef, sheep, and lamb meat in Turkey. International Journal of Food Microbiology, 361, 109461. https://doi.org/10.1016/j.ijfoodmicro.2021.109461

Saraiva C, Fontes MC, Patarata L, Martins C, Cadavez V and Gonzales-Barron U, 2016. Modelling the kinetics of *Listeria monocytogenes* in refrigerated fresh beef under different packaging atmospheres. LWT - Food Science and Technology, 66, 664-671. https://doi.org/10.1016/j.lwt.2015.11.026

Savell JW, 2008. Dry-aging of beef: Executive Summary. Place Center for Research and Knowledge Management. National Cattlemen's Beef Association, Center for Research and Knowledge Management. National Cattlemen's Beef Association.

Savell JW and Gehring K, 2018. Dry-aged beef revival: new thoughts on an old process. Available online: https://www.meatpoultry.com/articles/19412-dry-aged-beef-revival [Accessed: 17 Jan 2022].

Shi Y, Zhang W and Zhou G, 2020. Effects of different moisture-permeable packaging on the quality of aging beef compared with wet aging and dry aging. Food, 9, 647. https://doi.org/10.3390/foods9050649

da Silva ACM, de Oliveira PP, Pflanzer SB and da Silva do Nascimento M, 2019. Effect of different dry aging temperatures on *Listeria innocua* as surrogate for *Listeria monocytogenes*. Meat Science, 157, 107884. https://doi.org/10.1016/j.meatsci.2019.107884

da Silva APB, da Silva ACM, Ferreira FMS, Do Nascimento M and Pflanzer SB, 2020. The effects of time and relative humidity on dry-aged beef: traditional versus special bag. Food Science and Technology International, 27, 626-634. https://doi.org/10.1177/1082013220976487

Sitz B, Calkins C, Feuz D, Umberger W and Eskridge K, 2006. Consumer sensory acceptance and value of wet-aged and dry-aged beef steaks. Journal of Animal Science, 84, 1221-1226. https://doi.org/10.2527/2006.8451221x

Skandamis PN and Nychas G-JE, 2002a. Effect of oregano essential oil on microbiological and physicochemical attributes of minced meat stored in air and modified atmospheres. Journal of Applied Microbiology, 91, 1011-1022. https://doi.org/10.1046/j.1365-2672.2001.01467.x

Skandamis PN and Nychas G-JE, 2002b. Preservation of fresh meat with active and modified atmosphere packaging conditions. International Journal of Food Microbiology, 79(1-2), 35-45. https://doi.org/10.1016/S0168-1605(02)00177-0

Smaldone G, Marrone R, Vollano L, Peruzy MF, Barone CMA, Ambrosio RL and Anastasi A, 2019. Microbiological, rheological and physical-chemical characteristics of bovine meat subjected to a prolonged ageing period. Italian Journal of Food Safety, 8, 131-136. https://doi.org/10.4081/ijfs.2019.8100

Smith RD, Nicholson KL, Nicholson JDW, Harris KB, Miller RK, Griffin DB and Savell JW, 2008. Dry versus wet aging of beef: retail cutting yields and consumer palatability evaluations of steaks from US Choice and US Select short loins. Meat Science, 79, 631-639. https://doi.org/10.1016/j.meatsci.2007.10.028

Smith AM, Harris KB, Griffin DB, Miller RK, Kerth CR and Savell JW, 2014. Retail yields and palatability evaluations of individual muscles from wet-aged and dry-aged beef ribeyes and top sirloin butts that were merchandised innovatively. Meat Science, 97, 21-26. https://doi.org/10.1016/j.meatsci.2013.12.013

Smulders FJM, Hiesberger J, Hofbauer P, Dogl B and Dransfield E, 2006. Modified-atmosphere storage under subatmospheric pressure and beef quality: II. Color, drip, cooking loss, sarcomere length, and tenderness. Journal of Animal Science, 84, 2456-2462. https://doi.org/10.2527/jas.2005-684
Richardson EL, Fields B, Dilger AC and Boler DD, 2018. The effects of ultimate pH and color on sensory traits of pork loin chops cooked to a medium-rare degree of doneness. Journal of Animal Science, 96, 3768-3776. https://doi.org/10.1093/jas/sky258

Rodriguez A, Rodriguez M, Andrade MJ and Cordoba JJ, 2012. Development of a multiplex real-time PCR to quantify aflatoxin, ochratoxin A and patulin producing molds in foods. International Journal of Food Microbiology, 155, 10-18. https://doi.org/10.1016/j.ijfoodmicro.2012.01.007

Rodriguez A, Bernaldez V, Rodriguez M, Andrade MJ, Nunez F and Cordoba JJ, 2015. Effect of selected protective cultures on ochratoxin A accumulation in dry-cured Iberian ham during its ripening process. LWT- Food Science and Technology, 60, 923-928. https://doi.org/10.1016/j.lwt.2014.09.059

Rohatgi A, 2021. Webplotdigitizer: Version 4.5. Available online: https://automeris.io/WebPlotDigitizer

Ross T, 1999. Predictive food microbiology models in the meat industry. Meat and livestock. Australia, Sydney. pp. 196.

Russell L, Galindo CP, Whyte P and Bolton D, 2021. A preliminary study of *Salmonella* spp., *Listeria monocytogenes*, *Escherichia coli* O157, *Enterococcus faecalis* and *Clostridium* spp. in Irish cattle. Irish Journal of Agricultural and Food Research, 60. https://doi.org/10.15212/ijafr-2020-0122

Russo F, Ercolini D and Villani F, 2006. Behaviour of *Brochothrix thermosphacta* in presence of other meat spoilage microbial groups. Food Microbiology, 23(8), 797-802. https://doi.org/10.1016/j.fm.2006.02.004

Ryu S, Park MR, Maburutse BE, Lee WJ, Park DJ, Cho S, Hwang I, Oh S and Kim Y, 2018. Diversity and characteristics of the meat microbiological community on dry-aged beef. Journal of Microbiology and Biotechnology, 28(1), 105-108.

Sakala RM, Hayashidani H, Kato Y, Hirata T, Makino Y, Fukushima A, Yamada T, Kaneuchi C and Ogawa M, 2002. Change in the composition of the microflora on vacuum-packaged beef during chiller storage. International Journal of Food Microbiology, 74, 87-99.

Şanlibaba P, 2022. Prevalence, antibiotic resistance, and enterotoxin production of Staphylococcus aureus isolated from retail raw beef, sheep, and lamb meat in Turkey. International Journal of Food Microbiology, 361, 109461. https://doi.org/10.1016/j.ijfoodmicro.2021.109461

Saraiva C, Fontes MC, Patarata L, Martins C, Cadavez V and Gonzales-Barron U, 2016. Modelling the kinetics of *Listeria monocytogenes* in refrigerated fresh beef under different packaging atmospheres. LWT - Food Science and Technology, 66, 664-671. https://doi.org/10.1016/j.lwt.2015.11.026

Savell JW, 2008. Dry-aging of beef: Executive Summary. Place Center for Research and Knowledge Management. National Cattlemen's Beef Association, Center for Research and Knowledge Management. National Cattlemen's Beef Association.

Savell JW and Gehring K, 2018. Dry-aged beef revival: new thoughts on an old process. Available online: https://www.meatpoultry.com/articles/19412-dry-aged-beef-revival [Accessed: 17 Jan 2022].

Shi Y, Zhang W and Zhou G, 2020. Effects of different moisture-permeable packaging on the quality of aging beef compared with wet aging and dry aging. Food, 9, 647. https://doi.org/10.3390/foods9050649

da Silva ACM, de Oliveira PP, Pflanzer SB and da Silva do Nascimento M, 2019. Effect of different dry aging temperatures on *Listeria innocua* as surrogate for *Listeria monocytogenes*. Meat Science, 157, 107884. https://doi.org/10.1016/j.meatsci.2019.107884

da Silva APB, da Silva ACM, Ferreira FMS, Do Nascimento M and Pflanzer SB, 2020. The effects of time and relative humidity on dry-aged beef: traditional versus special bag. Food Science and Technology International, 27, 626-634. https://doi.org/10.1177/1082013220976487

Sitz B, Calkins C, Feuz D, Umberger W and Eskridge K, 2006. Consumer sensory acceptance and value of wet-aged and dry-aged beef steaks. Journal of Animal Science, 84, 1221-1226. https://doi.org/10.2527/2006.8451221x

Skandamis PN and Nychas G-JE, 2002a. Effect of oregano essential oil on microbiological and physicochemical attributes of minced meat stored in air and modified atmospheres. Journal of Applied Microbiology, 91, 1011-1022. https://doi.org/10.1046/j.1365-2672.2001.01467.x

Skandamis PN and Nychas G-JE, 2002b. Preservation of fresh meat with active and modified atmosphere packaging conditions. International Journal of Food Microbiology, 79(1-2), 35-45. https://doi.org/10.1016/S0168-1605(02)00177-0

Smaldone G, Marrone R, Vollano L, Peruzy MF, Barone CMA, Ambrosio RL and Anastasi A, 2019. Microbiological, rheological and physical-chemical characteristics of bovine meat subjected to a prolonged ageing period. Italian Journal of Food Safety, 8, 131-136. https://doi.org/10.4081/ijfs.2019.8100

Smith RD, Nicholson KL, Nicholson JDW, Harris KB, Miller RK, Griffin DB and Savell JW, 2008. Dry versus wet aging of beef: retail cutting yields and consumer palatability evaluations of steaks from US Choice and US Select short loins. Meat Science, 79, 631-639. https://doi.org/10.1016/j.meatsci.2007.10.028

Smith AM, Harris KB, Griffin DB, Miller RK, Kerth CR and Savell JW, 2014. Retail yields and palatability evaluations of individual muscles from wet-aged and dry-aged beef ribeyes and top sirloin butts that were merchandised innovatively. Meat Science, 97, 21-26. https://doi.org/10.1016/j.meatsci.2013.12.013

Smulders FJM, Hiesberger J, Hofbauer P, Dogl B and Dransfield E, 2006. Modified-atmosphere storage under subatmospheric pressure and beef quality: II. Color, drip, cooking loss, sarcomere length, and tenderness. Journal of Animal Science, 84, 2456-2462. https://doi.org/10.2527/jas.2005-684
Solomakos N, Govaris A, Koidis P and Botsoglou N, 2008. The antimicrobial effect of thyme essential oil, nisin, and their combination against *Listeria monocytogenes* in minced beef during refrigerated storage. Food Microbiology, 25, 120–127. https://doi.org/10.1016/j.fm.2007.07.002

Sommer NF, Buchanan JR and Fortlage RJ, 1974. Production of patulin by Penicillium expansum. Applied Microbiology, 28, 589–593.

Song Y, Liu C and Finegold Sydney M, 2004. Real-time PCR quantitation of clostridia in feces of autistic children. Applied and Environmental Microbiology, 70, 6459–6465. https://doi.org/10.1128/AEM.70.11.6459-6465.2004

Stanborough T, Fegan N, Powell SM, Tamplin M and Chandry PS, 2017. Insight into the genome of *Brochothrix thermosphacta*, a problematic meat spoilage bacterium. Applied and Environmental Microbiology, 83, e02786–e02716.

Stenstrom H, Li X, Hunt MC and Lundstrom K, 2014. Consumer preference and effect of correct or misleading information after ageing beef *longissimus* muscle using vacuum, dry-ageing, or a dry-ageing bag. Meat Science, 96, 661–666. https://doi.org/10.1016/j.meatsci.2013.10.022

Stern NJ, Pierson MD and Kotula AW, 1980. Effects of pH and sodium chloride on *Yersinia enterocolitica* growth at room and refrigeration temperatures. Journal of Food Science, 45, 64–67. https://doi.org/10.1111/j.1365-2621.1980.tb03871.x

Stuby-Souva MA, 1994. Tenderness and aging response of beef muscles of different quality grades before and after freezing.

Suman SP, Hunt MC, Nair MN and Rentfrow G, 2014. Improving beef color stability: practical strategies and underlying mechanisms. Meat Science, 98, 490–504. https://doi.org/10.1016/j.meatsci.2014.06.032

Sutherland JP and Bayliss AJ, 1994. Predictive modelling of growth of *Yersinia enterocolitica*: the effects of temperature, pH and sodium chloride. International Journal of Food Microbiology, 21, 197–215. https://doi.org/10.1016/0168-1605(94)90028-0

Terjung N, Witte F and Heinz V, 2021. The dry-aged beef paradox: why dry aging is sometimes not better than wet aging. Meat Science, 172, 108355. https://doi.org/10.1016/j.meatsci.2020.108355

Thépault A, Poezevara T, Quesne S, Rose V, Chemaly M and Rivoal K, 2018. Prevalence of thermophilic *Campylobacter* in cattle production at slaughterhouse level in France and link between C. jejuni bovine strains and campylobacteriosis. Frontiers in Microbiology, 9.

Tittor AW, Tittor MG, Brashears MM, Brooks JC, Garmyn AJ and Miller MF, 2011. Effects of simulated dry and wet chilling and aging of beef fat and lean tissues on the reduction of *Escherichia coli* O157:H7 and Salmonella. Journal of Food Protection, 74, 289–293.

Tsigarida E and Nychas GJE, 2001. Ecophysiological attributes of *Lactobacillus* sp. and *Pseudomonas* sp. on sterile beef fillets in relation to storage temperature and film permeability. Journal of Applied Microbiology, 90, 696–705.

Tsigarida E, Skandamis P and Nychas GJE, 2000. Behaviour of *Listeria monocytogenes* and autochthonous flora on meat stored under aerobic, vacuum and modified atmosphere packaging conditions with or without the presence of oregano essential oil at 5°C. Journal of Applied Microbiology, 89, 901–909. https://doi.org/10.1046/j.1365-2672.2000.01170.x

Utama DT, Kim YJ, Jeong HS, Kim J, Barido FH and Lee SK, 2020. Comparison of meat quality, fatty acid composition and aroma volatiles of dry-aged beef from Hanwoo cows slaughtered at 60 or 80 months old. Asian-Australasian Journal of Animal Sciences, 33, 157–165. https://doi.org/10.5713/ajas.19.0205

Van Damme I, Habib I and De Zutter L, 2010. *Yersinia enterocolitica* in slaughter pig tonsils: enumeration and detection by enrichment versus direct plating culture. Food Microbiology, 27, 158–161. https://doi.org/10.1016/j.fm.2009.09.011

Van Damme I, Berkvens D, Vanantwerpen G, Baré J, Houf K, Wauters G and De Zutter L, 2015. Contamination of freshly slaughtered pig carcasses with enteropathogenic *Yersinia* spp.: distribution, quantification and identification of risk factors. International Journal of Food Microbiology, 204, 33–40. https://doi.org/10.1016/j.ijfoodmicro.2015.03.016

Van Damme I, Varalakshmi S, De Zutter L, Vossen E and De Smet S, 2022. Decrease of *Salmonella* and *Escherichia coli* O157:H7 counts during dry-aging of beef but potential growth of *Listeria monocytogenes* under certain dry-aging conditions. Food Microbiology, 104, 104000. https://doi.org/10.1016/j.fm.2022.104000

Varga J, Baranyi N, Chandrasekaran M, Vágvölgyi C and Kocsubé S, 2015. Mycotoxin producers in the *Aspergillus* genus: an update. Acta Biologica Szegediensis, 59, 151–167.

Vásquez MSM, Héctor SM and Inés MO, 2009. Evaluation of bacteriocines as protective means for the biopreservation of refrigerated meat. Revista Chilena de Nutricion, 36, 228–238.

Vieira C, Diaz MT, Martinez B and Garcia-Cachan MD, 2009. Effect of frozen storage conditions (temperature and length of storage) on microbiological and sensory quality of rustic crossbred beef at different states of ageing. Meat Science, 83, 398–404. https://doi.org/10.1016/j.meatsci.2009.06.013

Wang Y, Li F, Zhuang H, Chen X, Li L, Qiao W and Zhang J, 2015. Effects of plant polyphenols and alpha-tocopherol on lipid oxidation, residual nitrites, biogenic amines, and N-nitrosamines formation during ripening and storage of dry-cured bacon. LWT-Food Science and Technology, 60, 199–206. https://doi.org/10.1016/j.lwt.2014.09.022
Solomakos N, Govaris A, Koidis P and Botsoglou N, 2008. The antimicrobial effect of thyme essential oil, nisin, and their combination against *Listeria monocytogenes* in minced beef during refrigerated storage. Food Microbiology, 25, 120–127. https://doi.org/10.1016/j.fm.2007.07.002

Sommer NF, Buchanan JR and Fortlage RJ, 1974. Production of patulin by *Penicillium* expansum. Applied Microbiology, 28, 589–593.

