# Biopreservation strategies: Can organic acid concentrations be sufficient to predict yeast and mould growth in strawberry purée?

Clarisse Breard, Camille Duc, Christian Ginies, Cécile Grondin, Aurélie Cendrès, Isabelle Souchon, Frédéric Carlin

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Clarisse Breard, Camille Duc, Christian Ginies, Cécile Grondin, Aurélie Cendrès, et al.. Biopreservation strategies: Can organic acid concentrations be sufficient to predict yeast and mould growth in strawberry purée?. International Journal of Food Microbiology, 2025, 443, pp.111396. 10.1016/j.ijfoodmicro.2025.111396. hal-05233867

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# Biopreservation strategies: Can organic acid concentrations be sufficient to predict yeast and mould growth in strawberry purée?

Clarisse Breard<sup>a,b</sup>, Camille Duc<sup>b</sup>, Christian Ginies<sup>a</sup>, Cécile Grondin<sup>c</sup>, Aurélie Cendrès<sup>b</sup>, Isabelle Souchon<sup>a</sup>, Frédéric Carlin<sup>a,*</sup>

## ARTICLE INFO

Dataset link: Research data to Breard et al. (2025), Biopreservation strategies: can organic acid concentrations be sufficient to predict yeast and mould growth in strawberry purée?, submitted for publication to In (Original data)

**Keywords:**

Minimum inhibitory concentration

Predictive model

Fermentation

Spoilage yeasts and fungi

## ABSTRACT

With growing demand for healthier food without additive, biopreservation offers a promising alternative. However, most studies focus on its effectiveness rather than the development of biopreservation strategy. To address this, a modeling-based approach was used to design a biopreservation solution for strawberry purée.

A predictive model was developed to assess the growth of spoilage yeasts and moulds in strawberry purée in the presence of organic acids (OAs). For model development and evaluation, minimum or least inhibitory concentrations (MIC, LIC) of lactic, acetic, propionic, sorbic, and benzoic acids were determined for representative spoilage strains for strawberry (Saccharomyces uvarum, S. paradoxus, Rhodotorula glutinis and Botrytis cinerea) and five yeasts isolated from spoiled strawberry purée. Results aligned with literature data, identifying sorbic acid as the most effective inhibitor and lactic acid as the least across all strains.

The model, developed in synthetic strawberry medium (SSM), generally provided conservative or accurate predictions. Only 1 % and 10 % of observed growth values in SSM and strawberry purée, respectively, were faster than predictions (with a 20 % error margin). However, a biopreservation solution based on fermented vegetable juice had lower inhibitory activity than expected from its OA content, likely due to other compounds present. This suggests OA concentrations alone cannot predict biopreservation performance.

Identifying these additional compounds and understanding their actions could enhance model accuracy, incorporating matrix physicochemical properties.

## 1. Introduction

The food industry faces increasing consumer demand for minimally processed and additive-free foods (Román et al., 2017), driven partially by growing health concerns (Lane et al., 2024). Fresh strawberries are highly susceptible to fungal spoilage. Consequently, strawberry purée, a common ingredient in dairy and bakery products, is heavily processed to extend shelf-life (Etievant and Delolme, 2011). Fresh strawberry spoilage is often caused by fungi (especially Botrytis cinerea) (Feliziani and Romanazzi, 2016), which lead to soft rot, and yeasts, which generate off-odours and flavours (Ragaert et al., 2006b). To limit the spoilage of processed products, strawberries are generally washed with

chlorinated water (e.g., maximum of 80 ppm under French regulations; Anses, 2012) and undergo thermal treatments (pasteurisation) (Etievant and Delolme, 2011). The inevitable result is alterations in vitamin and phenolic compounds content, colour and taste (Marszalek et al., 2017). Consequently, it is worth exploring alternative stabilisation processes, such as biopreservation.

