# Co-construction of a method for evaluating the intrinsic quality of bovine milk in relation to its fate

Lucille Rey-Cadilhac, Raphaëlle Botreau, Anne Ferlay, Sophie Hulin, Catherine Hurtaud, Romain Lardy, Bruno Martin, Claire Laurent

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Lucille Rey-Cadilhac, Raphaëlle Botreau, Anne Ferlay, Sophie Hulin, Catherine Hurtaud, et al. Co-construction of a method for evaluating the intrinsic quality of bovine milk in relation to its fate. Animal, 2021, 15 (7), pp.100264. 10.1016/j.animal.2021.100264. hal-03259574

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1 Co-construction of a method for evaluating the intrinsic quality of bovine milk

2 in relation to its fate

3 L. Rey-Cadilhacª, R. Botreauª, A. Ferlayª, S. Hulinᵇ, C. Hurtaudᶜ, R. Lardyª, B.

4 Martinª, C. Laurentª

5

6 a Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, F-63122

7 Saint-Genès-Champanelle, France

8 b Pôle Fromager AOP Massif central, F-15000 Aurillac, France

9 c PEGASE, INRAE, Institut Agro, F-35590, Saint-Gilles, France

10

11 Corresponding author: C Laurent. E-mail: claire.laurent@vetagro-sup.fr

12 Abstract

13 There are time-tested assessments for the environmental and economic aspects of
14 sustainability. Its societal aspect has mainly been approached through the
15 assessment of animal welfare. However, the intrinsic quality of milk is seldom taken
16 into account. We developed a participatory construction method for the overall
17 assessment of intrinsic milk quality in its different dimensions (sensory, technological,
18 nutritional and health), according to the fate of the raw milk. Two assessment models
19 were developed, for semi-skimmed standardized ultra-high temperature (UHT) milk
20 and for pressed uncooked non-standardized raw milk cheese. They were constructed
21 by a participatory approach involving experts in the dairy sector with the aim to obtain
22 a diagnostic tool that could be used in the field to help farmers to manage the quality
23 of their milk (by prioritizing improvements on major problems). They were shaped
24 from prerequisite specifications (limited costs and time of application, desire to obtain
25 a transparent tool with all the steps kept visible) and current technical and scientific
26 knowledge. They were based on indicators obtained from raw bulk tank milk analyses
27 (30 for UHT milk and 50 for cheese assessments), which were then aggregated into
28 criteria, principles, dimensions and overall intrinsic quality at farm level. The
29 assessment models had parts in common, e.g. same four dimensions, common
30 indicators for health and nutritional dimensions. They also had process-specific
31 features: units chosen, criteria, indicators and weightings in relation to the final
32 product specifications. For instance, sensory and technological dimensions are more
33 complex and preponderant in the cheese assessment (3 principles for cheese vs. 1
34 for UHT milk in both dimensions). Another example is the lack of microbial pathogens
35 (as potential health risk for consumer) in the UHT milk assessment because of
36 pasteurization. The assessment models then underwent a sensitivity analysis and an
37 application in 30 farms in indoor and grazing periods to finally obtain overall UHT milk
38 and cheese quality scores at a one-year level. The tool was found to be applicable at
39 farm level. However, we observed low overall quality scores with a narrow dispersion,
40 characteristic of a severe evaluation. Even so, the assessment models showed up
41 seasonal differences of the UHT milk and cheese quality at both overall and
42 dimensional levels. In the light of new scientific knowledge and future quality
43 objectives, these are adaptable to other dairy products allowing for their specific
44 features.

45

46 **Keywords:** Dairy cows, cheese and ultra-high temperature milk, bulk tank milk,
47 multicriteria assessment model, participatory approach

48

**Implications**
This article presents the development of two intrinsic quality of milk assessment models according to milk fate (ultra-high temperature milk or raw milk cheese) and following a participatory approach. It is the first multicriteria assessment of the intrinsic quality of milk with the definition of principles to evaluate four quality dimensions (sensory, technological, nutritional, and health), criteria and indicators to be considered, and rules to interpret them. The approach followed is adaptable to other dairy products, allowing for the specific features of the product and the demands of the stakeholder group seeking to develop it.

## Introduction

In a context of a continuous decrease in the number of dairy farms, their assessment for sustainability is a crucial issue. There are time-tested assessment methods for the environmental and economic aspects of sustainability (Halberg et al., 2005; Zahm et al., 2008), but the assessment of its social aspect is more complex. Social sustainability has two levels: social sustainability within the farm (quality of life, working conditions...) and social sustainability external to the farm, constantly evolving with values and concerns of the society (Lebacq et al., 2013): the main societal expectations are animal welfare and intrinsic product quality. There has been a significant development of tools for assessing animal welfare (Botreau et al., 2008), but the intrinsic quality of milk is seldom taken into account in assessment methods (Lebacq et al., 2013). A few evaluation methods for the intrinsic quality of milk have been proposed (Harris, 1998; Müller-Lindenlauf et al., 2010; Zucali et al., 2016) with few chosen indicators (3-8 depending on the method), and mostly related to health (somatic cells count, protein and fat contents, milk fat composition or total bacterial count), and technological dimensions (microorganisms) of quality. Intrinsic quality can
73 be defined as a combination of sensory, technological, nutritional, and hygiene
74 dimensions (Coulon, 2008) and the quality expectations are not the same according
75 to the fate of the milk (Prache et al., 2020). However, these existing evaluations
76 address quality on targeted aspects and without considering the fate of the milk. The
77 aim of this study was to develop a participatory construction method for the overall
78 assessment of intrinsic milk quality taking into account all its dimensions depending
79 on the fate of the raw milk. Ultimately, the goal is to obtain an assessment model that
80 could be used in the field to help farmers to manage the quality of their milk.

81 **Material and methods**

82 The multicriteria assessment model of the intrinsic quality of milk was constructed in
83 three steps (Lairez et al., 2015) from the participative approach : definition of the
84 scope, definition of the conceptual and methodological frameworks and validation of
85 the assessment model (Figure 1).