Song Y, Liu C and Finegold Sydney M, 2004. Real-time PCR quantitation of clostridia in feces of autistic children. Applied and Environmental Microbiology, 70, 6459–6465. https://doi.org/10.1128/AEM.70.11.6459-6465.2004

Stanborough T, Fegan N, Powell SM, Tamplin M and Chandry PS, 2017. Insight into the genome of *Brochothrix thermosphacta*, a problematic meat spoilage bacterium. Applied and Environmental Microbiology, 83, e02786–e02716.

Stenstrom H, Li X, Hunt MC and Lundstrom K, 2014. Consumer preference and effect of correct or misleading information after ageing beef *longissimus* muscle using vacuum, dry-ageing, or a dry-ageing bag. Meat Science, 96, 661–666. https://doi.org/10.1016/j.meatsci.2013.10.022

Stern NJ, Pierson MD and Kotula AW, 1980. Effects of pH and sodium chloride on *Yersinia enterocolitica* growth at room and refrigeration temperatures. Journal of Food Science, 45, 64–67. https://doi.org/10.1111/j.1365-2621.1980.tb03871.x

Stuby-Souva MA, 1994. Tenderness and aging response of beef muscles of different quality grades before and after freezing.

Suman SP, Hunt MC, Nair MN and Rentfrow G, 2014. Improving beef color stability: practical strategies and underlying mechanisms. Meat Science, 98, 490–504. https://doi.org/10.1016/j.meatsci.2014.06.032

Sutherland JP and Bayliss AJ, 1994. Predictive modelling of growth of *Yersinia enterocolitica*: the effects of temperature, pH and sodium chloride. International Journal of Food Microbiology, 21, 197–215. https://doi.org/10.1016/0168-1605(94)90028-0

Terjung N, Witte F and Heinz V, 2021. The dry-aged beef paradox: why dry aging is sometimes not better than wet aging. Meat Science, 172, 108355. https://doi.org/10.1016/j.meatsci.2020.108355

Thépault A, Poezevara T, Quesne S, Rose V, Chemaly M and Rivoal K, 2018. Prevalence of thermophilic *Campylobacter* in cattle production at slaughterhouse level in France and link between C. jejuni bovine strains and campylobacteriosis. Frontiers in Microbiology, 9.

Tittor AW, Tittor MG, Brashears MM, Brooks JC, Garmyn AJ and Miller MF, 2011. Effects of simulated dry and wet chilling and aging of beef fat and lean tissues on the reduction of *Escherichia coli* O157:H7 and Salmonella. Journal of Food Protection, 74, 289–293.

Tsigarida E and Nychas GJE, 2001. Ecophysiological attributes of *Lactobacillus* sp. and *Pseudomonas* sp. on sterile beef fillets in relation to storage temperature and film permeability. Journal of Applied Microbiology, 90, 696–705.

Tsigarida E, Skandamis P and Nychas GJE, 2000. Behaviour of *Listeria monocytogenes* and autochthonous flora on meat stored under aerobic, vacuum and modified atmosphere packaging conditions with or without the presence of oregano essential oil at 5°C. Journal of Applied Microbiology, 89, 901–909. https://doi.org/10.1046/j.1365-2672.2000.01170.x

Utama DT, Kim YJ, Jeong HS, Kim J, Barido FH and Lee SK, 2020. Comparison of meat quality, fatty acid composition and aroma volatiles of dry-aged beef from Hanwoo cows slaughtered at 60 or 80 months old. Asian-Australasian Journal of Animal Sciences, 33, 157–165. https://doi.org/10.5713/ajas.19.0205

Van Damme I, Habib I and De Zutter L, 2010. *Yersinia enterocolitica* in slaughter pig tonsils: enumeration and detection by enrichment versus direct plating culture. Food Microbiology, 27, 158–161. https://doi.org/10.1016/j.fm.2009.09.011

Van Damme I, Berkvens D, Vanantwerpen G, Baré J, Houf K, Wauters G and De Zutter L, 2015. Contamination of freshly slaughtered pig carcasses with enteropathogenic *Yersinia* spp.: distribution, quantification and identification of risk factors. International Journal of Food Microbiology, 204, 33–40. https://doi.org/10.1016/j.ijfoodmicro.2015.03.016

Van Damme I, Varalakshmi S, De Zutter L, Vossen E and De Smet S, 2022. Decrease of *Salmonella* and *Escherichia coli* O157:H7 counts during dry-aging of beef but potential growth of *Listeria monocytogenes* under certain dry-aging conditions. Food Microbiology, 104, 104000. https://doi.org/10.1016/j.fm.2022.104000

Varga J, Baranyi N, Chandrasekaran M, Vágvölgyi C and Kocsubé S, 2015. Mycotoxin producers in the *Aspergillus* genus: an update. Acta Biologica Szegediensis, 59, 151–167.

Vásquez MSM, Héctor SM and Inés MO, 2009. Evaluation of bacteriocines as protective means for the biopreservation of refrigerated meat. Revista Chilena de Nutricion, 36, 228–238.

Vieira C, Diaz MT, Martinez B and Garcia-Cachan MD, 2009. Effect of frozen storage conditions (temperature and length of storage) on microbiological and sensory quality of rustic crossbred beef at different states of ageing. Meat Science, 83, 398–404. https://doi.org/10.1016/j.meatsci.2009.06.013

Wang Y, Li F, Zhuang H, Chen X, Li L, Qiao W and Zhang J, 2015. Effects of plant polyphenols and alpha-tocopherol on lipid oxidation, residual nitrites, biogenic amines, and N-nitrosamines formation during ripening and storage of dry-cured bacon. LWT-Food Science and Technology, 60, 199–206. https://doi.org/10.1016/j.lwt.2014.09.022
Wang H, Zhang X, Wang G, Kun J, Xu X and Zhou G, 2017. Bacterial community and spoilage profiles shift in response to packaging in yellow-feather broiler, a highly popular meat in Asia. Frontiers in Microbiology, 8, 2588. https://doi.org/10.3389/fmicb.2017.02588

Watanabe A, Kamada G, Imanari M, Shiba N, Yonai M and Muramoto T, 2015. Effect of aging on volatile compounds in cooked beef. Meat Science, 107, 12–19. https://doi.org/10.1016/j.meatsci.2015.04.004

Whyte P, McGill K, Cowley D, Madden RH, Moran L, Scates P, Carroll C, O'Leary A, Fanning S, Collins JD, McNamara E, Moore JE and Cormican M, 2004. Occurrence of *Campylobacter* in retail foods in Ireland. International Journal of Food Microbiology, 95, 111–118. https://doi.org/10.1016/j.ijfoodmicro.2003.10.018

Witte F, Smetana S, Heinz V and Terjung N, 2020. High-pressure processing of usually discarded dry-aged beef trimmings for subsequent processing. Meat Science, 170, 108241. https://doi.org/10.1016/j.meatsci.2020.108241

Woraprayote W, Malila Y, Sorapukdee S, Swetwiwatharna A, Benjakul S and Visessanguan W, 2016. Bacteriocins from lactic acid bacteria and their applications in meat and meat products. Meat Science, 120, 118–132. https://doi.org/10.1016/j.meatsci.2016.04.004

Yaylayan VA, Keyhani A and Wnorowski A, 2000. Formation of sugar-specific reactive intermediates from 13C-Labeled I-Serines. Journal of Agricultural and Food Chemistry, 48, 636–641. https://doi.org/10.1021/jf990687a

Yu HH, Song YJ, Kim YJ, Lee H, Choi YS, Lee NA-K and Paik H-D, 2020. Predictive model of growth kinetics for *Staphylococcus aureus* in raw beef under various packaging systems. Meat Science, 165, J108108. https://doi.org/10.1016/j.meatsci.2020.108108

Zadravec M, Vahcić N, Brnić D, Markov K, Frece J, Beck R, Lešić T and Pleadin J, 2019. A study of surface moulds and mycotoxins in Croatian traditional dry-cured meat products. International Journal of Food Microbiology, 317, 108459. https://doi.org/10.1016/j.ijfoodmicro.2019.108459

Zeaki N, Johler S, Skandamis PN and Schelin J, 2019. The role of regulatory mechanisms and environmental parameters in staphylococcal food poisoning and resulting challenges to risk assessment. Frontiers in Microbiology, 10, 1307.

Zhang S and Mustapha A, 1999. Reduction of *Listeria monocytogenes* and *Escherichia coli* O157:H7 numbers on vacuum-packaged fresh beef treated with nisin or nisin combined with EDTA†. Journal of Food Protection, 62, 1123–1127. https://doi.org/10.4315/0362-028X-62.10.1123

Zhang R, Yoo MJY, Realini CE, Staincliffe M and Farouk MM, 2021. In-bag dry- vs. wet-aged lamb: quality, consumer acceptability, oxidative stability and in vitro digestibility. Food, 10, 41. https://doi.org/10.3390/foods10010041

Zheng J, Wittouck S, Salvetti E, Franz C, Harris HMB, Mattarelli P, O'Toole PW, Pot B, Vandamme P, Walter J, Watanabe K, Wuyts S, Felis GE, Ganzle MG and Lebeer S, 2020. A taxonomic note on the genus *Lactobacillus*: description of 23 novel genera, emended description of the genus *Lactobacillus* Beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae. International Journal of Systematic and Evolutionary Microbiology, 70, 2782–2858. https://doi.org/10.1099/ijsem.0.004107

## Abbreviations

<table><tr><td>Af</td><td>accuracy factor</td></tr><tr><td>AQ</td><td>assessment question</td></tr><tr><td>a<sub>w</sub></td><td>water activity</td></tr><tr><td>Bf</td><td>bias factor</td></tr><tr><td>CCP</td><td>critical control point</td></tr><tr><td>DFD</td><td>dark firm dry</td></tr><tr><td>FBO</td><td>food business operator</td></tr><tr><td>FSSP</td><td>Food Spoilage and Safety Predictor</td></tr><tr><td>GHP</td><td>good hygiene practice</td></tr><tr><td>GMP</td><td>good manufacturing practice</td></tr><tr><td>HACCP</td><td>Hazard Analysis and Critical Control Point</td></tr><tr><td>LAB</td><td>lactic acid bacteria</td></tr><tr><td>MSM</td><td>mechanically separated meat</td></tr><tr><td>PRP</td><td>prerequisite programme</td></tr><tr><td>RH</td><td>relative humidity</td></tr><tr><td>SQ</td><td>sub-question</td></tr><tr><td>STEC</td><td>Shiga toxin-producing <i>Escherichia coli</i></td></tr><tr><td>TEC</td><td>total Enterobacteriaceae count</td></tr><tr><td>ToR</td><td>Term of Reference</td></tr><tr><td>TVC</td><td>total viable count</td></tr></table>
Wang H, Zhang X, Wang G, Kun J, Xu X and Zhou G, 2017. Bacterial community and spoilage profiles shift in response to packaging in yellow-feather broiler, a highly popular meat in Asia. *Frontiers in Microbiology*, 8, 2588. https://doi.org/10.3389/fmicb.2017.02588

Watanabe A, Kamada G, Imanari M, Shiba N, Yonai M and Muramoto T, 2015. Effect of aging on volatile compounds in cooked beef. *Meat Science*, 107, 12–19. https://doi.org/10.1016/j.meatsci.2015.04.004

Whyte P, McGill K, Cowley D, Madden RH, Moran L, Scates P, Carroll C, O'Leary A, Fanning S, Collins JD, McNamara E, Moore JE and Cormican M, 2004. Occurrence of *Campylobacter* in retail foods in Ireland. *International Journal of Food Microbiology*, 95, 111–118. https://doi.org/10.1016/j.ijfoodmicro.2003.10.018

Witte F, Smetana S, Heinz V and Terjung N, 2020. High-pressure processing of usually discarded dry-aged beef trimmings for subsequent processing. *Meat Science*, 170, 108241. https://doi.org/10.1016/j.meatsci.2020.108241

Woraprayote W, Malila Y, Sorapukdee S, Swetwiwatharna A, Benjakul S and Visessanguan W, 2016. Bacteriocins from lactic acid bacteria and their applications in meat and meat products. *Meat Science*, 120, 118–132. https://doi.org/10.1016/j.meatsci.2016.04.004

Yaylayan VA, Keyhani A and Wnorowski A, 2000. Formation of sugar-specific reactive intermediates from 13C-Labeled I-Serines. *Journal of Agricultural and Food Chemistry*, 48, 636–641. https://doi.org/10.1021/jf990687a

Yu HH, Song YJ, Kim YJ, Lee H, Choi YS, Lee NA-K and Paik H-D, 2020. Predictive model of growth kinetics for *Staphylococcus aureus* in raw beef under various packaging systems. *Meat Science*, 165, J108108. https://doi.org/10.1016/j.meatsci.2020.108108

Zadravec M, Vahcić N, Brnić D, Markov K, Frece J, Beck R, Lešić T and Pleadin J, 2019. A study of surface moulds and mycotoxins in Croatian traditional dry-cured meat products. *International Journal of Food Microbiology*, 317, 108459. https://doi.org/10.1016/j.ijfoodmicro.2019.108459

Zeaki N, Johler S, Skandamis PN and Schelin J, 2019. The role of regulatory mechanisms and environmental parameters in staphylococcal food poisoning and resulting challenges to risk assessment. *Frontiers in Microbiology*, 10, 1307.

Zhang S and Mustapha A, 1999. Reduction of *Listeria monocytogenes* and *Escherichia coli* O157:H7 numbers on vacuum-packaged fresh beef treated with nisin or nisin combined with EDTA†. *Journal of Food Protection*, 62, 1123–1127. https://doi.org/10.4315/0362-028X-62.10.1123

Zhang R, Yoo MJY, Realini CE, Staincliffe M and Farouk MM, 2021. In-bag dry- vs. wet-aged lamb: quality, consumer acceptability, oxidative stability and in vitro digestibility. *Food*, 10, 41. https://doi.org/10.3390/foods10010041

Zheng J, Wittouck S, Salvetti E, Franz C, Harris HMB, Mattarelli P, O'Toole PW, Pot B, Vandamme P, Walter J, Watanabe K, Wuyts S, Felis GE, Ganzle MG and Lebeer S, 2020. A taxonomic note on the genus *Lactobacillus*: description of 23 novel genera, emended description of the genus *Lactobacillus* Beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae. *International Journal of Systematic and Evolutionary Microbiology*, 70, 2782–2858. https://doi.org/10.1099/ijsem.0.004107

## Abbreviations

<table><tr><td>Af</td><td>accuracy factor</td></tr><tr><td>AQ</td><td>assessment question</td></tr><tr><td>a<sub>w</sub></td><td>water activity</td></tr><tr><td>Bf</td><td>bias factor</td></tr><tr><td>CCP</td><td>critical control point</td></tr><tr><td>DFD</td><td>dark firm dry</td></tr><tr><td>FBO</td><td>food business operator</td></tr><tr><td>FSSP</td><td>Food Spoilage and Safety Predictor</td></tr><tr><td>GHP</td><td>good hygiene practice</td></tr><tr><td>GMP</td><td>good manufacturing practice</td></tr><tr><td>HACCP</td><td>Hazard Analysis and Critical Control Point</td></tr><tr><td>LAB</td><td>lactic acid bacteria</td></tr><tr><td>MSM</td><td>mechanically separated meat</td></tr><tr><td>PRP</td><td>prerequisite programme</td></tr><tr><td>RH</td><td>relative humidity</td></tr><tr><td>SQ</td><td>sub-question</td></tr><tr><td>STEC</td><td>Shiga toxin-producing <i>Escherichia coli</i></td></tr><tr><td>TEC</td><td>total Enterobacteriaceae count</td></tr><tr><td>ToR</td><td>Term of Reference</td></tr><tr><td>TVC</td><td>total viable count</td></tr></table>
3) Do you measure the initial and final pH, available water (aw) and/or surface temperature for each combination of meat ageing processes listed above? If so please provide these data in the table below.