Biopreservation is “the use of natural or added microorganisms, fermentates, or their metabolites to extend food shelf-life and/or increase food safety” (Leyva Salas et al., 2017). The preservative effect results from antimicrobial compounds, such as bacteriocin-like inhibitory substances, volatile organic compounds, phenolic acids, fatty acids, and, notably, organic acids (OAs) (Le Lay et al., 2016a). In their

<footnote><sup>a</sup> INRAE, Avignon Université, UMR SQPOV, Avignon, France</footnote>

<footnote><sup>b</sup> Atelier du Fruit, Cavaillon, France</footnote>

<footnote><sup>c</sup> SPO, Univ Montpellier, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote>* Corresponding author at: UMR408 INRAE – Avignon Université, Sécurité et Qualité des Produits d’Origine Végétale (SQPOV), Domaine Saint Paul, 228, route de l’Aérodrome, Site Agroparc – CS, 40509, France.

E-mail address: frederic.carlin@inrae.fr (F. Carlin).</footnote>
undissociated form, OAs can diffuse through the cell membrane and then dissociate within the cytoplasm which is generally at neutral pH, causing acidification and altering cell metabolism (Leyva Salas et al., 2017). Biopreservation is applied on various foods, such as dairy products, meat products, fruits, and vegetables. However, there are some limitations associated with its usage, linked to unacceptable organoleptic changes, regulatory standards or safety requirements. Furthermore, inconsistent results have been obtained when biopreservation has been used with laboratory media versus real foods, likely because the latter contain complex, diverse natural microbiota and have variable levels of nutrients and bioactive compounds (Leyva Salas et al., 2017). For example, chitosan and a bacteriocin from *Carnobacterium maltaromaticum* produced a synergistic antimicrobial effect in a buffered nutrient broth but not in beef (Hu et al., 2019). Similarly, while 69 lactic acid bacteria and propionibacteria showed antifungal activity in their reference nutrient medium, only 12 exhibited this activity when applied to bakery products (Le Lay et al., 2016b). These differences in inhibitory capacity were attributed to the higher pH of the bakery products, as well as the lower concentrations of antifungal compounds.

Predictive microbiological models predict the growth of foodborne microorganisms as a function of intrinsic or environmental factors such as pH, preservative concentrations or temperature. These models most commonly focus on pathogenic bacteria, like *Salmonella* or *Listeria monocytogenes*, to assess the microbial risks of storage conditions and predict safe shelf-life (Stavropoulou and Bezirtzoglou, 2019), but are also among tools and strategies for microbial spoilage control (Snyder et al., 2024). Predictive models also have relevance when exploring biopreservation solutions. For example, a model looked at competition between the bioprotective strain L. sakei and the pathogenic strain L. monocytogenes in surimi and tuna pâté, and highlighted differences in their interactions that depended on food matrix and temperature (Bolívar et al., 2021). Predictive models were also developed to evaluate the potential of bioprotective cultures or biopreservatives to control spoilage yeasts in dairy products (Buehler et al., 2018; Nielsen et al., 2021). These models could therefore be used for the development of biopreservation strategies.

The objective of the study was to build and evaluate a model that could predict the effects of OAs on spoilage yeast and fungi in the context of strawberry purée biopreservation. Thus, a set of yeast and mould strains associated with strawberry spoilage was first grown in a synthetic strawberry medium that contained OAs to build a predictive model. Then, performances were examined when predicting (i) the growth of representative strains in synthetic strawberry medium and in real strawberry purée, and (ii) the growth of spoilage microbiota in fresh strawberry purée. Finally, the model was used to assess the efficacy of a potential biopreservation solution—a fermented vegetable juice.

# 2. Materials and methods

## 2.1. Microbial strains

### 2.1.1. Representative strains

The yeasts *Saccharomyces paradoxus* CLIB 3821 (isolated from wild strawberry), *Saccharomyces paradoxus* CLIB 253 (isolated from pear), and *Rhodotorula glutinis* CLIB 365 (isolated from a hot acidic spring) were obtained from the CIRM (*Centre International de Ressources Microbiennes - Levures*; SPO, University of Montpellier, INRAE, Institut Agro Montpellier, https://cirm-levures.bio-aware.com). The filamentous fungus *B. cinerea* FR89 (isolated from fresh strawberry) was obtained from INRAE (UMR408-SQPOV, INRAE). These collection strains (hereafter, representative strains) were chosen because they are representative of the fungus and yeast species found on fresh or spoiled fruit, especially strawberries (Feliziani and Romanazzi, 2016; Hidalgo et al., 2013; Ragaert et al., 2006a; Tournas et al., 2006). The strains were stored at -20 °C in 20 % glycerol.