86 **_The different steps of the assessment model construction_**

87 *Step 1 - Definition of the scope of the assessment*

88 The first step was the definition of the scope of the assessment by the experts,
89 described by the object to be evaluated and its spatial and temporal scale, the aim of
90 the assessment and its future users, and its prerequisites (e.g. financial and time
91 costs, ability to run it).

92 *Step 2a - Definition of the conceptual framework*

93 The overall quality had to be defined in several *dimensions* or qualities, then
94 subdivided into *principles* and *criteria* (and *subcriteria* if needed) that itemized the
95 principles into more concrete categories. Criteria were assessed by *indicators* that
96 were measurable (= raw data) and that had to meet four conditions (Lairez et al.,
97 2015): scientific relevance, feasibility, clarity and interpretability. We then checked
98 that the hierarchical structure respected the scope choices (step 1; Figure 1),
99 especially the indicators that had to fulfil strict specifications in terms of feasibility and
cost. If possible or necessary we then simplified the structure in that sense.
100

101 *Step 2b - Definition of the methodological framework*

102 The methodological framework of the assessment is defined from the previously built
103 hierarchical structure to interpret and aggregate the indicators up to the overall
104 assessment (Figure 1). We decided to use the CONTRA tool, an aggregation method
105 based on a decision tree using fuzzy sets (Bockstaller et al., 2017; Botreau et al.,
106 2018). This tool requires the setting of a few parameters to interpret and to aggregate
107 the indicators up to the overall assessment. In our case, a common quantitative scale
108 for the scoring was defined: from 0, worst possible quality score, to 10, best possible
109 quality score.

110 Interpretation of the indicators. For each indicator, threshold values to delimit the
111 favourable class (above which the score will be 10/10) and the unfavourable one
112 (below which the score will be 0/10) had to be defined. In the fuzzy zone (between
113 the two classes), the element to be interpreted (indicator value, criterion or principle
114 score, generically called 'input variable') is considered to be part of both classes with
115 different degrees of membership. CONTRA then aggregates these two membership
116 degrees to produce an intermediate score for each input (Bockstaller et al., 2017),
117 thus limiting the risk of a threshold effect induced by the drop from one class to
118 another.

119 Aggregation of scores. CONTRA aggregates indicators' scores to produce the
120 criterion score (and so on until the overall quality score). This aggregation relies both
121 on weightings (is one indicator more important than another?) and management of
122 compensations between indicators (do we want to be severe by limiting the possibility
123 of compensating for a bad score on one indicator by a good one on another?). Other
124 specific settings that are not presented here were also defined (refer to Bockstaller et
125 al., 2017).

126 Thus, three types of parameter had to be defined: threshold values, weights and
127 rules for compensation management.

128 Step 3 - Validation of the assessment model

129 Sensitivity analysis. For a better overview of the assessment tool's behaviour, a
130 sensitivity analysis was performed using the screening method initially developed by
131 Morris (1991) and modified by Campolongo et al., (2007), known as the elementary
132 effects method. For each input variable, the method calculates two indices, µ
133 (assessing the overall influence of the input on the final result of the assessment) and
134 σ (estimating both the nonlinearity of the impact and interactions with other inputs). A
135 high value of µ indicates a marked overall influence on the output (total effect). A high
136 σ indicates either a non-linear effect on the output, or involvement in interaction with
137 other inputs (higher than one-order effects). More details are given in Supplementary
138 Material S1.

139 Test of the assessment model on dairy farms. In addition, the behaviour of the
140 models was also checked through a test on 30 commercial farms. Farms
141 characteristics are presented in Table S1, they were selected to cover a large
142 diversity of farming systems (plain and mountain, intensive and extensive...). Bulk
143 tank milk samples were collected in July (grazing period) and in January-March
144 (indoor period) in a tank containing an even number of milkings (2, 4 or 6) except in
145 two farms equipped with a milking robot. A 1.2 L sample of milk was directly collected
146 in the tank after 5 min agitation using a sterile sampling rod through the manhole.

147 The sample was kept between 0 and 4°C during the transfer to the lab within 18 h.

148 Twelve and nine indicators were analysed on fresh milk within 48 h after sampling
149 and on frozen milk samples (–20°C), respectively (Table S2). Some of the analyses

150 were performed by private laboratories, using mostly routine methods. The others
151 were performed by research laboratories using reference methods (e.g. gas

152 chromatography for milk fatty acids, FA). When data were missing (some analyses
153 were not carried out because sampling and analysis of bulk tank milks occurred

154 before the assessment model had been finalized), they were approximated by
155 existing equations or assigned a unique mean value for all the farms obtained from
156 the literature or databases (Table S2). The two assessment models were then

157 applied to those 30 farms.

158 **The participative approach for the construction of the multicriteria assessment model**
159

160 The construction of the assessment model was based on a participative approach

161 with the consultation of scientists and stakeholders of the dairy sector. This approach
162 is based on the confrontation of diverse points of view to reach a consensus that
163 satisfies the whole group (bottom-up approach) within focus group meetings, and so

164 allows a better adoption of the tool by the stakeholders who helped to build it. The
165 people involved in the process, later called ‘experts’, were divided into 2 groups: (i)

166 Leader group composed of researchers on milk quality (2 people) and stakeholders
167 (2): representative of the cheese sector and one from a private company. They

168 participated in the specific steps identified in Figure 1; (ii) Specific experts group
169 according to the indicators addressed, who participated in one or more ad hoc
170 meetings composed of researchers on specific milk compounds (2) or on milk

171 technology (2), technologists (1), doctors and nutritionists (4), farm advisors (2),

172 representatives of the cheese sector (2), process manager within a dairy company

173 (1). They participated in the specific steps identified in Figure 1.

174 The focus group meetings (about 4 h long) were organized following the simplified

175 DELPHI (Linstone and Turoff, 1975) method with only one round with phases of

176 individual reflection of experts and phases of pooling/discussion (for more details,

177 see Supplementary material S2).