<table><thead><tr><th rowspan="2">Meat ageing process(es)</th><th colspan="2">Surface pH</th><th colspan="2">Surface a<sub>w</sub></th><th colspan="2">Surface T (°C)</th></tr><tr><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

4) Do you test the initial and final bacterial load total viable count (TVC) and/or total Enterobacteriaceae count (TEC) and/or other bacteria for each combination?

If so, please provide the most recent data for each meat ageing process.

<table><thead><tr><th rowspan="2">Meat ageing process(es)</th><th colspan="2">TVC</th><th colspan="2">TEC</th><th colspan="2">Other specify</th><th colspan="2">Other specify</th><th colspan="2">Other specify</th></tr><tr><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th></tr></thead><tbody><tr><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

5) Do you test for *Salmonella*, *Shiga toxin-producing E. coli* (STEC), *Listeria monocytogenes*, *Staphylococcus aureus* and/or *Yersinia enterocolitica* (tick as appropriate). If so, what is the average prevalence on the meat before and/or after ageing (e.g. 1% of samples positive)

<table><thead><tr><th rowspan="2"></th><th colspan="2">Prevalence (average %)</th></tr><tr><th>Before ageing</th><th>After ageing</th></tr></thead><tbody><tr><td><strong>Salmonella</strong></td><td></td><td></td></tr><tr><td><strong>STEC</strong></td><td></td><td></td></tr><tr><td><strong>Listeria monocytogenes</strong></td><td></td><td></td></tr><tr><td><strong>Staphylococcus aureus</strong></td><td></td><td></td></tr><tr><td><strong>Yersinia enterocolitica</strong></td><td></td><td></td></tr><tr><td><strong>Other: Please specify</strong></td><td></td><td></td></tr></tbody></table>

6) Do you test dry-aged beef for moulds? Yes/No

If yes then:

(Sub-question for yes) Which moulds do you test for and what is the maximum concentration permitted for each mould tested for?

7) Do you test for mycotoxins in dry-aged beef? Yes/No

If yes then:

(Sub-question for yes) Which mycotoxins and what was the maximum concentration obtained in the last 6 months?

8) What hygiene practices do you use when preparing dry-aged beef? (Please tick as appropriate):

☐ Hand washing

☐ Knife washing/sterilisation

☐ Cleaning & disinfection of the production area

☐ Others (please specify all other hygiene measures used):

9) What are the recommended storage conditions and the shelf-life of the dry-aged beef you produce?

10) If you produce minced meat from the dry-aged beef what are the recommended storage conditions and the shelf-life of this product?

11) If you produce mechanically separated meat (MSM) from the dry-aged beef (what are the recommended storage conditions and the shelf-life of this product?
3) Do you measure the initial and final pH, available water (aw) and/or surface temperature for each combination of meat ageing processes listed above? If so please provide these data in the table below.

<table><thead><tr><th rowspan="2">Meat ageing process(es)</th><th colspan="2">Surface pH</th><th colspan="2">Surface a<sub>w</sub></th><th colspan="2">Surface T (°C)</th></tr><tr><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

4) Do you test the initial and final bacterial load total viable count (TVC) and/or total Enterobacteriaceae count (TEC) and/or other bacteria for each combination?

If so, please provide the most recent data for each meat ageing process.

<table><thead><tr><th rowspan="2">Meat ageing process(es)</th><th colspan="2">TVC</th><th colspan="2">TEC</th><th colspan="2">Other specify</th><th colspan="2">Other specify</th><th colspan="2">Other specify</th></tr><tr><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th></tr></thead><tbody><tr><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

5) Do you test for *Salmonella*, Shiga toxin-producing *E. coli* (STEC), *Listeria monocytogenes*, *Staphylococcus aureus* and/or *Yersinia enterocolitica* (tick as appropriate). If so, what is the average prevalence on the meat before and/or after ageing (e.g. 1% of samples positive)

<table><thead><tr><th rowspan="2"></th><th colspan="2">Prevalence (average %)</th></tr><tr><th>Before ageing</th><th>After ageing</th></tr></thead><tbody><tr><td><strong>Salmonella</strong></td><td></td><td></td></tr><tr><td><strong>STEC</strong></td><td></td><td></td></tr><tr><td><strong>Listeria monocytogenes</strong></td><td></td><td></td></tr><tr><td><strong>Staphylococcus aureus</strong></td><td></td><td></td></tr><tr><td><strong>Yersinia enterocolitica</strong></td><td></td><td></td></tr><tr><td><strong>Other: Please specify</strong></td><td></td><td></td></tr></tbody></table>

6) Do you test dry-aged beef for moulds? Yes/No

If yes then:

(Sub-question for yes) Which moulds do you test for and what is the maximum concentration permitted for each mould tested for?

7) Do you test for mycotoxins in dry-aged beef? Yes/No

If yes then:

(Sub-question for yes) Which mycotoxins and what was the maximum concentration obtained in the last 6 months?

8) What hygiene practices do you use when preparing dry-aged beef? (Please tick as appropriate):

☐ Hand washing

☐ Knife washing/sterilisation

☐ Cleaning & disinfection of the production area

☐ Others (please specify all other hygiene measures used):

9) What are the recommended storage conditions and the shelf-life of the dry-aged beef you produce?

10) If you produce minced meat from the dry-aged beef what are the recommended storage conditions and the shelf-life of this product?

11) If you produce mechanically separated meat (MSM) from the dry-aged beef (what are the recommended storage conditions and the shelf-life of this product?
## 12) Other information

<table><thead><tr><th></th><th>Weight of dry-aged beef produced in 2020</th><th>Weight of minced meat from dry-aged beef in 2020</th><th>Weight of MSM from dry-aged beef in 2020</th></tr></thead><tbody><tr><td>Dry-aged beef:</td><td></td><td></td><td></td></tr></tbody></table>

## 13) Do you have any additional comments which you consider relevant for the assessment?

### Wet/Vacuum Aged Beef, Pork & Lamb

1) What beef, pork and/or lamb cuts are wet/vacuum aged?

Beef: _________________________________

Pork: _________________________________

Lamb: _________________________________

Other: _________________________________

2) What temperature, time and packaging films (brand & product code) are used? (Please include all of the combinations of meat ageing processes used) (e.g. 2°C, 6 weeks, CryoVac - BB3055x)

<table><thead><tr><th>Meat ageing process(es)</th><th>Temperature (°C)</th><th>Duration of ageing (days)</th><th>Packaging (brand & product code)</th></tr></thead><tbody><tr><td>Beef</td><td></td><td></td><td></td></tr><tr><td>Pork</td><td></td><td></td><td></td></tr><tr><td>Lamb</td><td></td><td></td><td></td></tr></tbody></table>

3) Do you measure the initial and final pH, available water ($a_w$) and/or surface temperature? If so, please provide these data for your last batch

<table><thead><tr><th rowspan="2">Meat ageing condition</th><th colspan="2">pH</th><th colspan="2">$a_w$</th><th colspan="2">Surface T (°C)</th></tr><tr><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th></tr></thead><tbody><tr><td>Beef</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pork</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lamb</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

4) Do you test the initial and final bacterial load total viable count (TVC) and/or total Enterobacteriaceae count (TEC) and/or other bacteria (please specify)? If so, please provide the most recent data for each meat ageing process.

<table><thead><tr><th rowspan="2">Meat ageing process(es)</th><th colspan="2">TVC</th><th colspan="2">TEC</th><th colspan="2">Other Specify</th><th colspan="2">Other Specify</th><th colspan="2">Other Specify</th></tr><tr><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th></tr></thead><tbody><tr><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

5) Do you test for *Salmonella*, Shiga toxin-producing *E. coli* (STEC), *Listeria monocytogenes*, *Staphylococcus aureus* and/or *Yersinia enterocolitica* (tick as appropriate). If so, what is the average prevalence on the meat before and/or after ageing (e.g. 1% of samples positive)

<table><thead><tr><th rowspan="2"></th><th colspan="2">Prevalence (average %)</th></tr><tr><th>Before ageing</th><th>After ageing</th></tr></thead><tbody><tr><td>Salmonella</td><td></td><td></td></tr><tr><td>STEC</td><td></td><td></td></tr></tbody></table>
## 12) Other information

<table><thead><tr><th></th><th>Weight of dry-aged beef produced in 2020</th><th>Weight of minced meat from dry-aged beef in 2020</th><th>Weight of MSM from dry-aged beef in 2020</th></tr></thead><tbody><tr><td>Dry-aged beef:</td><td></td><td></td><td></td></tr></tbody></table>

## 13) Do you have any additional comments which you consider relevant for the assessment?

### Wet/Vacuum Aged Beef, Pork & Lamb

1) What beef, pork and/or lamb cuts are wet/vacuum aged?

Beef: _________________________________

Pork: _________________________________

Lamb: _________________________________

Other: _________________________________

2) What temperature, time and packaging films (brand & product code) are used? (Please include all of the combinations of meat ageing processes used) (e.g. 2°C, 6 weeks, CryoVac - BB3055x)

<table><thead><tr><th>Meat ageing process(es)</th><th>Temperature (°C)</th><th>Duration of ageing (days)</th><th>Packaging (brand & product code)</th></tr></thead><tbody><tr><td>Beef</td><td></td><td></td><td></td></tr><tr><td>Pork</td><td></td><td></td><td></td></tr><tr><td>Lamb</td><td></td><td></td><td></td></tr></tbody></table>

3) Do you measure the initial and final pH, available water ($a_w$) and/or surface temperature? If so, please provide these data for your last batch

<table><thead><tr><th rowspan="2">Meat ageing condition</th><th colspan="2">pH</th><th colspan="2">$a_w$</th><th colspan="2">Surface T (°C)</th></tr><tr><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th></tr></thead><tbody><tr><td>Beef</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pork</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lamb</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

4) Do you test the initial and final bacterial load total viable count (TVC) and/or total Enterobacteriaceae count (TEC) and/or other bacteria (please specify)? If so, please provide the most recent data for each meat ageing process.

<table><thead><tr><th rowspan="2">Meat ageing process(es)</th><th colspan="2">TVC</th><th colspan="2">TEC</th><th colspan="2">Other Specify</th><th colspan="2">Other Specify</th><th colspan="2">Other Specify</th></tr><tr><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th><th>Initial</th><th>Final</th></tr></thead><tbody><tr><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

5) Do you test for *Salmonella*, Shiga toxin-producing *E. coli* (STEC), *Listeria monocytogenes*, *Staphylococcus aureus* and/or *Yersinia enterocolitica* (tick as appropriate). If so, what is the average prevalence on the meat before and/or after ageing (e.g. 1% of samples positive)

<table><thead><tr><th rowspan="2"></th><th colspan="2">Prevalence (average %)</th></tr><tr><th>Before ageing</th><th>After ageing</th></tr></thead><tbody><tr><td>Salmonella</td><td></td><td></td></tr><tr><td>STEC</td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th rowspan="2"></th><th colspan="2">Prevalence (average %)</th></tr><tr><th>Before ageing</th><th>After ageing</th></tr></thead><tbody><tr><td>Listeria monocytogenes</td><td></td><td></td></tr><tr><td>Staphylococcus aureus</td><td></td><td></td></tr><tr><td>Yersinia enterocolitica</td><td></td><td></td></tr><tr><td>Other (please specify)</td><td></td><td></td></tr></tbody></table>

6) What hygiene practices do you use when preparing dry-aged beef? (Please tick as appropriate):

☐ Hand washing

☐ Knife washing/sterilisation

☐ Cleaning & disinfection of the production area

☐ Others (please specify all other hygiene measures used)

7) What are the recommended storage conditions and the shelf-life of the wet-aged beef, pork and/or lamb you produce?

8) If you produce minced meat from the wet-aged beef, pork or lamb, what are the recommended storage conditions and the shelf-life of this product?

9) If you produce mechanically separated meat (MSM) from wet-aged beef, pork or lamb, what are the recommended storage conditions and the shelf-life of this product?

10) Other information

<table><thead><tr><th></th><th>Weight of product produced in 2020</th><th>Weight of minced meat from wet-aged meat in 2020</th><th>Weight of MSM from wet-aged meat in 2020</th></tr></thead><tbody><tr><td>Wet/vacuum aged beef:</td><td></td><td></td><td></td></tr><tr><td>Wet/vacuum aged pork:</td><td></td><td></td><td></td></tr><tr><td>Wet/vacuum aged lamb:</td><td></td><td></td><td></td></tr></tbody></table>

11) Do you have any additional comments which you consider relevant for the assessment?

## A.2. Version 2 of the questionnaire sent on 02 December 2021

### General question

What is the usual time between slaughter and start of dry/wet-ageing?
(e.g. 1 day or 0–3 days)

### Questions on dry-ageing of beef

1) For how long is the beef typically dry-aged? (e.g. 28 days).

2) What specific temperature do you use for dry-ageing beef?
(This may be a single numerical value, e.g. 2°C or expressed as a range e.g. 0–3°C).

3) What is the relative humidity in the chamber when dry-ageing beef? (e.g. 75%).

4) If you check the temperature inside the beef ageing room/chamber and/or on the surface of beef during dry-ageing, what is the usual oscillation (minimum and maximum) and the actual mean temperature? (e.g. 4°C and the start and 1°C at the end of the dry-ageing process).

5) If you check the pH of the surface of the beef during dry-ageing, what are the usual start and finish pH values? If you have additional pH values, please specify.
(e.g. pH 5.9 at the start and 6.5 at the end of the dry-ageing process).

6) If you check the $a_w$ of the surface of the beef during dry-ageing, what are the usual start and finish $a_w$ values? If you have additional $a_w$ values, please specify.
(e.g. $a_w$ 0.998 and the start and 0.905 at the end of the dry-ageing process).
<table><thead><tr><th rowspan="2"></th><th colspan="2">Prevalence (average %)</th></tr><tr><th>Before ageing</th><th>After ageing</th></tr></thead><tbody><tr><td>Listeria monocytogenes</td><td></td><td></td></tr><tr><td>Staphylococcus aureus</td><td></td><td></td></tr><tr><td>Yersinia enterocolitica</td><td></td><td></td></tr><tr><td>Other (please specify)</td><td></td><td></td></tr></tbody></table>

6) What hygiene practices do you use when preparing dry-aged beef? (Please tick as appropriate):

☐ Hand washing

☐ Knife washing/sterilisation

☐ Cleaning & disinfection of the production area

☐ Others (please specify all other hygiene measures used)

7) What are the recommended storage conditions and the shelf-life of the wet-aged beef, pork and/or lamb you produce?

8) If you produce minced meat from the wet-aged beef, pork or lamb, what are the recommended storage conditions and the shelf-life of this product?

9) If you produce mechanically separated meat (MSM) from wet-aged beef, pork or lamb, what are the recommended storage conditions and the shelf-life of this product?

10) Other information

<table><thead><tr><th></th><th>Weight of product produced in 2020</th><th>Weight of minced meat from wet-aged meat in 2020</th><th>Weight of MSM from wet-aged meat in 2020</th></tr></thead><tbody><tr><td>Wet/vacuum aged beef:</td><td></td><td></td><td></td></tr><tr><td>Wet/vacuum aged pork:</td><td></td><td></td><td></td></tr><tr><td>Wet/vacuum aged lamb:</td><td></td><td></td><td></td></tr></tbody></table>

11) Do you have any additional comments which you consider relevant for the assessment?

## A.2. Version 2 of the questionnaire sent on 02 December 2021

### General question

What is the usual time between slaughter and start of dry/wet-ageing?
(e.g. 1 day or 0–3 days)

### Questions on dry-ageing of beef

1) For how long is the beef typically dry-aged? (e.g. 28 days).

2) What specific temperature do you use for dry-ageing beef?
(This may be a single numerical value, e.g. 2°C or expressed as a range e.g. 0–3°C).

3) What is the relative humidity in the chamber when dry-ageing beef? (e.g. 75%).