### 2.1.2. Isolation and identification of spoilage strains

Fresh strawberry purées (see section 2.2) were kept at 8 °C for 10 days until they showed visible signs of spoilage and mould formation. The spoiled purée was diluted in sterile water, and plated on plate count agar (PCA, Biokar, Beauvais, France) and yeast glucose chloramphenicol (YGC, Biokar) agar plates for total microbiota and yeast and mould counts. Isolates of each colony type on the plates were purified by subculturing. A total of five strains (hereafter, spoilage strains) were isolated and used in further experiments.

DNA was extracted from the isolated spoilage strains using phenol chloroform (Legras and Karst, 2003). Briefly, cells were suspended in 0.2 mL of lysing buffer (2 % Triton X-100, 1 % SDS, 100 mM NaCl, 50 mM Tris pH 8, and 50 mM EDTA), 0.2 mL of phenol chloroform isoamyl alcohol 25:24:1, and 0.3 g of lysing matrix Y (MP biomedical). After vortexing and centrifugation (13,800 g for 5 min), the aqueous phase was washed with chloroform, supplemented with a volume of isopropanol and centrifuged (13,800 g for 4 min). The pellet was then washed with 70 % ethanol, dried, resuspended in TE buffer with 2 µL of RNAase, and incubated for 30 min at 37 °C. Species identification was performed by analysis of the rRNA gene D1/D2 domains (Kurtzman and Robnett, 1997). The amplification and sequencing of strains were done as described in Jacques et al. (2016). Sequences were compared to sequences in databases, such as GenBank (www.ncbi.nlm.nih.gov/pubmed), using the basic local alignment search tool (BLAST) program.

## 2.2. Media

To build the predictive model, data on strain growth in a synthetic strawberry medium (SSM) were collected. To assess the model's predictive performance, strains were grown in pasteurised strawberry purée and fresh strawberry purée. The composition of the SSM was based on the nutritional table for strawberry (Ciqual, 2017; Giampieri et al., 2012; Sturm et al., 2003; USDA, 2022). The sterile SSM was composed of fructose (27 g/L), glucose (21.9 g/L), yeast extract (6.5 g/L; Biokar), citric acid (5.6 g/L), trisodium citrate (3.1 g/L), malic acid (2 g/L), and ascorbic acid (57.5 mg/L); the pH was adjusted to 3.5. A pasteurised strawberry puree was purchased at Sicoly (Saint-Laurent-d'Agny, France). This purée was made from variety *Charlotte* strawberries and heat treated thanks to an ohmic heating treatment; the purée's pH was 3.5. Pasteurised purée was mixed with sterile molten agar (66 g/L) and then poured into Petri dishes to prepare a pasteurised purée agar at 10 g/L agar. Concentrated sterile SSM (1.05×) without OA and with agar (15 g/L) was mixed with the appropriate concentration of a sterile OA solution to obtain an SSM agar at 15 g/L agar; the result was poured into Petri dishes. Both media were used to characterize *B. cinerea* growth. Fresh strawberry purée was made from fresh Clery variety strawberries purchased from a local market (Avignon, France). Unripe and damaged strawberries were eliminated. The calyx and peduncle were removed from the remaining strawberries, which were then mashed using a Thermomix (Cloyes-sur-le-Loir, France) for 2 min at a speed of 750 rpm. Batches of fresh strawberry purée were prepared on four different dates, three in 2023 and one in 2024, for use in experiments that same day.