178 **Results**

179 **Definition of the scope of the assessment**

180 During the first meeting, the experts defined the object to be evaluated as the bulk

181 tank milk of a farm, safe for human consumption and thus complying with the relevant

182 legislation). The intrinsic quality of bulk tank milk from the farm was evaluated at the

183 year level.

184 The aim of the assessment was to propose an action-oriented diagnostic tool to be

185 used by farmers to manage the overall quality of their milk (by prioritizing

186 improvements on major problems). This had an impact on the setting of the following

187 initial prerequisites by the experts: (i) transparency: to keep all the intermediary

188 steps accessible and to make it possible to point out the strengths and weaknesses

189 of the assessment, (ii) genericity and standardization: the assessment should be

190 applicable to any dairy farm, independently of the local context, (iii) use in routine

191 practice: this implies a limited cost (<100 €/year), a limited time for

192 sampling/surveying (<1 h), the use of routine methods for milk analyses (chemical,

193 microbial or spectral methods), and a time limit for processing the information (<1 h).
194 The sampling must thus be easy (a single milk sample taken from the bulk) and non-
195 invasive (no measure on animals, only on milk).

196 To obtain an assessment at year level but to limit the costs, the experts decided to
197 plan only two periods of assessment: grazing and indoor. The overall score at year
198 level would be calculated from a weighted average of the two seasonal quality scores
199 as follows:

200 Year quality score = [Annual part time spent in barn (%) × Indoor period quality score + Annual part time spent on pasture (%) × Grazing period quality score] / 100
201
202 The experts soon found that it was not possible to describe the dimensions at lower
203 levels and define criteria of quality without taking into account the fate of the milk.

204 Accordingly, two different products were targeted, and two assessment models were
205 developed: one for semi-skimmed standardized ultra-high temperature (UHT) milk
206 and the other for pressed uncooked non-standardized raw milk cheese, these having
207 very different transformation processes and final desired characteristics.

208 **Definition of the conceptual framework**

209 *Intrinsic quality defined through its different dimensions and according to the fate of*
210 *the milk*

211 The experts defined intrinsic milk quality as a combination of four major dimensions
212 during the first meeting too: (i) sensory: ability of the milk to contribute to the
213 organoleptic characteristics of the final product, (ii) technological: ability of the milk to
214 be correctly processed into the final product, (iii) nutritional: ability of the milk to give
215 a final product that contributes significantly to the coverage of consumers' nutritional
216 needs, and (iv) health: ability of the milk to give a final product that has potentially
217 more beneficial and less deleterious effects on human health. The contribution of milk
218 (bottled or carton, flavoured, concentrated or powdered milk and other milks) and
219 cheese (ripened, processed, unripened fresh or other cheeses) to the total
220 recommended daily intakes was calculated from data on recommended nutrient
221 intakes by nutrient, the average intake of milk and cheese and the levels of these
222 nutrients in milk and cheese (Agence française de sécurité sanitaire des aliments
223 (AFSSA), 2009). Only those nutrients for which the contribution of milk and cheese
224 exceeded 5% of the total daily intakes were kept in the nutritional quality part of the
225 respective assessments. Constituents whose concentrations can be greatly
226 increased or decreased by the process were also excluded. For instance, vitamins B
227 were not taken into account in the cheese assessment even though cheese supplies
228 them in abundance, these vitamins resulting mainly from synthesis by cheese flora
229 (Reif et al., 1976) and not from bulk milk content.

230 *From dimensions to indicators*

231 Twenty-one meetings were needed to define the principles, criteria and indicators
232 (together with their interpretation) for the two assessment models.
233 This construction was an iterative process, particularly in relation to the choice of
234 indicators, which had to be both scientifically relevant and compliant with the
235 specifications (limited cost and easy analysis, in particular). The hierarchical structure
236 was thus simplified and the Leader group decided to do it with 3 possibilities for the
237 indicators originally selected: (i) if the indicator was measurable (analytical cost and
238 method available in private laboratories), then it was kept; (ii) if the indicator was not
239 directly easily measurable but it could be predicted by analytical methods (e.g.
240 prediction by mid-infrared method) or proxies that allowed to approach it (e.g.
241 coagulase positive staphylococcus level to estimate biological toxins level
242 (enterotoxins), pseudomonas level to estimate coming proteolysis intensity), then it
243 was kept too; (iii) if the indicator was not directly measurable and there was no
244 simplified analysis method or proxy that allowed to approach it, then it was deleted. If
245 no indicator was retained for a branch of the structure, then the entire branch was
deleted.
246

247 The UHT milk conceptual framework was structured into 4 dimensions, 9 principles
248 (1–4 per dimension), 27 criteria (plus 13 sublevels), and 28 indicators (Figure 2).

249 The cheese conceptual framework was structured into 4 dimensions, 11 principles
250 (2–3 per dimension), 27 criteria (plus 15 sublevels and 6 sub-sublevels), and 50
251 indicators (Figure 3).

252 **Definition of the methodological framework**

253 *Interpretation of indicators*

254 For a majority of indicators, the thresholds were defined during the consultation
255 process. However, for most of the nutritional and health indicators no consensus was
256 reached and experts were only able to define the global sense of interpretation (e.g.
257 lower is better) but not the thresholds. These were therefore defined from the
258 analysis of a database of around 1300 individuals (Coppa et al., 2013; Chassaing et
259 al., 2016): the 0/10 score was defined as the 2.5th percentile and the 10/10 score as
260 the 97.5th percentile of the values of this database for indicators interpreted as
261 ‘higher is better’ (and the opposite for ‘lower is better’ indicators), and then discussed
262 and validated with the experts (Table S3).