4) If you check the temperature inside the beef ageing room/chamber and/or on the surface of beef during dry-ageing, what is the usual oscillation (minimum and maximum) and the actual mean temperature? (e.g. 4°C and the start and 1°C at the end of the dry-ageing process).

5) If you check the pH of the surface of the beef during dry-ageing, what are the usual start and finish pH values? If you have additional pH values, please specify.
(e.g. pH 5.9 at the start and 6.5 at the end of the dry-ageing process).

6) If you check the $a_w$ of the surface of the beef during dry-ageing, what are the usual start and finish $a_w$ values? If you have additional $a_w$ values, please specify.
(e.g. $a_w$ 0.998 and the start and 0.905 at the end of the dry-ageing process).
Questions on wet-ageing (maturing in a vacuum pack under chilled conditions) of beef,
pork and lamb

1) For how long is the beef, pork or lamb typically wet-aged (i.e. prolonged ageing)?
(Please specify for each relevant meat species and the time, e.g. beef - 4 weeks, pork - 3 days, lamb - 4 days).

2) What specific temperature do you use for wet-ageing beef, pork or lamb?
(Please specify for each relevant meat species. This may be a single numerical value, e.g. 2°C or expressed as a range e.g. 0–3°C).

3) If you check the temperature of the chamber or of the surface of the beef, pork or lamb during wet-ageing, what are the usual oscillation (minimum and maximum)?
(Please specify for each relevant meat species. e.g. 4°C and the start and 1°C at the end of the process).

4) If you check the pH of the surface of the beef, pork or lamb during wet-ageing, what are the usual start and final pH values?
(e.g. pH 5.9 at the start and 6.5 at the end of the process).
Questions on wet-ageing (maturing in a vacuum pack under chilled conditions) of beef,
pork and lamb

1) For how long is the beef, pork or lamb typically wet-aged (i.e. prolonged ageing)?

(Please specify for each relevant meat species and the time, e.g. beef - 4 weeks, pork - 3 days, lamb - 4 days).

2) What specific temperature do you use for wet-ageing beef, pork or lamb?

(Please specify for each relevant meat species. This may be a single numerical value, e.g. 2°C or expressed as a range e.g. 0–3°C).

3) If you check the temperature of the chamber or of the surface of the beef, pork or lamb during wet-ageing, what are the usual oscillation (minimum and maximum)?

(Please specify for each relevant meat species. e.g. 4°C and the start and 1°C at the end of the process).

4) If you check the pH of the surface of the beef, pork or lamb during wet-ageing, what are the usual start and final pH values?

(e.g. pH 5.9 at the start and 6.5 at the end of the process).
## A.3. Summary of the replies of the questionnaire

<table><caption>**Table A.1:** Conditions reported for dry-ageing beef in response to the questionnaire, provided by food business operators (FBO) or associations of FBOs</caption><thead><tr><th>Respondents</th><th>EU/ MS</th><th>Time between slaughter and ageing (d)</th><th>Duration (days)</th><th>RH (%)</th><th>Specified T (°C)</th><th>Actual T (°C)</th><th>Specified meat T (°C)</th><th>Min-max meat T (°C)</th><th>Mean meat T (°C)</th><th>Start pH<sup>(b)</sup></th><th>End pH<sup>(b)</sup></th><th>Start a<sub>w</sub></th><th>End a<sub>w</sub></th></tr></thead><tbody><tr><td rowspan="2">1</td><td>EU</td><td>-</td><td>-</td><td>&lt; 85</td><td>0-4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>EU</td><td>1-5</td><td>Usually between 21-50 but up to 70 days</td><td>80 max</td><td>0-2</td><td>0-2</td><td>0-2</td><td>0-2</td><td>-</td><td>5.80 max (core)</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2</td><td>AT</td><td>3</td><td>21-35</td><td>72 (min. 62-76 max)</td><td>1-4</td><td>1-4</td><td>-</td><td>Start: 1-4, End: 1-4</td><td>-</td><td>5.4-5.8</td><td>-</td><td>-</td><td>-</td></tr><tr><td>3</td><td>ES</td><td>2</td><td>21</td><td>80</td><td>2-3</td><td>-</td><td>-</td><td>2-3</td><td>-</td><td>5.8 max</td><td>-</td><td>0.98</td><td>0.81</td></tr><tr><td>4<sup>(a)</sup></td><td>ES</td><td>2</td><td>60</td><td>80</td><td>2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>5.4</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">5</td><td>DE</td><td>2</td><td>21-28</td><td>80</td><td>0.5-2</td><td>-</td><td>-</td><td>-</td><td>-0.5 min; 4 max</td><td>5.9-5.7</td><td>6.2</td><td>-</td><td>-</td></tr><tr><td>BE</td><td>1-2</td><td>21-28</td><td>80 (75-85)</td><td>0-2</td><td>-</td><td>-</td><td>-</td><td>Start: 4° During process: 0-2°</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>6</td><td>BE</td><td>3 max</td><td>42 max</td><td>75-85</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>7<sup>(a)</sup></td><td>DE</td><td>2 to 7</td><td>21 min</td><td>90, varies depending on temperature</td><td>1-4</td><td>1-4</td><td>7 max upon receipt decreasing to 1-4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>8<sup>(a)</sup></td><td>DE</td><td>1 to 2</td><td>21 max</td><td>70</td><td>2</td><td>-</td><td>-</td><td>-</td><td>2 ± 0.5</td><td>5.8</td><td>-</td><td>-</td><td>-</td></tr><tr><td>9<sup>(a)</sup></td><td>DE</td><td>4 to 6</td><td>21, min 30 mean</td><td>75</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>&lt; 1</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>14</td><td>FR</td><td>1-3</td><td>28</td><td>-</td><td>0-4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>5.7 at 24 h</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Belgian survey</td><td>BE</td><td>1-21</td><td>14-77</td><td>Set 40-75 Read 46-84 Measured 39-95</td><td>-</td><td>Programmed -1-3</td><td>-</td><td>Read (display) T: 1-3.5 Measured T: -1.2°C-6.6</td><td>-</td><td>5.4-5.7 (core)</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

(a): Replies to the questionnaire provided by the hearing expert.

(b): Surface unless otherwise stated.
## A.3. Summary of the replies of the questionnaire

<table><caption>**Table A.1:** Conditions reported for dry-ageing beef in response to the questionnaire, provided by food business operators (FBO) or associations of FBOs</caption><thead><tr><th>Respondents</th><th>EU/ MS</th><th>Time between slaughter and ageing (d)</th><th>Duration (days)</th><th>RH (%)</th><th>Specified T (°C)</th><th>Actual T (°C)</th><th>Specified meat T (°C)</th><th>Min-max meat T (°C)</th><th>Mean meat T (°C)</th><th>Start pH<sup>(b)</sup></th><th>End pH<sup>(b)</sup></th><th>Start a<sub>w</sub></th><th>End a<sub>w</sub></th></tr></thead><tbody><tr><td rowspan="2">1</td><td>EU</td><td>–</td><td>–</td><td>&lt; 85</td><td>0-4</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>EU</td><td>1-5</td><td>Usually between 21-50 but up to 70 days</td><td>80 max</td><td>0-2</td><td>0-2</td><td>0-2</td><td>0-2</td><td>–</td><td>5.80 max (core)</td><td>–</td><td>–</td><td>–</td></tr><tr><td>2</td><td>AT</td><td>3</td><td>21-35</td><td>72 (min. 62-76 max)</td><td>1-4</td><td>1-4</td><td>–</td><td>Start: 1-4, End: 1-4</td><td>–</td><td>5.4-5.8</td><td>–</td><td>–</td><td>–</td></tr><tr><td>3</td><td>ES</td><td>2</td><td>21</td><td>80</td><td>2-3</td><td>–</td><td>–</td><td>2-3</td><td>–</td><td>5.8 max</td><td>–</td><td>0.98</td><td>0.81</td></tr><tr><td>4<sup>(a)</sup></td><td>ES</td><td>2</td><td>60</td><td>80</td><td>2</td><td>–</td><td>–</td><td>–</td><td>–</td><td>5.4</td><td>–</td><td>–</td><td>–</td></tr><tr><td rowspan="2">5</td><td>DE</td><td>2</td><td>21-28</td><td>80</td><td>0.5-2</td><td>–</td><td>–</td><td>–</td><td>–</td><td>5.9-5.7</td><td>6.2</td><td>–</td><td>–</td></tr><tr><td>BE</td><td>1-2</td><td>21-28</td><td>80 (75-85)</td><td>0-2</td><td>–</td><td>–</td><td>–</td><td>Start: 4° During process: 0-2°</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>6</td><td>BE</td><td>3 max</td><td>42 max</td><td>75-85</td><td>1</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>7<sup>(a)</sup></td><td>DE</td><td>2 to 7</td><td>21 min</td><td>90, varies depending on temperature</td><td>1-4</td><td>1-4</td><td>7 max upon receipt decreasing to 1-4</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>8<sup>(a)</sup></td><td>DE</td><td>1 to 2</td><td>21 max</td><td>70</td><td>2</td><td>–</td><td>–</td><td>–</td><td>2 ± 0.5</td><td>5.8</td><td>–</td><td>–</td><td>–</td></tr><tr><td>9<sup>(a)</sup></td><td>DE</td><td>4 to 6</td><td>21, min 30 mean</td><td>75</td><td>0.5</td><td>–</td><td>–</td><td>–</td><td>&lt; 1</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>14</td><td>FR</td><td>1-3</td><td>28</td><td>–</td><td>0-4</td><td>–</td><td>–</td><td>–</td><td>–</td><td>5.7 at 24 h</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Belgian survey</td><td>BE</td><td>1-21</td><td>14-77</td><td>Set 40-75 Read 46-84 Measured 39-95</td><td>–</td><td>Programmed -1-3</td><td>–</td><td>Read (display) T: 1-3.5 Measured T: -1.2°C-6.6</td><td>–</td><td>5.4-5.7 (core)</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>

(a): Replies to the questionnaire provided by the hearing expert.

(b): Surface unless otherwise stated.
<table><caption>**Table A.2:** Conditions reported for wet-ageing beef, pork and lamb in response to the questionnaire, provided by food business operators (FBO) or associations of FBOs</caption><thead><tr><th>Respondents</th><th>EU/ MS</th><th>Time between slaughter and ageing (d)</th><th>Duration (d)</th><th>Specified temperature (°C)</th><th>Chill room temperature (°C)</th><th>Minimum – maximum temperature (°C)</th><th>Mean meat temperature (°C)</th><th>Meat pH</th><th>Start pH</th><th>End pH</th></tr></thead><tbody><tr><td colspan="11"><strong>Beef</strong></td></tr><tr><td>1</td><td>EU</td><td>–</td><td>30–40 (longer when super chilled)</td><td>–</td><td>–2–0</td><td>–</td><td>–</td><td>Checked in the abattoir</td><td>–</td><td>–</td></tr><tr><td></td><td>EU</td><td>NP</td><td>14, 28 or 42</td><td>0–5</td><td>–</td><td>–</td><td>–</td><td>Core pH measured 18 &amp; 24 h post-mortem</td><td>–</td><td>–</td></tr><tr><td>2</td><td>AT</td><td>3</td><td>9</td><td>0–3</td><td>0–3</td><td>Start: 1–4<br/>End: 0–3</td><td>–</td><td>Not checked</td><td>5.4–5.8</td><td>–</td></tr><tr><td>4</td><td>ES</td><td>2</td><td>40</td><td>&lt; 5, optimum 2</td><td>–</td><td>2–5</td><td>–</td><td>Not checked</td><td>–</td><td>–</td></tr><tr><td></td><td>DE</td><td>2</td><td>–</td><td>2 to 7</td><td>–</td><td>2 to 7</td><td>–</td><td>Not answered</td><td>–</td><td>–</td></tr><tr><td>5</td><td>BE</td><td>1–2</td><td>28–42</td><td>0–2</td><td>–</td><td>Start: 4, Later: 0–2</td><td>–</td><td>Not checked</td><td>–</td><td>–</td></tr><tr><td>8<sup>(a)</sup></td><td>DE</td><td>1–2</td><td>49 max</td><td>2 ± 0.5</td><td>–</td><td>–</td><td>1.5–2</td><td>5.8</td><td>–</td><td>–</td></tr><tr><td>9<sup>(a)</sup></td><td>DE</td><td>4 to 6</td><td>35–42</td><td>–</td><td>1.5</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>10</td><td>IE</td><td>–</td><td>Customer specific, typically 14–46</td><td>0–2</td><td>–</td><td>&lt; 4°C start, 2°C within 24 h, thereafter &lt; 2°C</td><td>–</td><td>Checked pre-boning</td><td>5.7</td><td>–</td></tr><tr><td>11<sup>(a)</sup></td><td>DE</td><td>2</td><td>14–21</td><td>0–4</td><td>–</td><td>0–4</td><td>–</td><td>Not measured</td><td>–</td><td>–</td></tr><tr><td>12</td><td>BE</td><td>2</td><td>7–14</td><td>2</td><td>2 ± 1</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>13</td><td>BE</td><td>2</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>14</td><td>FR</td><td>1–3</td><td>28</td><td>4</td><td>–</td><td>Not measured</td><td>–</td><td>Not measured</td><td>–</td><td>–</td></tr><tr><td>Belgian study</td><td>BE</td><td>1–21</td><td>5 to 21 but usually 7–14</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td colspan="11"><strong>Pork</strong></td></tr><tr><td>2</td><td>AT</td><td>3</td><td>min 3</td><td>0–3</td><td>room: 0–3°C</td><td>Start: 1–4<br/>End: 0–3</td><td>–</td><td>–</td><td>5.4–5.8</td><td>–</td></tr><tr><td>9</td><td>DE</td><td>4–6</td><td>14–18</td><td>1.5</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>13</td><td>BE</td><td>2</td><td>18</td><td>0–4</td><td>–</td><td>–</td><td>–</td><td>Not routinely checked</td><td>–</td><td>–</td></tr><tr><td>14</td><td>FR</td><td>0.5–1</td><td>6</td><td>4</td><td>–</td><td>Not measured</td><td>Not measured</td><td>Not measured</td><td>–</td><td>–</td></tr></tbody></table>
<table><caption>**Table A.2:** Conditions reported for wet-ageing beef, pork and lamb in response to the questionnaire, provided by food business operators (FBO) or associations of FBOs</caption><thead><tr><th>Respondents</th><th>EU/ MS</th><th>Time between slaughter and ageing (d)</th><th>Duration (d)</th><th>Specified temperature (°C)</th><th>Chill room temperature (°C)</th><th>Minimum – maximum temperature (°C)</th><th>Mean meat temperature (°C)</th><th>Meat pH</th><th>Start pH</th><th>End pH</th></tr></thead><tbody><tr><td colspan="11"><strong>Beef</strong></td></tr><tr><td>1</td><td>EU</td><td>–</td><td>30–40 (longer when super chilled)</td><td>–</td><td>–2–0</td><td>–</td><td>–</td><td>Checked in the abattoir</td><td>–</td><td>–</td></tr><tr><td></td><td>EU</td><td>NP</td><td>14, 28 or 42</td><td>0–5</td><td>–</td><td>–</td><td>–</td><td>Core pH measured 18 &amp; 24 h post-mortem</td><td>–</td><td>–</td></tr><tr><td>2</td><td>AT</td><td>3</td><td>9</td><td>0–3</td><td>0–3</td><td>Start: 1–4<br/>End: 0–3</td><td>–</td><td>Not checked</td><td>5.4–5.8</td><td>–</td></tr><tr><td>4</td><td>ES</td><td>2</td><td>40</td><td>&lt; 5, optimum 2</td><td>–</td><td>2–5</td><td>–</td><td>Not checked</td><td>–</td><td>–</td></tr><tr><td></td><td>DE</td><td>2</td><td>–</td><td>2 to 7</td><td>–</td><td>2 to 7</td><td>–</td><td>Not answered</td><td>–</td><td>–</td></tr><tr><td>5</td><td>BE</td><td>1–2</td><td>28–42</td><td>0–2</td><td>–</td><td>Start: 4, Later: 0–2</td><td>–</td><td>Not checked</td><td>–</td><td>–</td></tr><tr><td>8<sup>(a)</sup></td><td>DE</td><td>1–2</td><td>49 max</td><td>2 ± 0.5</td><td>–</td><td>–</td><td>1.5–2</td><td>5.8</td><td>–</td><td>–</td></tr><tr><td>9<sup>(a)</sup></td><td>DE</td><td>4 to 6</td><td>35–42</td><td>–</td><td>1.5</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>10</td><td>IE</td><td>–</td><td>Customer specific, typically 14–46</td><td>0–2</td><td>–</td><td>&lt; 4°C start, 2°C within 24 h, thereafter &lt; 2°C</td><td>–</td><td>Checked pre-boning</td><td>5.7</td><td>–</td></tr><tr><td>11<sup>(a)</sup></td><td>DE</td><td>2</td><td>14–21</td><td>0–4</td><td>–</td><td>0–4</td><td>–</td><td>Not measured</td><td>–</td><td>–</td></tr><tr><td>12</td><td>BE</td><td>2</td><td>7–14</td><td>2</td><td>2 ± 1</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>13</td><td>BE</td><td>2</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>14</td><td>FR</td><td>1–3</td><td>28</td><td>4</td><td>–</td><td>Not measured</td><td>–</td><td>Not measured</td><td>–</td><td>–</td></tr><tr><td>Belgian study</td><td>BE</td><td>1–21</td><td>5 to 21 but usually 7–14</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td colspan="11"><strong>Pork</strong></td></tr><tr><td>2</td><td>AT</td><td>3</td><td>min 3</td><td>0–3</td><td>room: 0–3°C</td><td>Start: 1–4<br/>End: 0–3</td><td>–</td><td>–</td><td>5.4–5.8</td><td>–</td></tr><tr><td>9</td><td>DE</td><td>4–6</td><td>14–18</td><td>1.5</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>13</td><td>BE</td><td>2</td><td>18</td><td>0–4</td><td>–</td><td>–</td><td>–</td><td>Not routinely checked</td><td>–</td><td>–</td></tr><tr><td>14</td><td>FR</td><td>0.5–1</td><td>6</td><td>4</td><td>–</td><td>Not measured</td><td>Not measured</td><td>Not measured</td><td>–</td><td>–</td></tr></tbody></table>
<table><thead><tr><th>Respondents</th><th>EU/ MS</th><th>Time between slaughter and ageing (d)</th><th>Duration (d)</th><th>Specified temperature (°C)</th><th>Chill room temperature (°C)</th><th>Minimum – maximum temperature (°C)</th><th>Mean meat temperature (°C)</th><th>Meat pH</th><th>Start pH</th><th>End pH</th></tr></thead><tbody><tr><td colspan="11"><strong>Lamb</strong></td></tr><tr><td>2</td><td>ES</td><td>2</td><td>30</td><td>&lt; 5<br>optimum 2</td><td>–</td><td>–</td><td>2–5</td><td>–</td><td>–</td><td>–</td></tr><tr><td>10</td><td>IE</td><td>–</td><td>Customer specific,<br>typically 7–21</td><td>0–2</td><td>–</td><td>–</td><td>Start: 4°C Within<br>24 h: &lt; 2°C<br>After &lt; 2°C</td><td>–</td><td>–</td><td>–</td></tr><tr><td>12</td><td>BE</td><td>–</td><td>70–77</td><td>–1 to 2°C</td><td>2°C ± 1°C</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>