## 2.3. Growth conditions

Yeasts were precultured in malt extract broth (ME, Fluka, France, Le Mans) for 48 h at 28 °C under stirring (150 rpm). Cells were washed twice with sterile saline (NaCl at 0.9 % wt/vol in demineralised water) and then pelleted using centrifugation (15,300 g at 4 °C). The inoculum was adjusted to $10^5$ CFU/mL based on optical density at $\lambda = 600$ nm (OD$_{600}$) and was subsequently used to inoculate the culture medium (5 % v/v). *B. cinerea* was precultured on potato dextrose agar (PDA, Biokar) for 10 days at 23 °C; conidia were harvested with 1 mL of sterile demineralised water by scraping with a sterile spreader. Conidia concentration was determined using a haemocytometer and adjusted to $10^3$
conidia/mL. Plates were inoculated by depositing a 5-µL droplet of the suspension at the centre of the plates.

B. cinerea growth was quantified by measuring mycelium diameter. The growth of the inoculated yeasts was quantified either by measuring OD₆₀₀ in the ME or SSM or by colony counting on PDA. Growth of the spoilage strains was measured by counting the colonies present on YGC agar. Microbial counts were performed by spreading serial decimal dilutions of cell suspensions in sterile demineralised water. Plates of YGC, PDA, and PCA were incubated at 25 °C for 5 days, 28 °C for 3 days, and 30 °C for 3 days, respectively.

## 2.4. Chemicals and the biopreservation solution

Unless otherwise indicated, chemicals were purchased from Sigma-Aldrich (Saint Louis, USA). The hydrochloric acid (37 %), mevalonolactone (97 %), malic acid (99 %), acetic acid (99 %), and ethyl acetate were purchased from Fisher Scientific (Elancourt, France). The trisodium citrate (99 %), propionic acid (99 %), and orthophosphoric acid were purchased from Prolabo (Fontenay-sous-Bois, France). The potassium sorbate (99 %), succinic acid, Orafti L85 fructooligosaccharide solution, β-carotene, and triton 100× were purchased from Jean Niel (Grasse, France), Cambridge Isotope Laboratories, Inc. (Andover, USA), Beneo (Mannheim, Germany), Extrasynthèse (Genay, France) and VWR Life Science (Solon, USA), respectively.

The biopreservation solution (further named "fermented juice" in the work) was supplied as ready-made from Atelier du Fruit (Cavaillon, France). It is the supernatant of a carrot purée that has been fermented by lactic acid bacteria, propionibacteria, gluconobacter and *Saccharomyces cerevisiae* and then pasteurised and centrifuged (Breard, 2025). This fermented juice has an OA concentration (ca 5 g/L of propionic acid, 5 g/L of acetic acid, and 10 g/L of lactic acid) that seems high enough to inhibit yeast growth and therefore act as a biopreservation agent based on model results. The concentrations of non-OA compounds with known antifungal properties (succinic acid, 3-phenyllactic acid, 3-[4-hydroxyphenyl]lactic acid, 2-hydroxyisocaproic acid, caffeic acid, diacetyl, ethanol, and mevalonolactone) (Le Lay et al., 2016a; Leyva Salas et al., 2017) were also determined, as described in section 2.5.

A synthetic unfermented juice was made to assess the impact of some compounds in the fermented juice, composed of glucose (2 g/L), phospholipids (1 g/L), fructooligosaccharides (0.8 g/L), pectin (0.7 g/L), β-carotene (50 mg/L), zinc chloride (1 mg/L), manganese chloride (1 mg/L), pantothenate (1 mg/L). Phospholipids were incorporated under sterile conditions using a Polytron PT 2500 E ultraturax at 12000 rpm after sterilisation of the medium.

## 2.5. Metabolite analysis

OA and ethanol concentrations were quantified using high-performance liquid chromatography (HPLC; Jasco, Lisses, France). For OAs, an RSpak KC-C precolumn and RSpak KC-811 column (Shodex, Tokyo, Japan) with a UV-4075 detector set to 210 nm (Wu et al., 2002) were used. For ethanol, an HPLC-SEC Sugar-Pak column (Waters, Milford, USA) with an RI-4050 detector (Gomez et al., 2002) were used.