263 *Aggregation of inputs*

264 Nine meetings were then needed to aggregate indicators, criteria, principles and
265 dimensions: definition of weightings (7 meetings) and compensation rules (2
266 meetings) by the experts.
267 Weightings. The UHT milk assessment model was principally based on nutritional
268 quality (weighting of 40%), which took into account the mineral, protein, and vitamin
269 inputs. Then came health quality (30%), predominantly based on the impact of lipid
270 content on health. Lastly, the technological (20%) and sensory (10%) qualities were
271 characterized by only one principle each: stability and suitability for preservation, and
272 taste, respectively. The cheese assessment model was largely based on sensory
273 (35%) and technological (30%) qualities. Sensory quality was mainly based on
274 flavour (flavour defects from non-desirable microorganisms) of the final cheese.
275 Technological quality was predominantly evaluated by the cheese yield capacity and
276 drainability. Health quality (20%) was mainly based on the effects of the bioactive
277 components (especially lipids) and nutritional quality (15%) based on energy and
278 macro-element inputs. All weightings for UHT milk and cheese are given in Figures 2
279 and 3, respectively.

280 Compensations. The decision was collectively made to limit compensations to
281 promote an overall quality without bad results on some inputs. Two main rules were
282 defined by the experts whatever the final product:

283 (i) the more heterogeneous the input scores, the more limited the compensation. This
284 means that with the same average score, the more heterogeneous the milk was over
285 the inputs to be aggregated, the lower was the aggregate score;

286 (ii) the lower the input scores, the more limited the compensation: higher severity
287 when globally input scores are bad.

288 Validation of the assessments

289 Sensitivity analysis of the assessment models
290 The results obtained on the seven most influential indicators on the overall score for
291 both models are presented in Table 1 (for more details, see Tables S4 and S5). For
292 both assessment models, the μ were all of the same order of magnitude so no
293 indicators predominated in their influence on the overall score. However, some
294 indicators were more influential than others. The most influential indicator in both
295 assessments was the Pseudomonas level. This indicator was present and influential
296 in all four dimensions of the two assessments. It was the only indicator for sensory
297 dimension for milk assessment and the most influential in the sensory dimension of
298 the cheese assessment.
299 The other most influential indicators of the overall UHT milk quality score were initial
300 pH, calcium, β-carotenes, vitamin B2 and lactose concentrations. They were also
301 among the most influential indicators of the main dimensions: calcium and vitamin B2
302 concentrations for nutritional dimension score; β-carotenes and lactose
303 concentrations for health dimension score, and initial pH and Pseudomonas level for
304 technological dimension score.
305 The other most influential indicators of the overall cheese quality score were the
306 casein concentration, total bacteria count, α-linolenic acid (ALA), urea and calcium
307 concentrations. Caseins were present in three of the four dimensions, health,
308 nutritional and technological, and the most influential indicator of both these last
309 dimension scores. The other most influential factor in the cheese nutritional
310 dimension score was the ALA concentration, whereas it was urea concentration for
311 the cheese technological dimension score. For the sensory dimension score,
312 Pseudomonas level and the total bacteria count were the most influential indicators.
313 The most influential indicators of the health dimension score were not included in the
314 most influential indicators of the cheese overall score but were β-glucuronidase-
315 positive E. coli level (μ = 1.78, σ = 4.00; Table S5) and β-carotene concentration
316 (μ = 1.7, σ = 0.86; Table S5).

317 *Test of the assessment models on dairy farms*

318 The ranges of values obtained for indicators from the milks collected in the 30 farms
319 are presented in Table S3, covering fairly well the range defined by the thresholds for
320 each indicator. Mean quality scores of the 30 farms are presented in Table 2 and in
321 Figures S1 and S2.

322 At year level, cheese and UHT milk global quality scores were of 3.5 ± 0.7 and 2.6 ±
323 0.5 out of 10 (mean ± SD), respectively, with a narrow variability and a maximum that
324 did not exceed 5 for both assessment models. At dimension and season scales, the
325 variability increased, with a broader observed distribution of the scores. When
326 considered at season level, quality scores were higher in grazing than indoors for the
327 cheese and UHT milk assessments (3.7 ± 1.0 vs. 3.1 ± 1.0 for cheese, p = 0.02; 2.8
328 ± 0.5 vs. 2.1 ± 0.4 for UHT milk, p < 0.001). At dimensional level, health scores were
329 significantly higher in the grazing period, whereas no difference by season in
330 nutritional scores appeared in either assessment. Also, technological scores were
331 higher in the grazing period in the UHT milk assessment model but no differences
332 across seasons appeared for the cheese assessment model. Finally, sensory scores
333 were higher in the grazing period in the cheese assessment (sensory score in the
334 UHT milk assessment was assigned the same value for all the farms because of
335 missing data, so no comparison could be made).

336 **Discussion**

337 *Definition of intrinsic quality*
338 Milk intrinsic quality has often been defined as the resultant of four dimensions:
339 nutritional, sanitary/safety/hygiene, sensory, and technological (Coulon, 2008; García
340 et al., 2014). In our assessment models, the sensory and technological dimensions
341 have a meaning close to that usually taken in the literature, but the nutritional and
342 health dimensions encompass a different meaning. Nutritional quality usually refers
343 to the combination of ability to meet food requirements and positive/deleterious
344 effects of milk on human health. For example, FA profile has often been related to the
345 nutritional quality of milk, although the human nutritional needs for FAs can be strictly
346 defined only for essential FAs: ALA and linoleic acid (LA), and not on the other FAs
347 having beneficial or deleterious effect on health (Agence nationale de sécurité
348 sanitaire de l'alimentation, de l'environnement et du travail (ANSES), 2011). In this
349 light, the experts decided to clearly separate nutritional aspects from effects on health
350 in these assessment models. Only the ALA and the LA contents were therefore taken
351 into account in the nutritional dimension (in the cheese assessment model). Finally,
352 sanitary quality usually refers to the absence of hazardousness often evaluated as
353 the absence of pathogens and/or of chemical pollutants in the milk (Coulon, 2008;
354 García et al., 2014). Here, the definition was enlarged to cover all potential impacts
355 on human health, positive and negative. As the appraised object was a bulk tank milk
356 safe for human consumption, pathogens such as Listeria monocytogenes did not
357 appear in our assessment models despite belonging to health aspects.