- : not provided.

(a): Replies to the questionnaire provided by the hearing expert.
<figure><img src="image_53.png" /></figure>

<table><thead><tr><th>Respondents</th><th>EU/ MS</th><th>Time between slaughter and ageing (d)</th><th>Duration (d)</th><th>Specified temperature (°C)</th><th>Chill room temperature (°C)</th><th>Minimum – maximum temperature (°C)</th><th>Mean meat temperature (°C)</th><th>Meat pH</th><th>Start pH</th><th>End pH</th></tr></thead><tbody><tr><td colspan="11"><strong>Lamb</strong></td></tr><tr><td>2</td><td>ES</td><td>2</td><td>30</td><td>&lt; 5<br>optimum 2</td><td>—</td><td>—</td><td>2–5</td><td>—</td><td>—</td><td>—</td></tr><tr><td>10</td><td>IE</td><td>—</td><td>Customer specific,<br>typically 7–21</td><td>0–2</td><td>—</td><td>—</td><td>Start: 4°C Within<br>24 h: &lt; 2°C<br>After &lt; 2°C</td><td>—</td><td>—</td><td>—</td></tr><tr><td>12</td><td>BE</td><td>—</td><td>70–77</td><td>–1 to 2°C</td><td>2°C ± 1°C</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

- : not provided.

(a): Replies to the questionnaire provided by the hearing expert.
# Appendix B – Uncertainty analysis

An expert knowledge elicitation (EKE) was undertaken on the 8th of November, with the 5 members of the Aged Meat WG serving as experts. To prepare for this EKE, individual judgements were elicited for the correctness of 5 statements. Informed by the evidence collected in the draft opinion, including the uncertainty Table B.1, individual experts were asked to indicate how certain they were that the statements were correct. For expression of the uncertainty, they could use the standard ranges indicated in EFSA's approximate probability scale or any other probability range. Consensus was achieved during discussion in the WG meeting.

The 5 statements and the outcome of the uncertainty assessments were as follows:

**ToR2:**

1) 'A meat surface temperature of -0.5 to 3.0°C, with a RH of 75 to 85% and an airflow of 0.2-0.5 m/s will prevent mycotoxin production for at least 35 days.'

Certainty: 66-90%.

Comments: The experts mentioned the fact that this statement is based on research published in the 1970s (Ciegler and Kurtzman, 1970; Kurtzman and Ciegler, 1970; Buchanan et al., 1974; Sommer et al., 1974) (which report that mycotoxins can be produced at very low concentrations and very slowly by Penicillium spp. at temperatures as low as 0-1°C on fruits, corn and other crops) and the review published by the Meat and Livestock Australia (MLA) in 2019. The latter relies on 4 references. Three of these describe the production of mycotoxins in different foods (ICMSF, 1996; Hocking and Pitt, 2003; Pitt and Hocking, 2009) and conclude that Penicillium and Aspergillus spp. are incapable of producing mycotoxins at temperatures between -0.5 and 3°C. The fourth (Olivier, 2018) states that there was no evidence that moulds found on red meat are capable of producing mycotoxins at temperatures between -0.5 and 3°C and a RH of 75-85%.

**ToR4:**

2) 'Dry-ageing of beef for 35 days at 3°C will not result in a higher log increase in the concentration of *L. monocytogenes* than an assumed 2-log10 increase in standard fresh beef.'

Certainty: 80-95%.

Comments: The assessment of the pathogen behaviour was based on the application of predictive models for growth rate. Lag time, competition with other bacteria or inactivation were not considered to quantify the log increase. This conservative approach resulted in an overestimation of the predicted log increase. The availability of experimental data showing the behaviour of *L. monocytogenes* on the surface of beef during dry-ageing is limited to a single study. This study suggests that *L. monocytogenes* will decrease during dry-ageing of beef, probably as a result of the decrease in $a_w$. In comparison, in only one of two replicates in one of the combinations (i.e., at 2°C, 85% RH, pH 6.21) of the factorial design (representing two meat types, temperatures and RH) an increase in *L. monocytogenes* was observed and this was limited to 1 log at day 42.

3) 'Wet-ageing of beef for 35 days at 2°C will not result a higher log increase in the concentration of *L. monocytogenes* than an assumed 2-log10 increase in standard fresh beef.'

Certainty: 66-90%.

Comments: As for dry-ageing, the assessment of the pathogen behaviour was based on the application of predictive models of growth rate. Lag time, competition with other bacteria or inactivation were not considered to quantify the log increase. This conservative approach resulted in an overestimation of the predicted log increase.

There are several studies showing that *L. monocytogenes* can grow during the refrigerated storage of vacuum packaged beef. Several studies report no growth of *L. monocytogenes* but the inactivation extent was not as important as for dry-ageing (because there is no reduction of the surface $a_w$). It was considered that the degree of overestimation was lower than for dry-ageing.
# Appendix B – Uncertainty analysis

An expert knowledge elicitation (EKE) was undertaken on the 8th of November, with the 5 members of the Aged Meat WG serving as experts. To prepare for this EKE, individual judgements were elicited for the correctness of 5 statements. Informed by the evidence collected in the draft opinion, including the uncertainty Table B.1, individual experts were asked to indicate how certain they were that the statements were correct. For expression of the uncertainty, they could use the standard ranges indicated in EFSA's approximate probability scale or any other probability range. Consensus was achieved during discussion in the WG meeting.

The 5 statements and the outcome of the uncertainty assessments were as follows:

**ToR2:**

1) 'A meat surface temperature of -0.5 to 3.0°C, with a RH of 75 to 85% and an airflow of 0.2-0.5 m/s will prevent mycotoxin production for at least 35 days.'

Certainty: 66-90%.

Comments: The experts mentioned the fact that this statement is based on research published in the 1970s (Ciegler and Kurtzman, 1970; Kurtzman and Ciegler, 1970; Buchanan et al., 1974; Sommer et al., 1974) (which report that mycotoxins can be produced at very low concentrations and very slowly by Penicillium spp. at temperatures as low as 0-1°C on fruits, corn and other crops) and the review published by the Meat and Livestock Australia (MLA) in 2019. The latter relies on 4 references. Three of these describe the production of mycotoxins in different foods (ICMSF, 1996; Hocking and Pitt, 2003; Pitt and Hocking, 2009) and conclude that Penicillium and Aspergillus spp. are incapable of producing mycotoxins at temperatures between -0.5 and 3°C. The fourth (Olivier, 2018) states that there was no evidence that moulds found on red meat are capable of producing mycotoxins at temperatures between -0.5 and 3°C and a RH of 75-85%.

**ToR4:**

2) 'Dry-ageing of beef for 35 days at 3°C will not result in a higher log increase in the concentration of *L. monocytogenes* than an assumed 2-log10 increase in standard fresh beef.'

Certainty: 80-95%.

Comments: The assessment of the pathogen behaviour was based on the application of predictive models for growth rate. Lag time, competition with other bacteria or inactivation were not considered to quantify the log increase. This conservative approach resulted in an overestimation of the predicted log increase. The availability of experimental data showing the behaviour of *L. monocytogenes* on the surface of beef during dry-ageing is limited to a single study. This study suggests that *L. monocytogenes* will decrease during dry-ageing of beef, probably as a result of the decrease in $a_w$. In comparison, in only one of two replicates in one of the combinations (i.e., at 2°C, 85% RH, pH 6.21) of the factorial design (representing two meat types, temperatures and RH) an increase in *L. monocytogenes* was observed and this was limited to 1 log at day 42.

3) 'Wet-ageing of beef for 35 days at 2°C will not result a higher log increase in the concentration of *L. monocytogenes* than an assumed 2-log10 increase in standard fresh beef.'

Certainty: 66-90%.

Comments: As for dry-ageing, the assessment of the pathogen behaviour was based on the application of predictive models of growth rate. Lag time, competition with other bacteria or inactivation were not considered to quantify the log increase. This conservative approach resulted in an overestimation of the predicted log increase.

There are several studies showing that *L. monocytogenes* can grow during the refrigerated storage of vacuum packaged beef. Several studies report no growth of *L. monocytogenes* but the inactivation extent was not as important as for dry-ageing (because there is no reduction of the surface $a_w$). It was considered that the degree of overestimation was lower than for dry-ageing.
4) 'Wet-ageing of pork for 10 days at 3°C will not result a higher log increase in the concentration of *L. monocytogenes* than an assumed 1-log10 increase in standard fresh pork.'

Certainty: 66-90%.

Comments: As for statement 3 above.

5) 'Wet-ageing of lamb for 10 days at 3°C will not result a higher log increase in the concentration of *L. monocytogenes* than an assumed 1-log10 increase in standard fresh lamb.'

Certainty: 66-90%.

Comments: As for statement 3 above.

<table><caption>**Table B.1:** Qualitative assessment included the 'sources or location of the uncertainty', a description of the 'nature or cause of the uncertainty' associated with that source and a description of the Impact of the uncertainty on the conclusions</caption><thead><tr><td>Source or location of the uncertainty</td><td>Nature or cause of the uncertainty</td><td>Impact of the uncertainty on the conclusions (e.g. over/ underestimation)</td></tr></thead><tbody><tr><td>Defining 'standard fresh meat' versus 'dry-aged meat' and 'wet-aged meat'</td><td>By definition, fresh meat is meat that has not undergone any preserving process other than chilling, freezing or quick-freezing. The majority of fresh beef, pork and lamb is matured in vacuum packaging under chilled conditions.<br/>Wet-aged meat is meat that has been vacuum packaged and stored under chilled conditions.<br/>There is no scientific, commercial or legislative basis for differentiating between the 'standard fresh meat' and 'wet-aged meat'. Thus for the purposes of this Opinion differentiation is based solely on the time in chilled storage.</td><td>A misinterpretation or misunderstanding of what constitutes, 'standard fresh meat' versus 'wet-aged meat' could result in an over or underestimation of the predicted growth of pathogenic and/or spoilage bacteria in ToR3, negate the 'equivalence calculations' in ToR4 and render any additional GHPs or CCPs in ToR5 redundant.</td></tr><tr><td>Inaccurate, incorrect or incomplete information about current practices used by FBOs for dry-ageing and wet-ageing beef, pork and/or lamb in the scientific and grey literature. Information for wet-aged pork and lamb was especially lacking and limited to 3 studies, none of which reported a<sub>w</sub> values. As none of these were observational studies, the reported values may not be representative.</td><td>The scientific literature reports the time, temperature, RH and/or air flow used in experiments and these are selected based on the objective(s) of that study. Thus, these conditions may or may not reflect those used in commercial operations. Moreover, many of the scientific and technical reports are from countries outside of the EU and the conditions used may not be representative to practices within the EU.</td><td>This lack of information on commercial practices reduces the certainty that the methods and conditions described in the response to ToR1 are an accurate description of current commercial practices in the EU. If parameters such as surface temperature, pH and aw are incorrect there is uncertainty about the outcomes of the predictive modelling in ToRs 3 and 4.</td></tr><tr><td>The limited information about current practices provided by commercial FBOs involved in dry-ageing beef or wet-ageing beef, pork or lamb in response to the questionnaire.</td><td>As this information was only provided by 8 FBOs and 2 FBO associations data were limited (for example, they do not routinely monitor and record surface temperature, pH and a<sub>w</sub>) and from a limited number of countries it may not be accurate or a reasonable representative of the entirety of European or regional practices.</td><td>An underestimation of the temperatures used for modelling could, for example, result in an underestimation of the growth rate of pathogens/spoilage bacteria. However this has been mitigated to some extent by using a range of temperatures.</td></tr></tbody></table>
4) 'Wet-ageing of pork for 10 days at 3°C will not result a higher log increase in the concentration of *L. monocytogenes* than an assumed 1-log10 increase in standard fresh pork.'

Certainty: 66-90%.

Comments: As for statement 3 above.

5) 'Wet-ageing of lamb for 10 days at 3°C will not result a higher log increase in the concentration of *L. monocytogenes* than an assumed 1-log10 increase in standard fresh lamb.'

Certainty: 66-90%.

Comments: As for statement 3 above.