Diacetyl was quantified as follows (Ginies C., INRAE Avignon, France, unpublished). First, 4 mL of supernatant were placed in a 20-mL sealed headspace vial and left to equilibrate for 17 min at 50 °C under stirring (800 rpm). Adsorption was carried out for 17 min at 50 °C using a solid-phase microextraction (SPME-Arrow fibre, PDMS/DVB 120 µm). The result was injected into a gas chromatography-mass spectrometry device (GC-MS Agilent 5977; Agilent Technologies, Inc., Santa Clara, USA) equipped with a ZB-5MS capillary column (30 m × 0.25 mm × 0.25 µm; Phenomenex, Le Peck, France). After the extraction process, the fibre was desorbed in a split/splitless injector for 2 min at 270 °C (split ratio 15). The carrier gas was helium at a constant flow rate of 0.9 mL/min. The oven temperature programme was 40 °C for 1 min followed by an increase to 200 °C at a rate of 5 °C/min. The mass detector

was operated in electron ionisation mode (70 eV), and its scanning range was 29–300 m/z. Diacetyl concentration was determined via comparison with an external standard in an aqueous solution.

When measuring the concentrations of 3-phenyllactic acid, 3-(4-hydroxyphenyl)lactic acid, 2-hydroxyisocaproic acid, caffeic acid, and mevalonolactone, derivatisation with BSTFA (+1 % TMCS) was performed prior to carrying out the GC-MS analysis (Roessner et al., 2001). Two mL of supernatant were acidified using HCl (2 M) to reach a pH of 2. Two extractions were carried out using 2 mL of ethyl acetate: after vortexing for 30 s and centrifuging for 5 min at 3000 rpm, the supernatants were recovered and pooled. The result was dried under a flow of nitrogen. Once evaporation was complete, 50 µL of pyridine and 50 µL of BSTFA (+1 % TMCS) were added to the samples, which were then maintained at 70 °C for 1 h. The samples then underwent the same GCMS analysis (device and column) used with the diacetyl analysis, except that there were some changes in chromatographic conditions: injector temperature was 300 °C, and the split ratio was 50. The oven temperature programme was 100 °C for 1 min followed by an increase to 300 °C at a rate of 10 °C/min. The scanning range was 72–500 m/z. Compound concentrations were determined via comparisons with external standards in ethyl acetate and adjusted based on extraction yield (estimated by adding known quantities of the compounds to the unfermented juice before extraction).

All the compounds analysed via GC-MS were identified by comparing their retention times and mass spectra with those of reference compounds and those from the NIST2.4 library. MassHunter Workstation GC/MS Data Acquisition (v. 10.1) and MassHunter Quantitative Analysis (v. 10.2) were used.

## 2.6. Assessment of strain sensitivity to organic acids

### 2.6.1. Experimental conditions

Yeasts and moulds were grown in media with various OA concentrations. These were acetic, lactic, propionic, and succinic acid, OAs produced by fermentation and displaying antifungal properties (Lind et al., 2005; Schwenninger et al., 2008), and sorbic and benzoic acid commonly used in foods as preservatives to control yeast and mould growth (Commission Regulation EU, 2011). SSM and ME were prepared that contained acetic acid (10 g/L), lactic acid (50 g/L), propionic acid (6 g/L), succinic acid (12.5 g/L), sorbic acid (0.6 g/L), and benzoic acid (0.6 g/L); the pH was adjusted to 3.5 in SSM and 4.8 in ME using NaOH (2 M). The SSM inoculated with the strains was kept at 8 °C for 12 days. The ME inoculated with the yeasts was kept at 28 °C for 12 days, and the ME inoculated with *B. cinerea* was kept at 23 °C for 12 days.

### 2.6.2. Lowest inhibitory concentrations

Strain sensitivity to the OAs in the SSM and ME was determined by identifying the strains' lowest inhibitory concentrations (LICs), defined as the lowest concentration of an OA that inhibited growth over a 12-day incubation period. These data were used to compare OA sensitivity for the representative strains and the spoilage strains. The yeasts were grown in microplates under orbital stirring (150 rpm), and *B. cinerea* was grown on plates. Growth inhibition was concluded by an OD₆₀₀ increase lower than 0.03 for the yeasts and when radial growth on the agar plates was less than 1 mm (visual assessment) for *B. cinerea*.