358 Assessment models according to the fate of milk

359 Considering the fate of the milk entailed differences between the two assessment
360 models of the quality of the milk (for cheese and UHT milk).

361 *Importance of dimensions according to the fate of milk*
362 The experts opted to assign more weight to the sensory aspects for cheese
363 assessment model, considering that the raw milk cheese is purchased by consumers
364 for its gustative and olfactory experience and less for nutritional or health
365 expectations. As the technological process is very complex and largely responsible,
366 in interaction with the milk composition (Frétin et al., 2019), for the sensory
367 characteristics of the final cheese, the technological dimension was also given a
368 large weighting. On the contrary, UHT milk is an ‘everyday’ product that is mainly
369 used as an ingredient among others, so that the sensory dimension in UHT milk
370 assessment model is limited to its taste or rather the absence of defects in the taste,
371 as milk is defined as having no special taste per se (Clark and Bodyfelt, 2009). As it
372 is more frequently consumed, the expectations of its effects on nutrition and health
373 were assumed to be high, justifying higher assigned weighting.

374 *Criteria and indicators according to the fate of milk*

375 At criteria and indicator levels, the differences between the two assessment models
376 were still perceptive. The sensory dimension was based only on the absence of
377 defects in the UHT milk assessment, while for the cheese, three aspects referring to
378 the human senses were considered: appearance, texture, and flavour. The
379 technological dimension of the UHT milk assessment model was based on the
380 capacity of the milk to permit good conservation of the final product, linked to acidity
381 and micellar stability (linked in turn to possible milk precipitation during storage;
382 Gaucher et al., 2011). In comparison, the technological dimension in the cheese
383 assessment model referred more to parameters that could affect the transformation
384 process, such as acidification capacity, drainability and more generally cheese yield
385 capacity.
386 The transformation process leading to the final product also meant differences
387 between indicators. For example, no microbial aspects were considered in the UHT
388 milk assessment model as the microbial constituents are destroyed by the process
389 (heating at ultra-high temperature), except for the level of Pseudomonas, considered
390 as a predictor of proteolysis that can occur both before and after the transformation
391 process. This group of bacteria produces proteolytic enzymes able to stay active after
392 a high temperature treatment and impair milk quality (Gaucher et al., 2011). Similarly,
393 lactose and soluble protein contents were not relevant to the cheese assessment
394 model as they are nearly completely released in the whey during the clotting and
395 draining stages of the pressed uncooked non-standardized raw milk cheese process
396 (Kindstedt, 2014). On the contrary, FA profile, minerals, and total β-carotenes
397 appeared in both assessment models.

398 *Indicator units and thresholds according to the fate of milk and the dimension*
399 FA contents were measured as concentrations (g/L of milk) in the cheese
400 assessment model, as the milk used in the process was non-standardized for milk
401 fat. Conversely, in the UHT milk process, the milk was fat-standardized, milks were
402 not differentiated by FA concentrations, but instead by FA relative proportions (% of
403 total FAs).

404 For a given indicator used in different dimensions and in both assessment models,
405 thresholds can differ from one dimension to another and between models. For
406 example, in the cheese assessment model, fat-to-protein ratio was used as an
407 indicator for the cheese yield in the technological dimension and for the cheese
408 texture in the sensory dimension, with different interpretations according to the
409 dimension. The main drivers of the cheese yield are proteins (more specifically
410 caseins) and fat content in milk, with a higher weighting for the protein part (Banks,
411 2007). However, a certain balance between fat and protein is required to guarantee
412 the ability of the protein matrix to retain fat globules during cutting and early stages of
413 stirring and limit fat loss in whey (Guinee et al., 2007). The ‘technological’ score of
414 the fat-to-protein ratio was therefore higher for a lower ratio (10/10 for ratio ≤1.10 and
415 0/10 for a ratio ≥1.40; Table S3). The process is different for the effect of the fat-to-
416 protein ratio on the cheese texture. Cheese firmness is closely related to fat in dry
417 matter, which is linked to the fat-to-protein ratio of the milk. An increase in the fat-to-
418 protein ratio will thus give a softer, smoother cheese (Coulon et al., 2004).

419 Cheesemaking targets an intermediate fat-to-protein ratio, so that the cheese texture
420 corresponds to the standards for the cheese variety. Hence the distribution of
421 ‘sensory’ scores was bell-shaped, with an optimal score for a ratio of 1.15, and then
422 degressive scores for larger or smaller ratios (0/10 for ratio ≤1.05 and ≥1.35).

423 To conclude, of the 37 and the 21 different indicators in the cheese and the milk
424 assessment models, respectively, 7 indicators are common (same unit, same
425 interpretation) between the two models.

426 Assessment models shaped from participative approach

427 The building process, using a participative approach, produced the first overall
428 intrinsic milk quality assessment model, to our knowledge. It made it possible to
429 reconcile the points of view of the different actors involved in the development of the
430 models. Scientists from the dairy field and professionals from the dairy industry,
431 ensured the scientific and technical validity of the information and thus the credibility
432 of the assessment models. Perhaps the actors missing from this panel who could
433 confer a stronger legitimacy are the breeders. Besides, the number of meetings
434 required was high but could perhaps be reduced with experience. However, reducing
this number could negatively impact the participatory approach and reduce credibility of the tool (e.g. through lack of consensus). The models were salient since the information given by the assessment was relevant for the decision makers (farm level) and appropriate to the context (suitable for use in the field and adapted to two products). According to Cash et al. (2002), salience, credibility and legitimacy are the three properties that an assessment must possess to be functional as a decision support tool and be accepted by the stakeholders.