<table><caption>**Table B.1:** Qualitative assessment included the 'sources or location of the uncertainty', a description of the 'nature or cause of the uncertainty' associated with that source and a description of the Impact of the uncertainty on the conclusions</caption><thead><tr><td>Source or location of the uncertainty</td><td>Nature or cause of the uncertainty</td><td>Impact of the uncertainty on the conclusions (e.g. over/ underestimation)</td></tr></thead><tbody><tr><td>Defining 'standard fresh meat' versus 'dry-aged meat' and 'wet-aged meat'</td><td>By definition, fresh meat is meat that has not undergone any preserving process other than chilling, freezing or quick-freezing. The majority of fresh beef, pork and lamb is matured in vacuum packaging under chilled conditions.<br/>Wet-aged meat is meat that has been vacuum packaged and stored under chilled conditions.<br/>There is no scientific, commercial or legislative basis for differentiating between the 'standard fresh meat' and 'wet-aged meat'. Thus for the purposes of this Opinion differentiation is based solely on the time in chilled storage.</td><td>A misinterpretation or misunderstanding of what constitutes, 'standard fresh meat' versus 'wet-aged meat' could result in an over or underestimation of the predicted growth of pathogenic and/or spoilage bacteria in ToR3, negate the 'equivalence calculations' in ToR4 and render any additional GHPs or CCPs in ToR5 redundant.</td></tr><tr><td>Inaccurate, incorrect or incomplete information about current practices used by FBOs for dry-ageing and wet-ageing beef, pork and/or lamb in the scientific and grey literature. Information for wet-aged pork and lamb was especially lacking and limited to 3 studies, none of which reported a<sub>w</sub> values. As none of these were observational studies, the reported values may not be representative.</td><td>The scientific literature reports the time, temperature, RH and/or air flow used in experiments and these are selected based on the objective(s) of that study. Thus, these conditions may or may not reflect those used in commercial operations. Moreover, many of the scientific and technical reports are from countries outside of the EU and the conditions used may not be representative to practices within the EU.</td><td>This lack of information on commercial practices reduces the certainty that the methods and conditions described in the response to ToR1 are an accurate description of current commercial practices in the EU. If parameters such as surface temperature, pH and aw are incorrect there is uncertainty about the outcomes of the predictive modelling in ToRs 3 and 4.</td></tr><tr><td>The limited information about current practices provided by commercial FBOs involved in dry-ageing beef or wet-ageing beef, pork or lamb in response to the questionnaire.</td><td>As this information was only provided by 8 FBOs and 2 FBO associations data were limited (for example, they do not routinely monitor and record surface temperature, pH and a<sub>w</sub>) and from a limited number of countries it may not be accurate or a reasonable representative of the entirety of European or regional practices.</td><td>An underestimation of the temperatures used for modelling could, for example, result in an underestimation of the growth rate of pathogens/spoilage bacteria. However this has been mitigated to some extent by using a range of temperatures.</td></tr></tbody></table>
<table><thead><tr><td>Source or location of the uncertainty</td><td>Nature or cause of the uncertainty</td><td>Impact of the uncertainty on the conclusions (e.g. over/ underestimation)</td></tr></thead><tbody><tr><td></td><td>Moreover, for the dry-ageing of beef, tradition and personal preference for specific organoleptic qualities in the final product are more important than strictly following a pre-defined process. Thus, there may be differences in the processes used for different batches.</td><td></td></tr><tr><td>Information on pathogenic and spoilage bacteria that may be on dry-aged and wet-aged meat and how these bacteria will behave in minced meat or MSM prepared the aged meat.</td><td>Although several studies have reported and reviewed the prevalence of pathogenic and spoilage bacteria in beef, pork and lamb relatively few have specifically studied dry or wet-aged meat.</td><td>Specific pathogenic or spoilage bacteria may have been erroneously excluded from the answer to ToR2. However, this would have a minimal impact on the conclusions as the range of bacteria including those capable of growth under the conditions encountered were considered.</td></tr><tr><td>Information on the hygiene status of the carcass and the time between the end of carcass chilling and the preparation of meat cuts for dry and wet-ageing.</td><td>Few, if any, studies provide this information. Based on the available data, the pre-ageing time (between slaughter and ageing) may vary considerably and counts at the start of ageing may be highly variable.</td><td>The initial microbial counts at the commencement of dry or wet-ageing may be higher or lower than reported in the few available studies cited in answering ToR2. However, as the assessment deals with log changes this will not impact on the outputs of the simulations using predictive models in ToRs 3 and 4.</td></tr><tr><td>The impact of competing microbiota on the predicted growth of L. monocytogenes, Y. enterocolitica and non-proteolytic Clostridium spp.</td><td>The effect of competing microorganisms was not included in the predictive models used for the main assessment, though the impact has been assessed in the framework of the effect of factors on the predictions focusing on the interaction between LAB and L. monocytogenes</td><td>This uncertainty has been quantified in the uncertainty analysis and in general results in an overestimation (competing microbiota contributes to inhibit and/or stop pathogen growth). A possible exception is Pseudomonas spp. as some articles report the enhancement of L. monocytogenes growth in presence of pseudomonades. However, others have reported a suppression of L. monocytogenes maximum population density by Pseudomonas at low temperature (4°C, Buchanan and Bagi, 1999).</td></tr><tr><td>Determining if mycotoxins are produced by moulds during the dry-ageing of beef</td><td>The specific conditions under which moulds, specifically Penicillium and Aspergillus spp. will not produce these toxins are based on a limited number (4) of studies, 3 of which focused on plant based foods. These conditions are not defined for meat and/or no specific studies have been undertaken during the dry-ageing of beef.</td><td>It is not possible to definitively state whether or not mycotoxins are produced during the dry-ageing of beef, even when this process is well described, although our current knowledge would suggest mycotoxin production is inhibited at temperatures below 3°C and at the a<sub>w</sub> values encountered.</td></tr><tr><td>Simulation of microbial growth - lag phase</td><td>No lag phase was considered for any relevant microorganism assessed for the main assessment.</td><td>An overestimation of the potential growth can occur when no lag phase is included (conservative approach). However, if contamination is already present at slaughter, microorganisms can adapt to the environmental conditions during the pre-ageing time. In this case, the impact of this uncertainty in ToR3 and ToR4 is considered to be limited/low.</td></tr></tbody></table>
<table><thead><tr><td>Source or location of the uncertainty</td><td>Nature or cause of the uncertainty</td><td>Impact of the uncertainty on the conclusions (e.g. over/ underestimation)</td></tr></thead><tbody><tr><td></td><td>Moreover, for the dry-ageing of beef, tradition and personal preference for specific organoleptic qualities in the final product are more important than strictly following a pre-defined process. Thus, there may be differences in the processes used for different batches.</td><td></td></tr><tr><td>Information on pathogenic and spoilage bacteria that may be on dry-aged and wet-aged meat and how these bacteria will behave in minced meat or MSM prepared the aged meat.</td><td>Although several studies have reported and reviewed the prevalence of pathogenic and spoilage bacteria in beef, pork and lamb relatively few have specifically studied dry or wet-aged meat.</td><td>Specific pathogenic or spoilage bacteria may have been erroneously excluded from the answer to ToR2. However, this would have a minimal impact on the conclusions as the range of bacteria including those capable of growth under the conditions encountered were considered.</td></tr><tr><td>Information on the hygiene status of the carcass and the time between the end of carcass chilling and the preparation of meat cuts for dry and wet-ageing.</td><td>Few, if any, studies provide this information. Based on the available data, the pre-ageing time (between slaughter and ageing) may vary considerably and counts at the start of ageing may be highly variable.</td><td>The initial microbial counts at the commencement of dry or wet-ageing may be higher or lower than reported in the few available studies cited in answering ToR2. However, as the assessment deals with log changes this will not impact on the outputs of the simulations using predictive models in ToRs 3 and 4.</td></tr><tr><td>The impact of competing microbiota on the predicted growth of L. monocytogenes, Y. enterocolitica and non-proteolytic Clostridium spp.</td><td>The effect of competing microorganisms was not included in the predictive models used for the main assessment, though the impact has been assessed in the framework of the effect of factors on the predictions focusing on the interaction between LAB and L. monocytogenes</td><td>This uncertainty has been quantified in the uncertainty analysis and in general results in an overestimation (competing microbiota contributes to inhibit and/or stop pathogen growth). A possible exception is Pseudomonas spp. as some articles report the enhancement of L. monocytogenes growth in presence of pseudomonades. However, others have reported a suppression of L. monocytogenes maximum population density by Pseudomonas at low temperature (4°C, Buchanan and Bagi, 1999).</td></tr><tr><td>Determining if mycotoxins are produced by moulds during the dry-ageing of beef</td><td>The specific conditions under which moulds, specifically Penicillium and Aspergillus spp. will not produce these toxins are based on a limited number (4) of studies, 3 of which focused on plant based foods. These conditions are not defined for meat and/or no specific studies have been undertaken during the dry-ageing of beef.</td><td>It is not possible to definitively state whether or not mycotoxins are produced during the dry-ageing of beef, even when this process is well described, although our current knowledge would suggest mycotoxin production is inhibited at temperatures below 3°C and at the a<sub>w</sub> values encountered.</td></tr><tr><td>Simulation of microbial growth - lag phase</td><td>No lag phase was considered for any relevant microorganism assessed for the main assessment.</td><td>An overestimation of the potential growth can occur when no lag phase is included (conservative approach). However, if contamination is already present at slaughter, microorganisms can adapt to the environmental conditions during the pre-ageing time. In this case, the impact of this uncertainty in ToR3 and ToR4 is considered to be limited/low.</td></tr></tbody></table>
<table><thead><tr><td>Source or location of the uncertainty</td><td>Nature or cause of the uncertainty</td><td>Impact of the uncertainty on the conclusions (e.g. over/ underestimation)</td></tr></thead><tbody><tr><td>Simulation of microbial growth - inactivation</td><td>Only growth rate models were used in the assessment. No inactivation was considered for any relevant microorganism assessed for the main assessment, though the impact of this factor has been assessed in the framework of uncertainty analysis (Section 3.4.4.1).</td><td>An overestimation of the growth can occur when no inactivation is included (conservative approach). This is especially relevant during dry-ageing and the impact was evaluated and considered in the response to ToR4.</td></tr><tr><td>Simulation of microbial growth - calibration factors for predictive models</td><td>Observed growth rates from the collected experiments were estimated by fitting the primary growth model to data from challenge test (pathogens) or naturally contaminated means (spoilage). Depending on the design of the experiment and the available data the model fitting will be more or less robust. Predicted growth rates had to be obtained by assuming the input value for aw and for endogenous lactic acid concentration (if considered). For vacuum-packaged meat, several scientific articles report no growth at all of L. monocytogenes. The growth model will represent the worst-case scenario For dry-ageing, the calibration factors have been obtained comparing growth data at high aw. The same behaviour was assumed for the entire range of aw occurring during dry-ageing.</td><td>Over- or underestimation of the Bf can over- or underestimate growth predictions in ToR3 and ToR4.</td></tr><tr><td>The additional actions required in the PRP and HACCP programmes to assure the food safety of dry-aged beef.</td><td>There is a lack of information on the specific GHPs currently used in the meat ageing processes as these are rarely studied and seldom reported.</td><td>There may be additional GHPs or CCPs used in FBOs that are not reported and therefore unknown outside of those specific food businesses. Thus the response to ToR5 may be inadequate.</td></tr></tbody></table>
<table><thead><tr><td>Source or location of the uncertainty</td><td>Nature or cause of the uncertainty</td><td>Impact of the uncertainty on the conclusions (e.g. over/ underestimation)</td></tr></thead><tbody><tr><td>Simulation of microbial growth - inactivation</td><td>Only growth rate models were used in the assessment. No inactivation was considered for any relevant microorganism assessed for the main assessment, though the impact of this factor has been assessed in the framework of uncertainty analysis (Section 3.4.4.1).</td><td>An overestimation of the growth can occur when no inactivation is included (conservative approach). This is especially relevant during dry-ageing and the impact was evaluated and considered in the response to ToR4.</td></tr><tr><td>Simulation of microbial growth - calibration factors for predictive models</td><td>Observed growth rates from the collected experiments were estimated by fitting the primary growth model to data from challenge test (pathogens) or naturally contaminated means (spoilage). Depending on the design of the experiment and the available data the model fitting will be more or less robust. Predicted growth rates had to be obtained by assuming the input value for aw and for endogenous lactic acid concentration (if considered). For vacuum-packaged meat, several scientific articles report no growth at all of L. monocytogenes. The growth model will represent the worst-case scenario For dry-ageing, the calibration factors have been obtained comparing growth data at high aw. The same behaviour was assumed for the entire range of aw occurring during dry-ageing.</td><td>Over- or underestimation of the Bf can over- or underestimate growth predictions in ToR3 and ToR4.</td></tr><tr><td>The additional actions required in the PRP and HACCP programmes to assure the food safety of dry-aged beef.</td><td>There is a lack of information on the specific GHPs currently used in the meat ageing processes as these are rarely studied and seldom reported.</td><td>There may be additional GHPs or CCPs used in FBOs that are not reported and therefore unknown outside of those specific food businesses. Thus the response to ToR5 may be inadequate.</td></tr></tbody></table>
# Appendix C – Calculation of calibration factor for the correction of predictive models used to simulate microbial growth in raw meat

## C.1. Predictive model for *Listeria monocytogenes*

To simulate the growth of *L. monocytogenes* in raw meat during wet and dry-ageing, the growth rate (μmax) model of Mejlholm and Dalgaard (Mejlholm and Dalgaard, 2009) was used. The model is part of the user-friendly tool Food Spoilage and Safety Predictor (FSSP version v4.0, freeware available at http://fssp.food.dtu.dk/). The model was originally developed for processed and ready-to-eat seafood including 12 environmental factors and its interaction factor.<fnref n="4" /> Its predictive performance was assessed in an international validation study focussed on processed and ready-to-eat meat and seafood products, by comparing the growth rate predicted by the model with that observed from 640 growth curves for *L. monocytogenes* in different types of processed meat, seafood, poultry and dairy products, showing satisfactory results for meat, poultry and non-fermented dairy products with a bias factor (Bf = 1, indicating that on average there was no systematic over- nor underestimation of growth rates<fnref n="5" />) and accuracy factor (Af = 1.5, as a measure of the average differences between observed and predicted μmax values) (Mejlholm et al., 2010).

However, the validation study was not focussed on raw meat (not processed). Further, the model does not consider anaerobic conditions as an input factor, and the differences between aerobic (dry-ageing) and anaerobic (vacuum packaged wet-ageing of vacuum packaged meat).

Therefore, the performance of this predictive model specifically for raw meat stored under aerobic conditions and vacuum-packaged was evaluated. Growth behaviour and environmental parameters regarding temperature, pH and aw were collected from experimental trials available in the ComBase Browser of the ComBase portal (www.combase.cc) and/or complemented with additional data from scientific literature. When directly reported the log counts were retrieved or digitalised from figures using the WebPlotDigitizer (v4.5) tool (Rohatgi, 2021). Unless provided by the authors, the growth rate was estimated by fitting the Baranyi and Roberts (1994) growth model using the DMFit tool available at the ComBase portal. A total of 66 growth rates (μmax) values in raw meat under aerobic conditions and 8 growth rates (μmax) values in vacuum-packaged raw meat (anaerobic conditions) were obtained, covering different *L. monocytogenes* strains and experimental conditions (inoculation, meat species/pieces, packaging materials, inoculation procedure, storage temperature, enumeration methods, etc.).

The observed μmax was compared with the μmax predicted by a simplified version of the predictive model quantifying the effect of the temperature, pH, aw and undissociated lactic acid concentration (Eq. 1), thus ignoring the other environmental input factors not relevant for raw meat.

$$ \mu_{\max} = \mu_{\max_{ref}} \cdot \left( \frac{T + 2.83}{25 + 2.83} \right)^2 \cdot 1 - 10^{(4.97 - \text{pH})} \cdot \frac{a_w - 0.923}{1 - 0.923} \cdot 1 - \frac{LACu}{3.79} \quad (1) $$

where, μmax$_{ref}$ = 0.491; T is the reported temperature (°C) of the experiment, pH of the reported initial pH of the meat; aw is the initial water activity of the meat and LACu is the concentration of undissociated lactic acid in the water phase. In most cases, the aw of the meat was not reported by the authors and was assumed to be 0.996. Though, fresh raw meat is known to have a certain amount of lactic acid from endogenous origin, for the baseline predictions this input parameter was set at zero ppm. However, to quantify the possible impact of the uncertainty associated with these assumptions, the predicted μmax was also obtained considering aw = 0.980 and endogenous lactic acid concentration of 4,000 ppm in water phase.