### 2.6.3. Minimum inhibitory concentrations

For modeling purposes, minimum inhibitory concentrations (MICs) were determined for strains grown in SSM (regression of eqs. 3 and 4 for yeasts and eq. 3 for *B. cinerea*; see 2.7.1). Yeast growth was observed using a microplate reader (Bioscreen C MBR EZExperiement SW) by monitoring changes in the OD₆₀₀ values of suspensions kept under continuous agitation (linear shaking at 13.5 Hz, amplitude 0.26 mm). The diameter growth of *B. cinerea* on SSM agar plates kept at 8 °C was daily measured for 12 days.
## 2.7. Model implementation

### 2.7.1. Primary model

The Baranyi model (Baranyi and Roberts, 1995) was used to fit yeast growth curves (eq. 1):

$$ \ln(x(t)) = \ln(x_{min}) + \mu_{max} * A(t) - \ln \left( 1 + \frac{\exp^{\mu_{max}}A(t) - 1}{\frac{x_{max}}{x_{min}}} \right) \quad (1) $$

$$ \text{where } A(t) = t + \frac{\ln(\exp^{\mu_{max}t} + \exp^{\mu_{max}\theta} - \exp^{\mu_{max}(t+\theta)})}{\mu_{max}} $$

Here, x is yeast population size (either an OD$_{600}$ value for growth in SSM or a CFU/mL value for growth in strawberry purée), $x_{min}$ and $x_{max}$ are the initial and final yeast population sizes (OD$_{600}$ or CFU/mL), $\mu_{max}$ is the maximum growth rate (d$^{-1}$), $\theta$ is the time until visible growth (OD$_{600}$) or lag time $\lambda$ for growth measured by counting (days), and t is the time (days).

To estimate the lag time $\lambda$ for OD$_{600}$ curves, the correlation between the OD$_{600}$ values and viable cell counts was determined (see Supplementary Fig. S1) (Dalgaard and Koutsoumanis, 2001)(eq. 2):

$$ \lambda = \theta_{OD_{600}} - \frac{\ln\left(\frac{N_D}{N_0}\right)}{\mu_{max}} \quad (2) $$

where $N_D$ is the population at the time at which growth became detectable (CFU/mL) and $N_0$ is initial population (CFU/mL).

The linear model was used to fit the growth curves for B. cinerea (eq. 3) (Dantigny et al., 2005):

$$ d(t) = \mu_{max} * (t - T_D) \quad (3) $$

where d is the mycelium diameter (cm) and $T_D$ is the time at which growth became detectable (i.e., the time at which mycelium diameter reached 2 mm).

Regressions were performed using the R package MicrobialGrowth (employing the nls function), which was developed to facilitate automatic regression of multiple curves (L'Homme and Breard, 2025).

### 2.7.2. Secondary model

The inhibitory effect of each OA on the strains was determined by using the values of $\mu_{max}$ to fit eq. 4 (Coroller et al., 2005):

$$ \mu_{max} = \mu_{opt} * \left( 1 - \left( \frac{[AH]}{MIC} \right)^{\alpha} \right) \quad (4) $$

where $\mu_{opt}$ is the maximum growth rate in the control SSM at 8 °C, [AH] is the concentration of the OA in its undissociated form, MIC is the minimum inhibitory concentration, and $\alpha$ is the curvature parameter. The relationship between OA concentration and $\mu_{max}$ was defined using at least n = 24 experimental points. Regression analysis was performed using the nls function in R after removing any outliers (standardised residuals >1.96). The higher the MIC, the less sensitive the strain is to the acid (i.e., a higher concentration is needed to inhibit strain growth). An $\alpha$ value >1 means a curve with a downward concave shape (i.e., the decrease in growth rate is low at lower concentrations and higher close to the MIC). An $\alpha$ value <1 means that the curve has an upward concave shape.