### Assessment models shaped from the scope

Experts chose the indicators associated with each dimension and prioritized them considering the constraints of the specifications identified for the tool at the beginning of the project. Some relevant indicators were withdrawn because they were too costly to analyse. Some other indicators were not directly measured, but estimated, either by others with less costly analyses, or by mid-infrared spectrometry or flow cytometry methods when reliable prediction equations existed (Table S2). The health dimension was particularly impacted by these specifications by ignoring some pathogens, trace metals and organic pollutants, for which there are threshold values not to be exceeded to allow the commercialization of the product.

Some other indicators were kept in the assessment models even if they could only be analysed with reference analysis methods (so they could not be analysed on all laboratories and had a quite high cost) because experts considered that in the near future routine analysis methods will be developed. This induced a gap between the initial specifications and the developed assessment models on these points (that affected the test on 30 farms). This will have to be updated in the future according to scientific and technical advances in analytical methods.
459 Beyond of the choice of the relevant indicators, the scope of the assessment also
460 impacted the developed models in their structure in order to maintain the information
461 available and transparent at all levels, from indicators to the overall assessment. This
462 allows the models to be used to advise the farmers (by self-assessment or with the
463 help of farm advisor) by identifying the main advantages (to be maintained) and
464 drawbacks (to be improved) in terms of milk quality. An example of such an
465 application on a real farm is given in Figure S3. However, this is only a diagnostic
466 tool, to find the best remedial solutions the farmer or his advisor would have to think
467 about new practices that can solve drawbacks without negatively impacting the good
468 points observed in terms of quality.

469 Assessment models shaped from current technical and scientific knowledge and its
470 interpretation by the focus groups

471 Experts chose the indicators with their current knowledge on their relevance and the
472 possibility of defining thresholds to evaluate them (according to the knowledge and
473 the references available at a given time). As knowledge and references evolve
474 continuously, the assessments had to be upgradeable. For the health and nutritional
475 dimensions of quality, the panel of experts agreed on the choice of the indicators and
476 on their rough interpretation (such as, for example, the higher the ALA content, the
477 better the health evaluation of the milk, in accordance with the literature (Haug et al.,
478 2007; ANSES, 2011), but they did not succeed in giving threshold values for most of
479 the indicators, although they successfully defined them for sensory and technological
480 dimensions. Hence it was more difficult to determine a threshold value above which
481 the quality was impaired in the health and nutritional dimensions because of a lack of
482 consensus among the experts due to insufficient knowledge. Levels of nutrients from
483 milk that could affect health and nutrition positively or negatively are very difficult to
484 determine as their interpretation depends on the composition of the rest of the
485 consumer's diet and on many other aspects (physical activity, genetics, environment,
486 etc.). Quantitative recommendations are set only on the overall consumption of the
487 constituents (minerals, FAs, etc.) (Agence nationale de sécurité sanitaire de
488 l'alimentation, de l'environnement et du travail (ANSES), 2011), and not on the
489 consumption of dairy products and even less on the nutrient content in the dairy
490 product itself (Martin et al., 2019). These dimensions of milk quality are also more
491 sensitive issues, for which the positioning of the experts was more difficult. A
492 database was therefore used to define these thresholds values. This means that they
493 do not have a direct nutritional or health meaning (no thresholds for which the
494 indicator would become dangerous), even if the general orientation of the
495 interpretation is known. It will be possible to review this point in step with the future
496 evolution of scientific knowledge on the contribution of dairy products to dietary
497 recommendations and requirements (Martin et al., 2019). In addition, the indicators
498 used to assess nutritional quality were selected among those providing at least 5% of
499 needs by the products concerned (UHT milk and cheese) thus depending on the
500 current, geographically targeted (France) consumption of these products. As this
501 consumption can vary according to place and time, it had to be possible to modulate
502 and update the assessment.

503 The participatory approach also allowed stimulating discussion among the experts in
504 the collective focus group times and produced unexpected ways to measure some
505 milk quality criteria. For example, the protein level was evaluated by both protein
506 content and the level of Pseudomonas, which can induce proteolysis before milk
507 transformation that would lead to denaturation of the proteins, impairing coagulation.
508 The alcohol and Ramsdell tests are rarely used as indicators in the literature, but are
509 commonly used in the dairy industry to test the stability of the colloidal suspension of
510 caseins and thus the stability of milk (Gaucher, 2007).

511 **Validation of the assessments**

512 *Pseudomonas, an influent indicator*

513 In both assessment models, the level of Pseudomonas appear to be most influential
514 input (Table 1). As previously explained this indicator is considered as a proxy of
515 proteolysis that will occur between the measurement in the tank and the milk delivery
516 to the transformation plant. Whenever an indicator related to the proteins content in
517 milk (total proteins, caseins, soluble proteins) was used, the experts associated it
518 with the Pseudomonas indicator to consider a potential level of proteolysis. As a
519 consequence, this indicator is used several times for both assessments. Its great
520 influence on the results is thus not surprising but may be questioned: protein
521 degradability is obviously relevant in respect with the milk quality, but does it deserve
522 to have such an important overall weight? Deeper investigations should be performed
523 to answer that question and it would be possible to revise the models accordingly.

524 *A low but real discriminating power*

525 The assessment was performed on 30 farms. Overall quality scores on the 30 farms
526 presented a narrow dispersion, which could question the power of the assessment to
527 discriminate the bulk tank milks of the different farms in terms of intrinsic quality.
528 However, some indicators could not be measured (alcohol test, Dornic acidity, levels
529 of Pseudomonas and E. coli) and the corresponding missing values were replaced by
530 the same mean value extracted from databases or literature (Table S2) for all the
531 farms, independently of the season, which could explain this narrow dispersion.
532 Despite this limitation, the assessment was able to discriminate the different milks in
terms of intrinsic quality at the seasonal level as it showed significant differences
between indoor and grazing period quality scores of the cheese and UHT milk
assessments. Quality scores were better in grazing than in indoor periods, resulting
from significant seasonal differences in health and sensory scores, mainly in the
cheese assessment, and in health and technological scores in the UHT milk
assessment. This supports the choice of the experts to plan two periods of
assessment to take this seasonal variability into account in the final score. Milk
components and properties related to nutritional, technological or organoleptic
aspects are known to vary widely during the year, notably from changes in the
feeding system (Verdier-Metz et al., 2005; Ferlay et al., 2006; Hurtaud et al., 2014).