The bias factor (Bf) and accuracy factor (Af) were calculated by comparing the predicted versus observed μmax according to Eq. 2 and Eq. 3, respectively (Baranyi et al., 1999)

<footnote n="4">⁴ The environmental factors (and the range of applicability) for the full model are: temperature (2–25°C), pH (5.6–7.7), aw (0.996–0.943; equivalent to 0.7–9.0% water phase salt, WPS), lactic acid (0–60,000 ppm in water phase, WP), atmosphere (0–100% CO₂), smoke components (phenol: 0–20 ppm), nitrite (0–150 ppm in product), acetic acid (0–11,000 ppm in WP), benzoic acid (0–1800 ppm in WP), citric acid (0–6,500 ppm in WP), diacetate (0–3,000 ppm in WP), and sorbic acid (0–1,300 in WP).</footnote>

<footnote n="5">⁵ To be considered good or acceptable, growth rates should not be over- or underpredicted by more than 43% and 13%, respectively, corresponding to a bias factor of between 1.43 and 0.87 (Ross, 1999; Mejlholm et al., 2010). Accuracy factors higher than 1.5 indicate poor model precision (Mejlholm and Dalgaard, 2013; Koukou et al., 2021).</footnote>
# Appendix C – Calculation of calibration factor for the correction of predictive models used to simulate microbial growth in raw meat

## C.1. Predictive model for *Listeria monocytogenes*

To simulate the growth of *L. monocytogenes* in raw meat during wet and dry-ageing, the growth rate (μmax) model of Mejlholm and Dalgaard (Mejlholm and Dalgaard, 2009) was used. The model is part of the user-friendly tool Food Spoilage and Safety Predictor (FSSP version v4.0, freeware available at http://fssp.food.dtu.dk/). The model was originally developed for processed and ready-to-eat seafood including 12 environmental factors and its interaction factor.<fnref n="4" /> Its predictive performance was assessed in an international validation study focussed on processed and ready-to-eat meat and seafood products, by comparing the growth rate predicted by the model with that observed from 640 growth curves for *L. monocytogenes* in different types of processed meat, seafood, poultry and dairy products, showing satisfactory results for meat, poultry and non-fermented dairy products with a bias factor (Bf = 1, indicating that on average there was no systematic over- nor underestimation of growth rates<fnref n="5" />) and accuracy factor (Af = 1.5, as a measure of the average differences between observed and predicted μmax values) (Mejlholm et al., 2010).

However, the validation study was not focussed on raw meat (not processed). Further, the model does not consider anaerobic conditions as an input factor, and the differences between aerobic (dry-ageing) and anaerobic (vacuum packaged wet-ageing of vacuum packaged meat).

Therefore, the performance of this predictive model specifically for raw meat stored under aerobic conditions and vacuum-packaged was evaluated. Growth behaviour and environmental parameters regarding temperature, pH and aw were collected from experimental trials available in the ComBase Browser of the ComBase portal (www.combase.cc) and/or complemented with additional data from scientific literature. When directly reported the log counts were retrieved or digitalised from figures using the WebPlotDigitizer (v4.5) tool (Rohatgi, 2021). Unless provided by the authors, the growth rate was estimated by fitting the Baranyi and Roberts (1994) growth model using the DMFit tool available at the ComBase portal. A total of 66 growth rates (μmax) values in raw meat under aerobic conditions and 8 growth rates (μmax) values in vacuum-packaged raw meat (anaerobic conditions) were obtained, covering different *L. monocytogenes* strains and experimental conditions (inoculation, meat species/pieces, packaging materials, inoculation procedure, storage temperature, enumeration methods, etc.).

The observed μmax was compared with the μmax predicted by a simplified version of the predictive model quantifying the effect of the temperature, pH, aw and undissociated lactic acid concentration (Eq. 1), thus ignoring the other environmental input factors not relevant for raw meat.

$$ \mu_{\max} = \mu_{\max_{ref}} \cdot \left( \frac{T + 2.83}{25 + 2.83} \right)^2 \cdot 1 - 10^{(4.97 - \text{pH})} \cdot \frac{a_w - 0.923}{1 - 0.923} \cdot 1 - \frac{LACu}{3.79} \quad (1) $$

where, μmax_{ref} = 0.491; T is the reported temperature (°C) of the experiment, pH of the reported initial pH of the meat; aw is the initial water activity of the meat and LACu is the concentration of undissociated lactic acid in the water phase. In most cases, the aw of the meat was not reported by the authors and was assumed to be 0.996. Though, fresh raw meat is known to have a certain amount of lactic acid from endogenous origin, for the baseline predictions this input parameter was set at zero ppm. However, to quantify the possible impact of the uncertainty associated with these assumptions, the predicted μmax was also obtained considering aw = 0.980 and endogenous lactic acid concentration of 4,000 ppm in water phase.

The bias factor (Bf) and accuracy factor (Af) were calculated by comparing the predicted versus observed μmax according to Eq. 2 and Eq. 3, respectively (Baranyi et al., 1999)

<footnote n="4">⁴ The environmental factors (and the range of applicability) for the full model are: temperature (2–25°C), pH (5.6–7.7), aw (0.996–0.943; equivalent to 0.7–9.0% water phase salt, WPS), lactic acid (0–60,000 ppm in water phase, WP), atmosphere (0–100% CO₂), smoke components (phenol: 0–20 ppm), nitrite (0–150 ppm in product), acetic acid (0–11,000 ppm in WP), benzoic acid (0–1800 ppm in WP), citric acid (0–6,500 ppm in WP), diacetate (0–3,000 ppm in WP), and sorbic acid (0–1,300 in WP).</footnote>

<footnote n="5">⁵ To be considered good or acceptable, growth rates should not be over- or underpredicted by more than 43% and 13%, respectively, corresponding to a bias factor of between 1.43 and 0.87 (Ross, 1999; Mejlholm et al., 2010). Accuracy factors higher than 1.5 indicate poor model precision (Mejlholm and Dalgaard, 2013; Koukou et al., 2021).</footnote>
$$B_f = \exp\left(\frac{\sum_{i=1}^{n} \frac{\text{Ln PRED}_{\mu_{\text{max}}} - \text{Ln OBS}_{\mu_{\text{max}}}}{n}\right) \quad (2)$$

$$A_f = \exp\left(\sqrt{\frac{\sum_{i=1}^{n} (\text{Ln PRED}_{\mu_{\text{max}}} - \text{Ln OBS}_{\mu_{\text{max}}})^2}{n}}\right) \quad (3)$$

The predictive model can be corrected by the matrix effect through the calculated B<sub>f</sub>, as the calibration factor dividing the μ<sub>max,ref</sub> to compensate the systematic bias of the model predictions with respect to the μ<sub>max</sub> observed in meat.

Figure C1 shows the comparison between the μ<sub>max</sub> observed for *L. monocytogenes* in raw meat under aerobic conditions (left, n = 66 data) and vacuum packaging conditions (right, n = 12 data) and the predictions provided by the model. Under aerobic conditions, the predictive model tended to underestimate the μ<sub>max</sub> with B<sub>f</sub> = 0.74, i.e. grey dots were systematically below the equivalence line by 26% on average. After the applying the correction to the μ<sub>max,ref</sub> the dots (in green) fall just on the equivalence line (no bias on average) and the A<sub>f</sub> improved to from 1.61 (before correction) to 1.43 (after correction). The calculations for B<sub>f</sub> and A<sub>f</sub> were performed taking into consideration (i) data from different temperature ranges and (ii) using different a<sub>w</sub> and lactic acid concentration as input factors. When data within temperature range 0–10°C was considered (n = 28) the resulting B<sub>f</sub> was 0.78. If a lower a<sub>w</sub> value (0.98) and an endogenous concentration of lactic acid of 4,000 ppm was used as input for the predictive model, the B<sub>f</sub> decreased to 0.44 (A<sub>f</sub> = 2.56).

For vacuum-packaged meat, much fewer data were available. Several studies reported no growth or even slight decrease of *L. monocytogenes* in vacuum packaged meat during the refrigerated storage (Mano et al., 1995; Zhang and Mustapha, 1999; Tsigarida et al., 2000; Dykes and Moorhead, 2002; Saraiva et al., 2016); Combase record ID: M802_LM, M157_3, M157_4, M157_11, M157_12. When growth of the pathogen was observed, on average the predictions matched the observed μ<sub>max</sub> values (B<sub>f</sub> = 0.94), though a poor A<sub>f</sub> = 1.63 was obtained.
$$B_f = \exp\left(\frac{\sum_{i=1}^{n} \frac{\text{Ln PRED}_{\mu_{\text{max}}} - \text{Ln OBS}_{\mu_{\text{max}}}}{n}\right) \quad (2)$$

$$A_f = \exp\left(\sqrt{\frac{\sum_{i=1}^{n} (\text{Ln PRED}_{\mu_{\text{max}}} - \text{Ln OBS}_{\mu_{\text{max}}})^2}{n}}\right) \quad (3)$$

The predictive model can be corrected by the matrix effect through the calculated B<sub>f</sub>, as the calibration factor dividing the μ<sub>max,ref</sub> to compensate the systematic bias of the model predictions with respect to the μ<sub>max</sub> observed in meat.

Figure C1 shows the comparison between the μ<sub>max</sub> observed for *L. monocytogenes* in raw meat under aerobic conditions (left, n = 66 data) and vacuum packaging conditions (right, n = 12 data) and the predictions provided by the model. Under aerobic conditions, the predictive model tended to underestimate the μ<sub>max</sub> with B<sub>f</sub> = 0.74, i.e. grey dots were systematically below the equivalence line by 26% on average. After the applying the correction to the μ<sub>max,ref</sub> the dots (in green) fall just on the equivalence line (no bias on average) and the A<sub>f</sub> improved to from 1.61 (before correction) to 1.43 (after correction). The calculations for B<sub>f</sub> and A<sub>f</sub> were performed taking into consideration (i) data from different temperature ranges and (ii) using different a<sub>w</sub> and lactic acid concentration as input factors. When data within temperature range 0–10°C was considered (n = 28) the resulting B<sub>f</sub> was 0.78. If a lower a<sub>w</sub> value (0.98) and an endogenous concentration of lactic acid of 4,000 ppm was used as input for the predictive model, the B<sub>f</sub> decreased to 0.44 (A<sub>f</sub> = 2.56).

For vacuum-packaged meat, much fewer data were available. Several studies reported no growth or even slight decrease of *L. monocytogenes* in vacuum packaged meat during the refrigerated storage (Mano et al., 1995; Zhang and Mustapha, 1999; Tsigarida et al., 2000; Dykes and Moorhead, 2002; Saraiva et al., 2016); Combase record ID: M802_LM, M157_3, M157_4, M157_11, M157_12. When growth of the pathogen was observed, on average the predictions matched the observed μ<sub>max</sub> values (B<sub>f</sub> = 0.94), though a poor A<sub>f</sub> = 1.63 was obtained.
<figure><img src="image_53.png" /></figure>

Data from: (Lee et al., 2014); (Wang et al., 2015); (Solomakos et al., 2008); (Giménez et al., 2021) ComBase records ID: M371_LM; M372_LM; M373_LM; M374_LM; M781_LM; M801_LM; M754_LM; M782_LM; M755_LM; M905_LM; M783_LM; M803_LM; M53_LM; M798_LM; M756_LM; M804_LM; M369_LM; M370_LM; M757_LM; M52_LM; M758_LM; M759_LM; M760_LM; M761_LM; M784_LM; M805_LM; M785_LM; M762_LM; M763_LM; M779_LM; M786_LM; M807_LM; M764_LM; M787_LM; M799_LM; M765_LM; M766_LM; M788_LM; M789_LM; M808_LM; M767_LM; M768_LM; M769_LM; M770_LM; M771_LM; M790_LM; M791_LM; M800_LM; M809_LM; M772_LM; M773_LM; M774_LM; M775_LM; M776_LM; M792_LM; M793_LM; M777_LM; M778_LM; M780_LM; M794_LM; M795_LM; Pawr_1.

**Figure C.1:** Comparison between the Ln transformed values of μmax observed in raw meat under aerobic conditions (top left) and vacuum packaging conditions (top right) and the predictions provided by the model of Mejlholm and Dalgaard (Mejlholm and Dalgaard, 2009). Red line represents the equivalence (i.e. predictions equal to observations). For aerobic conditions, grey dots represent the uncorrected predictions, while green points represent the corrected values after applying the calibration factor. Plots on the bottom show the observed μmax (grey) versus the predicted μmax (blue) as a function of the temperature of the experiment. The predictions for two different extreme pH values are also represented with the lines for an illustrative purpose

A calibration factor of 0.76 was used to correct the μmax<sub>ref</sub> of the predictive model (Eq. 1) to simulate the growth of *L. monocytogenes* in aerobically stored raw meat. This value corresponds to the average value of B<sub>f</sub> obtained for all range of temperatures (0.74) and up to 10°C (0.78). For the uncertainty assessment, a uniform distribution from 0.74 to 0.78 was used.

The simulations for anaerobic conditions (vacuum-packaged meat during wet-ageing) were done with the original predictive model without any correction, as the B<sub>f</sub> = 0.94 was considered satisfactory.
<figure><img src="image_54.png" /></figure>

Data from: (Lee et al., 2014); (Wang et al., 2015); (Solomakos et al., 2008); (Giménez et al., 2021) ComBase records ID: M371_LM; M372_LM; M373_LM; M374_LM; M781_LM; M801_LM; M754_LM; M782_LM; M755_LM; M905_LM; M783_LM; M803_LM; M53_LM; M798_LM; M756_LM; M804_LM; M369_LM; M370_LM; M757_LM; M52_LM; M758_LM; M759_LM; M760_LM; M761_LM; M784_LM; M805_LM; M785_LM; M762_LM; M763_LM; M779_LM; M786_LM; M807_LM; M764_LM; M787_LM; M799_LM; M765_LM; M766_LM; M788_LM; M789_LM; M808_LM; M767_LM; M768_LM; M769_LM; M770_LM; M771_LM; M790_LM; M791_LM; M800_LM; M809_LM; M772_LM; M773_LM; M774_LM; M775_LM; M776_LM; M792_LM; M793_LM; M777_LM; M778_LM; M780_LM; M794_LM; M795_LM; Pawr_1.

**Figure C.1:** Comparison between the Ln transformed values of μmax observed in raw meat under aerobic conditions (top left) and vacuum packaging conditions (top right) and the predictions provided by the model of Mejlholm and Dalgaard (Mejlholm and Dalgaard, 2009). Red line represents the equivalence (i.e. predictions equal to observations). For aerobic conditions, grey dots represent the uncorrected predictions, while green points represent the corrected values after applying the calibration factor. Plots on the bottom show the observed μmax (grey) versus the predicted μmax (blue) as a function of the temperature of the experiment. The predictions for two different extreme pH values are also represented with the lines for an illustrative purpose

A calibration factor of 0.76 was used to correct the μmax<sub>ref</sub> of the predictive model (Eq. 1) to simulate the growth of *L. monocytogenes* in aerobically stored raw meat. This value corresponds to the average value of B<sub>f</sub> obtained for all range of temperatures (0.74) and up to 10°C (0.78). For the uncertainty assessment, a uniform distribution from 0.74 to 0.78 was used.

The simulations for anaerobic conditions (vacuum-packaged meat during wet-ageing) were done with the original predictive model without any correction, as the B<sub>f</sub> = 0.94 was considered satisfactory.
## C.2. Predictive model for lactic acid bacteria

To simulate the growth of LAB in vacuum-packaged meat during wet-ageing, the growth rate ($\mu_{max}$) model of Mejlholm & Dalgaard (Mejlholm and Dalgaard, 2007, 2013) was used. The model is part of the user-friendly tool Food Spoilage and Safety Predictor (FSSP version v4.0, freeware available at http://fssp.food.dtu.dk/). The model was originally developed for processed and ready-to-eat seafood including 12 environmental factors and its interaction factor.<fnref n="6" /> Its predictive performance was assessed by comparing the $\mu_{max}$ predicted by the model with that observed from 320 growth curves for lactic acid bacteria in inoculated challenge tests or naturally contaminated seafood (112) and meat products (208), showing satisfactory results with a $B_f$ = 1.1 and $A_f$ = 1.3.

However, the validation study was not focussed on raw meat (not processed). Further, the model does not consider anaerobic conditions as an input factor.

Therefore, the performance of this predictive model specifically for vacuum-packaged meat was evaluated. Growth behaviour and environmental parameters regarding temperature, pH and $a_w$ were collected from experimental trials available in the ComBase Browser of the ComBase portal (www.combase.cc) and/or complemented with additional data from scientific literature. A total of 56 growth rates ($\mu_{max}$) values for naturally contaminating lactic acid bacteria on vacuum-packaged raw meat (anaerobic conditions) were obtained, covering different microbial strains and experimental conditions (meat species/pieces, meat source and level of initial contamination, packaging materials, storage temperature, enumeration methods, etc.).