### A severe assessment tool

The choice to limit compensation between scores led to low overall scores. To obtain
a good score at each aggregation level, the milk of a farm must have good scores in
all sublevel parts. This severity was accentuated by the construction of the
assessments. Some indicators such as *Pseudomonas* level or casein concentrations
served several times in the assessment to evaluate different criteria and principles,
resulting in a high influence of these indicators on the assessments (Table 1). Thus, if
a bulk tank milk has a bad score on this indicator, it will be penalised several times.

### Conclusion and perspectives

An innovative method for developing milk quality assessment has been built and led
to an evaluation of the intrinsic quality of milk taking into account its different
dimensions (nutritional, health, technological and sensory dimensions) as well as the
fate of the milk (semi-skimmed standardized UHT milk or pressed uncooked non-
standardized raw milk cheese). These two assessment models have the same four
557 dimensions but differ greatly on principles (mainly on sensory and technological
558 ones), criteria and indicators as well as on weights given to the inputs. They were
559 based on the decisions of experts, according to their own knowledge and opinions.
560 The models built are intended to be evolutive and the different elements can be
561 revised: addition or deletion of indicators, definition of thresholds, weights assigned
562 to inputs, compensation rules, in order to provide improvement at each level of the
563 evaluation process.

564 Moreover, this method can be adapted to other products (particularly dairy products)
565 according to their specificities and constraints in use (costs, application time and
566 users). The assessment models are transparent. They allow the assessor to start
567 from the overall score and access all the intermediate scores up to the indicators
568 measured in the milk. It is therefore possible to identify, at each level, what can be
569 maintained and what has to be improved. Combined with a thorough knowledge of
570 the relationships among the inputs and the breeding practices that influence them,
571 these models can be used to instigate changes in farming practices to improve milk
572 quality.

573 Ethics approval

574 No animal were used.

575 Data and model availability statement

576 None of the data were deposited in an official repository. The rights of access to the
577 data, or models will be made available by the respective author upon a confidential
578 request.

579 Author ORCIDs
580 L. Rey-Cadilhac : 0000-0002-4554-7662

581 R. Botreau : 0000-0002-9599-4313

582 A. Ferlay : 0000-0002-0651-792X

583 S. Hulin :

584 C. Hurtaud : 0000-0002-3965-826X

585 R. Lardy : 0000-0003-1338-8553

586 B. Martin : 0000-0003-2501-8306

587 C. Laurent : 0000-0002-8350-8496

588 **Author contributions**

589 L. Rey-Cadilhac : Validation, Formal analysis, Writing - Original Draft, Writing -

590 Review & Editing, Visualization

591 R. Botreau : Conceptualization, Methodology, Software, Formal analysis,

592 Investigation, Writing - Original Draft, Supervision, Project administration, Funding

593 acquisition

594 A. Ferlay : Resources, Writing - Original Draft, Supervision

595 S. Hulin : Conceptualization, Writing - Review & Editing, Project administration

596 C. Hurtaud : Resources, Writing - Review & Editing

597 R. Lardy : Validation, Formal analysis, Writing - Review & Editing

598 B. Martin : Conceptualization, Methodology, Writing - Review & Editing

599 C. Laurent : Conceptualization, Methodology, Data Curation, Investigation, Writing -

600 Original Draft, Supervision, Project administration, Funding acquisition

601 **Declaration of interest**

602 None to declare.
603 Acknowledgements

604 The authors would like to thank the experts who participated in the focus groups for
605 the development of the assessments, interviewers and samplers who went to the
606 farms to collect milk and information, as well as the French farmers who collaborated
607 in the samplings and surveys. Authors would also like to acknowledge the help they
608 received when drafting this article from the UMRH Scientific Writing Workshop
609 chaired by Isabelle Veissier and Isabelle Ortigues-Marty. The authors also thank
610 MetaForm Langues (Clermont-Ferrand, France) for checking and amending the
611 English text.

612 Financial support statement

613 This research was supported by the Ministry of Agriculture in the framework of the
614 CasDAR Qualenvic project (N°1251).