The observed $\mu_{max}$ provided by the authors was used or, when not provided, it was estimated by explained in C.1 above. A simplified version of the predictive model of Mejlholm and Dalgaard (2007) quantifying the effect of the temperature, pH and $a_w$ (Eq. 4), thus ignoring the other environmental input factors not relevant for raw meat.

$$ \mu_{max} = \mu_{max_{ref}} \cdot \left( \frac{T + 5.25}{25 + 5.25} \right)^2 \cdot 1 - 10^{(4.24 - pH)} \cdot \frac{a_w - 0.928}{1 - 0.928} \cdot 1 - \frac{LACu}{12.0} \quad (4) $$

where, $\mu_{max_{ref}}$ = 0.583; T is the reported temperature (°C) of the experiment, pH of the reported initial pH of the meat; $a_w$ is the initial water activity of the meat and LACu is the concentration of undissociated lactic acid in the water phase. The baseline predictions amount of lactic acid from endogenous origin as input parameter was set at zero ppm. However, to quantify the possible impact of the uncertainty associated with these assumptions, the predicted $\mu_{max}$ was also obtained considering $a_w$ = 0.980 and endogenous lactic acid concentration of 4,000 ppm in water phase.

$B_f$ and $A_f$ were calculated by comparing the predicted versus observed $\mu_{max}$ as described in C.1. Figure C.2 shows the comparison between the $\mu_{max}$ observed in vacuum-packaged raw meat (n = 58 data) and the predictions provided by the model. The predictive model tended to overestimate the $\mu_{max}$ by 117% on average, with a $B_f$ = 2.17, i.e., grey dots were systematically above the equivalence line. After the applying the correction to the $\mu_{max_{ref}}$ the dots (in green) fall just on the equivalence line (no bias on average) and the $A_f$ improved to from 2.7 (before correction) to 1.85 (after correction). The calculated $B_f$ and $A_f$ taking into consideration (i) data from different temperature ranges and (ii) using different $a_w$ and lactic acid concentration as input factors ranged from 1.30 to 1.66.

<footnote n="6">⁶ The environmental factors (and the range of applicability) for the full model are: temperature (2–25°C), pH (5.3–7.7), aw (0.999–0.961; equivalent to 0.1–6.4% water phase salt, WPS), lactic acid (0–67,000 ppm in water phase, WP), atmosphere (0–100% CO₂), smoke components (phenol: 0–21.2 ppm), nitrite (0–209 ppm in product), acetic acid (0–12,600 ppm in WP), benzoic acid (0–1800 ppm in WP), citric acid (0–7,300 ppm in WP), diacetate (0–3,000 ppm in WP), and sorbic acid (0–1,300 in WP).</footnote>
## C.2. Predictive model for lactic acid bacteria

To simulate the growth of LAB in vacuum-packaged meat during wet-ageing, the growth rate (μmax) model of Mejlholm & Dalgaard (Mejlholm and Dalgaard, 2007, 2013) was used. The model is part of the user-friendly tool Food Spoilage and Safety Predictor (FSSP version v4.0, freeware available at http://fssp.food.dtu.dk/). The model was originally developed for processed and ready-to-eat seafood including 12 environmental factors and its interaction factor.<fnref n="6" /> Its predictive performance was assessed by comparing the μmax predicted by the model with that observed from 320 growth curves for lactic acid bacteria in inoculated challenge tests or naturally contaminated seafood (112) and meat products (208), showing satisfactory results with a Bf = 1.1 and Af = 1.3.

However, the validation study was not focussed on raw meat (not processed). Further, the model does not consider anaerobic conditions as an input factor.

Therefore, the performance of this predictive model specifically for vacuum-packaged meat was evaluated. Growth behaviour and environmental parameters regarding temperature, pH and aw were collected from experimental trials available in the ComBase Browser of the ComBase portal (www.combase.cc) and/or complemented with additional data from scientific literature. A total of 56 growth rates (μmax) values for naturally contaminating lactic acid bacteria on vacuum-packaged raw meat (anaerobic conditions) were obtained, covering different microbial strains and experimental conditions (meat species/pieces, meat source and level of initial contamination, packaging materials, storage temperature, enumeration methods, etc.).

The observed μmax provided by the authors was used or, when not provided, it was estimated by explained in C.1 above. A simplified version of the predictive model of Mejlholm and Dalgaard (2007) quantifying the effect of the temperature, pH and aw (Eq. 4), thus ignoring the other environmental input factors not relevant for raw meat.

$$ \mu_{\max} = \mu_{\max_{ref}} \cdot \left( \frac{T + 5.25}{25 + 5.25} \right)^2 \cdot 1 - 10^{(4.24 - pH)} \cdot \frac{a_w - 0.928}{1 - 0.928} \cdot 1 - \frac{LACu}{12.0} \quad (4) $$

where, μmax,ref = 0.583; T is the reported temperature (°C) of the experiment, pH of the reported initial pH of the meat; aw is the initial water activity of the meat and LACu is the concentration of undissociated lactic acid in the water phase. The baseline predictions amount of lactic acid from endogenous origin as input parameter was set at zero ppm. However, to quantify the possible impact of the uncertainty associated with these assumptions, the predicted μmax was also obtained considering aw = 0.980 and endogenous lactic acid concentration of 4,000 ppm in water phase.

Bf and Af were calculated by comparing the predicted versus observed μmax as described in C.1. Figure C.2 shows the comparison between the μmax observed in vacuum packaged raw meat (n = 58 data) and the predictions provided by the model. The predictive model tended to overestimate the μmax by 117% on average, with a Bf = 2.17, i.e., grey dots were systematically above the equivalence line. After the applying the correction to the μmax,ref the dots (in green) fall just on the equivalence line (no bias on average) and the Af improved to from 2.7 (before correction) to 1.85 (after correction). The calculated Bf and Af taking into consideration (i) data from different temperature ranges and (ii) using different aw and lactic acid concentration as input factors ranged from 1.30 to 1.66.

<footnote n="6">⁶ The environmental factors (and the range of applicability) for the full model are: temperature (2–25°C), pH (5.3–7.7), aw (0.999–0.961; equivalent to 0.1–6.4% water phase salt, WPS), lactic acid (0–67,000 ppm in water phase, WP), atmosphere (0–100% CO₂), smoke components (phenol: 0–21.2 ppm), nitrite (0–209 ppm in product), acetic acid (0–12,600 ppm in WP), benzoic acid (0–1800 ppm in WP), citric acid (0–7,300 ppm in WP), diacetate (0–3,000 ppm in WP), and sorbic acid (0–1,300 in WP).</footnote>
<figure><img src="image_54.png" /></figure>

Data from: (Barrera et al., 2007) Barrera et al. (2007); IRTA unpublished data; ComBase records ID: L-1MRSA1; L-1MRSA2; L-1MRSAn1; L-1MRSAn2; L2MRSA1; L2MRSA2; L2MRSAn1; L2MRSAn2; L7MRSA1; L7MRSA2; L7MRSAn1; L7MRSAn2; L_LAB0_1; L_LAB0_2; L_LAB-1.5_1; L_LAB2_1; L_LAB2_2; L_LAB4_1; L_LAB4_2; L_LAB7_1; S_LAB0_1; S_LAB-1.5_1; S_LAB2_1; S_LAB2_2; S_LAB4_1; S_LAB7_1; S_LAB7_2; CB01k_1_12; CB02k_1_12; CB03k_1_12; CB04k_1_12; CB05k_1_12; CB05k_2_12; CB05k_3_12; CB06k_1_12; CB06k_2_12; CB06k_3_12; CB07k_1_12; CB07k_2_12; CB07k_3_12; CB08k_1_12; CB08k_2_12; CB08k_3_12.

**Figure C.2:** Comparison between the Ln transformed values of μmax observed for lactic acid bacteria in vacuum packaged raw meat and the predictions provided by the model of Mejlholm & Dalgaard (Mejlholm and Dalgaard, 2007, 2013). In the plot on the left, red line represents the equivalence (i.e., predictions equal to observations). Grey dots represent the uncorrected predictions, while green points represent the corrected values after applying the calibration factor. Plot on the right show the observed μmax (grey) versus the predicted μmax (blue) as a function of the temperature of the experiment. The predictions for two different extreme pH values are also represented with the lines for an illustrative purpose

A calibration factor of 1.89 was used to correct the μmax, of the predictive model (Eq. 4) to simulate the growth of lactic acid bacteria in vacuum-packaged meat. This value corresponds to the Bf value obtained for temperatures up to 10°C. For the uncertainty assessment, a uniform distribution from 1.30 to 2.17 was used.

### C.3. Predictive model for pseudomonas

To simulate the growth of pseudomonas in meat under aerobic conditions during dry-ageing, the growth rate (μmax) model of Neumeyer et al. (1997) was selected as it accounts for the effect of storage temperature and aw. The model, which is also available in ComBase Premium, was developed from experiments in laboratory growth media and for chilled beef stored between 0 to 20°C, including temperature as the sole environmental factors (Eq. 5).

$$ \sqrt{\mu_{\max}} = 0.1539 \cdot (T + 7.6) \cdot \sqrt{(a_w - 0.947)} \quad (5) $$

The performance of this predictive model for aerobically stored meat was evaluated. Growth behaviour and storage temperature were collected from experimental trials available in the ComBase Browser of the ComBase portal (www.combase.cc) and/or complemented with additional data from scientific literature. A total of 80 growth rates (μmax) values for naturally contaminating pseudomonas on meat (aerobic conditions) were obtained, covering different microbial strains and experimental conditions (meat species/pieces, meat source and level of initial contamination, packaging materials, storage temperature, enumeration methods, etc.).

Bf and Af were calculated by comparing the predicted versus observed μmax as described in C.1. Figure C.3 shows the comparison between the μmax observed in meat and the predictions provided by the model. The predictive model overestimated (80%) the μmax with a Bf = 1.80, i.e., grey dots were systematically almost above the equivalence line. Moreover, the Af was 1.95 was reduced to 1.37 after the correction, indicating an acceptable precision of the predictive model.
<figure><img src="image_55.png" /></figure>

Data from: (Barrera et al., 2007) Barrera et al. (2007); IRTA unpublished data; ComBase records ID: L-1MRSA1; L-1MRSA2; L-1MRSAn1; L-1MRSAn2; L2MRSA1; L2MRSA2; L2MRSAn1; L2MRSAn2; L7MRSA1; L7MRSA2; L7MRSAn1; L7MRSAn2; L_LAB0_1; L_LAB0_2; L_LAB-1.5_1; L_LAB2_1; L_LAB2_2; L_LAB4_1; L_LAB4_2; L_LAB7_1; S_LAB0_1; S_LAB-1.5_1; S_LAB2_1; S_LAB2_2; S_LAB4_1; S_LAB7_1; S_LAB7_2; CB01k_1_12; CB02k_1_12; CB03k_1_12; CB04k_1_12; CB05k_1_12; CB05k_2_12; CB05k_3_12; CB06k_1_12; CB06k_2_12; CB06k_3_12; CB07k_1_12; CB07k_2_12; CB07k_3_12; CB08k_1_12; CB08k_2_12; CB08k_3_12.

**Figure C.2:** Comparison between the Ln transformed values of μmax observed for lactic acid bacteria in vacuum packaged raw meat and the predictions provided by the model of Mejlholm & Dalgaard (Mejlholm and Dalgaard, 2007, 2013). In the plot on the left, red line represents the equivalence (i.e., predictions equal to observations). Grey dots represent the uncorrected predictions, while green points represent the corrected values after applying the calibration factor. Plot on the right show the observed μmax (grey) versus the predicted μmax (blue) as a function of the temperature of the experiment. The predictions for two different extreme pH values are also represented with the lines for an illustrative purpose

A calibration factor of 1.89 was used to correct the μmax, of the predictive model (Eq. 4) to simulate the growth of lactic acid bacteria in vacuum-packaged meat. This value corresponds to the Bf value obtained for temperatures up to 10°C. For the uncertainty assessment, a uniform distribution from 1.30 to 2.17 was used.

### C.3. Predictive model for pseudomonas

To simulate the growth of pseudomonas in meat under aerobic conditions during dry-ageing, the growth rate (μmax) model of Neumeyer et al. (1997) was selected as it accounts for the effect of storage temperature and aw. The model, which is also available in ComBase Premium, was developed from experiments in laboratory growth media and for chilled beef stored between 0 to 20°C, including temperature as the sole environmental factors (Eq. 5).

$$ \sqrt{\mu_{\max}} = 0.1539 \cdot (T + 7.6) \cdot \sqrt{(a_w - 0.947)} \quad (5) $$

The performance of this predictive model for aerobically stored meat was evaluated. Growth behaviour and storage temperature were collected from experimental trials available in the ComBase Browser of the ComBase portal (www.combase.cc) and/or complemented with additional data from scientific literature. A total of 80 growth rates (μmax) values for naturally contaminating pseudomonas on meat (aerobic conditions) were obtained, covering different microbial strains and experimental conditions (meat species/pieces, meat source and level of initial contamination, packaging materials, storage temperature, enumeration methods, etc.).

Bf and Af were calculated by comparing the predicted versus observed μmax as described in C.1. Figure C.3 shows the comparison between the μmax observed in meat and the predictions provided by the model. The predictive model overestimated (80%) the μmax with a Bf = 1.80, i.e., grey dots were systematically almost above the equivalence line. Moreover, the Af was 1.95 was reduced to 1.37 after the correction, indicating an acceptable precision of the predictive model.
<figure><img src="image_55.png" /></figure>

Data from Skandamis and Nychas (2002a,b); Koutsoumanis et al. (2006); Tsigarida and Nychas (2001); Delaquis and McCurdy (1990); Lasta et al. (1995); Skandamis and Nychas (2002a,b) and ComBase records ID: allP_78; allP_79; allP_80; NP_101; NP_93; NP_97; NP_102; NP_94; NP_98; NP_103; NP_95; NP_99; NP_100; NP_104; NP_96; KTM_13; KTC_17; KTC_21; KTC_25; KTC_29; KMS_11; KML_11; Tas3484; Tas3485; Tas3486; Tas3487; Tas3499; Tas3500; Barrera_Ps1; PA_16; PA_15; PA_14; PA_13; BA_28; BA_27; BA_26; BA_25; PA_40; PA_39; PA_38; PA_37; BA_44; BA_43; BA_42; BA_41; PA_52; PA_51; PA_50.

**Figure C.3:** Comparison between the Ln transformed values of μmax observed for pseudomonads on aerobically stored meat and the predictions provided by the model of Neumeyer et al. (1997). In the plot on the left, the red line represents the equivalence (i.e., predictions equal to observations). Grey dots represent the uncorrected predictions, while green points represent the corrected values after applying the calibration factor. Plot on the right show the observed μmax (grey) versus the predicted μmax (blue) as a function of the temperature of the experiment

### C.4. Predictive model for Yersinia enterocolitica

Several mathematical models to quantify the growth rate of Y. enterocolitica were found in the literature (Lindberg and Borch, 1994); (Sutherland and Bayliss, 1994); (Adams et al., 1991) (Little et al., 1992) besides those available in ComBase portal and in the Pathogen Modelling Program. However, they were not considered appropriate for the purpose of the opinion because they were based on absorbance or conductance without direct application to quantify the cell concentration as cells or cfu and/or because they were focused on the effect of temperature, pH and acidulant.

A growth rate square root-based model with temperature as the independent factor was obtained (Eq. 6) by fitting the experimental data extracted from Gill and Reichel (1989). This work dealt with the growth of Y. enterocolitica in high pH beef under vacuum packaging at five different temperatures (from -2°C, to 10°C). The predictions provided by this model were close to the μmax observed (n = 23 data) with a slight overestimation (10% on average) as indicated by a Bf = 1.10 (Figure C.4). However, the accuracy of the predictions was fairly poor as indicated by an Af = 1.68

$$ \mu_{\max} = \ln(10) \cdot (0.01347 \cdot (T + 5.57685))^2 \quad (6) $$