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715 Table 1

716 Sensitivity analysis of the UHT milk and cheese assessments of bovine bulk tank milk¹

<table><thead><tr><th rowspan="2">Indicators</th><th colspan="2">Overall score</th><th colspan="2">Nutritional dimension score</th><th colspan="2">Health dimension score</th><th colspan="2">Technological dimension score</th><th colspan="2">Sensory dimension score</th></tr><tr><th>μ</th><th>σ</th><th>μ</th><th>σ</th><th>μ</th><th>σ</th><th>μ</th><th>σ</th><th>μ</th><th>σ</th></tr></thead><tbody><tr><td colspan="11"><strong>UHT milk assessment</strong></td></tr><tr><td>Pseudomonas level</td><td>1.46</td><td>4.02</td><td>0.42</td><td>0.74</td><td>0.76</td><td>2.55</td><td>1.86</td><td>5.67</td><td>5.88</td><td>10.71</td></tr><tr><td>pH<sub>initial</sub></td><td>1.41</td><td>2.19</td><td></td><td></td><td></td><td></td><td>6.90</td><td>10.79</td><td></td><td></td></tr><tr><td>Calcium concentration</td><td>0.74</td><td>0.66</td><td>2.20</td><td>1.67</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total β-carotenes concentration</td><td>0.73</td><td>0.49</td><td></td><td></td><td>2.22</td><td>1.19</td><td></td><td></td><td></td><td></td></tr><tr><td>Vitamin B2 concentration</td><td>0.67</td><td>0.32</td><td>1.88</td><td>0.47</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lactose concentration</td><td>0.62</td><td>0.50</td><td></td><td></td><td>1.81</td><td>1.24</td><td></td><td></td><td></td><td></td></tr><tr><td colspan="11"><strong>Cheese assessment</strong></td></tr><tr><td>Pseudomonas level</td><td>1.55</td><td>2.82</td><td>1.08</td><td>2.15</td><td>0.52</td><td>1.00</td><td>0.88</td><td>1.79</td><td>2.54</td><td>5.22</td></tr><tr><td>Total caseins concentration</td><td>0.94</td><td>0.62</td><td>2.23</td><td>1.60</td><td>0.92</td><td>0.87</td><td>2.10</td><td>1.20</td><td></td><td></td></tr><tr><td>Total bacteria count</td><td>0.82</td><td>1.48</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.89</td><td>3.17</td></tr><tr><td>ALA concentration</td><td>0.71</td><td>0.41</td><td>2.24</td><td>1.24</td><td>0.76</td><td>0.33</td><td></td><td></td><td></td><td></td></tr><tr><td>Urea concentration</td><td>0.68</td><td>0.76</td><td></td><td></td><td></td><td></td><td>1.66</td><td>1.79</td><td>0.67</td><td>0.77</td></tr><tr><td>Calcium concentration</td><td>0.64</td><td>0.44</td><td>1.56</td><td>0.74</td><td></td><td></td><td>0.83</td><td>0.77</td><td></td><td></td></tr><tr><td>Coliforms level</td><td>0.58</td><td>1.72</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.05</td><td>3.09</td></tr></tbody></table>

717 Abbreviations: UHT = Ultra-high temperature; ALA = α-linolenic acid (C18:3 n-3)

718 ¹ Presentation of the seven most influential indicators of the overall score of each assessment. For the full sensitivity analysis, see Tables S3 and S4
719 **Table 2**

720 Mean year and season milk quality scores on 10 at global and dimensions level obtained on 30 dairy cow farms

<table><thead><tr><th rowspan="2">Scores<sup>1</sup></th><th colspan="3">UHT milk assessment</th><th colspan="3">Raw milk cheese assessment</th></tr><tr><th>Grazing</th><th>Indoor</th><th>Season effect<sup>2</sup></th><th>Grazing</th><th>Indoor</th><th>Season effect<sup>2</sup></th></tr></thead><tbody><tr><td>Year overall quality</td><td colspan="3">2.6 ± 0.5 (1.5-3.6)</td><td colspan="3">3.5 ± 0.7 (2.1-4.8)</td></tr><tr><td>Season overall quality</td><td>2.8 ± 0.5 (1.6-3.8)</td><td>2.1 ± 0.4 (1.3-2.9)</td><td>***</td><td>3.7 ± 1.0 (1.9-5.8)</td><td>3.1 ± 1.0 (1.1-5.1)</td><td>*</td></tr><tr><td>Dimensions of milk quality</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sensory</td><td>7.2 ± 0.0</td><td>7.2 ± 0.0</td><td>-</td><td>4.2 ± 1.0 (2.2-5.8)</td><td>3.4 ± 0.8 (2.0-5.3)</td><td>**</td></tr><tr><td>Technological</td><td>4.3 ± 1.5 (1.2-6.5)</td><td>3.0 ± 1.5 (1.0-6.5)</td><td>***</td><td>4.4 ± 1.2 (2.5-7.4)</td><td>4.5 ± 1.5 (1.5-7.6)</td><td>ns</td></tr><tr><td>Health</td><td>2.8 ± 1.0 (1.2-5.6)</td><td>2.0 ± 0.9 (0.8-4.7)</td><td>**</td><td>6.7 ± 0.9 (4.6-8.1)</td><td>4.5 ± 1.3 (0.8-6.6)</td><td>***</td></tr><tr><td>Nutritional</td><td>3.0 ± 1.1 (1.3-5.2)</td><td>2.9 ± 1.3 (0.7-5.8)</td><td>ns</td><td>3.2 ± 1.3 (1.5-6.1)</td><td>3.4 ± 1.3 (1.1-6.1)</td><td>ns</td></tr></tbody></table>

721 Abbreviations: UHT = Ultra-high temperature; ns = not significant.

722 <sup>1</sup> Mean ± SD (Minimum-Maximum)

723 <sup>2</sup> Symbols indicate a difference between grazing and indoor period's scores at P ≤ 0.05 (*), P ≤ 0.01 (**), and P ≤ 0.001 (***) on the considered dimension

724 and product [results from the Student's test or Wilcoxon test when data could not meet the normality assumption (sensory and health scores in cheese

725 assessment and technological and health scores in milk assessment)].
726 **Figure captions**

727 **Fig. 1.** Construction process of the multicriteria assessment model from the
728 participative approach

729 **Fig. 2.** Structure of the multicriteria assessment of the bovine intrinsic milk quality for
730 ultra-high temperature (UHT) milk production per dimension: A) sensory,
731 technological, health and B) nutritional quality. * Ponderation of the dimension to the
732 global quality score; $\xrightarrow{100}$ Ponderation of the observation (indicators, subcriteria,
733 criteria or principles) to the higher scale one (subcriteria, criteria, principles and
734 dimensions, respectively); a CFU: Colony-forming unit; b FA: Fatty acids.

735 **Fig. 3.** Structure of the multicriteria assessment of the bovine intrinsic milk quality for
736 cheese production per dimension: A) sensory, B) technological, C) health and
737 nutritional quality. * Ponderation of the dimension to the global quality score; $\xrightarrow{100}$
738 Ponderation of the observation (indicators, subcriteria, criteria or principles) to the
739 higher scale one (subcriteria, criteria, principles and dimensions, respectively); a
740 CFU: Colony-forming unit; b FA: Fatty acids; c ALA: α-linolenic acid (C18:3 n-3); d LA:
741 linoleic acid (C18:2 n-6).

742
<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>
Dimensions

Principles

Criteria

Subdivisions of

Criteria level

R1

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>