The secondary model was then built assuming multiplicative effects among OAs (eq. 5, Coroller et al., 2005):

$$ \mu_{max} = \mu_{opt} * \left( 1 - \left( \frac{[AH_{AA}]}{MIC_{AA}} \right)^{\alpha_{AA}} \right) * \left( 1 - \left( \frac{[AH_{LA}]}{MIC_{LA}} \right)^{\alpha_{LA}} \right) * \left( 1 - \left( \frac{[AH_{PA}]}{MIC_{PA}} \right)^{\alpha_{PA}} \right) \quad (5) $$

where AA, LA, and PA are acetic acid, lactic acid, and propionic acid, respectively.

As yeast cells were precultured and inoculated under the same conditions during each experiment, their initial physiological state ($h_0$) was assumed to be constant (Baranyi and Roberts, 1995). Therefore, the lag time of the yeasts was also affected by the addition of the OAs and was calculated using eq. 6:

$$ \lambda = h_0 / \mu_{max} \quad (6) $$

The value of $h_0$ was calculated for each strain using its growth rate and lag time in control SSM (i.e., without any OAs added) at 8 °C.

### 2.7.3. Model reliability indicators

The model's predictive capabilities were evaluated by examining the quality of the predicted time to critical growth (TCG). For yeasts, TCG was the time to a 3 log increase in population size, which was (i) an increase of 0.15 OD units for yeast in the SSM with inoculated at $5 \times 10^3$ CFU/mL in SSM (see the Supplementary Fig. S1 for the correlation between the OD$_{600}$ values and yeast counts) or (ii) a 3 log increase in CFUs in the strawberry purée. Predicted values of TCG were obtained from the eq. (1) describing the change in log(CFU) over time; in this equation, the values of $\mu_{max}$ and $\lambda$ came from eqs. (5) and (6), where $\mu_{opt}$ is $\mu_{max}$ in pure SSM or strawberry purée at 8 °C and a pH of 3.5 (see the $\mu_{opt}$ values in Supplementary Table S1). For B. cinerea, TCG was the time until mycelium diameter reached 2 mm. Predicted values of TCG were obtained from eq. (3), where $\mu_{max}$ came from eq. (5) and $\mu_{opt}$ was $\mu_{max}$ in pure SSM or strawberry purée at 8 °C and a pH of 3.5 (see the $\mu_{opt}$ values in the Supplementary Table S1).

The predicted TCG values for the representative strains grown in the SSM and strawberry purée were compared to the following: (i) the observed TCG values for the representative strains grown in the SSM containing a combination of AA, LA, and PA; (ii) the observed TCG values for the representative strains grown in the strawberry purée containing AA, LA, or PA individually; (iii) the observed TCG values for the total microbiota in the fresh strawberry purée containing AA, LA, or PA individually or combined; and (iv) the observed TCG values for the representative strains grown in the SSM containing the fermented juice. The observed TCG values for yeasts grown in the SSM and strawberry purée were obtained under the same conditions as for MIC determination (see section 1.5.3), with the exception of the strawberry purée. In the latter case, incubation occurred without shaking.

Goodness of fit was evaluated using the root mean square error (RMSE) (eq. 7; Ross, 1996):

$$ RMSE = \sqrt{\frac{\sum_{i=1}^{n} (TCG_{predicted} - TCG_{observed})^2}{n}} \quad (7) $$

where $TCG_{predicted}$ is the TCG value predicted by the model, $TCG_{observed}$ is the TCG value observed under experimental conditions, and n is the number of observations. The percentages of accurately predicted TCG values (± 20 %) were calculated.

## 2.8. Statistical analysis

The TCG values for the representative strains grown in the SSM containing the fermented juice were analysed as repeated measures (n = 3) using the function lmer (lme4 package). The results were compared using Tukey's test (multicomp package). These analyses were performed in R studio (v. 4.4.0, ©2009–2024 RStudio, Inc).

# 3. Results and discussion

## 3.1. Minimum inhibitory concentrations of organic acids that prevent representative strain growth

The MICs were determined to characterize the representative strains' sensitivity to LA, AA, PA, sorbic acid and benzoic acid during growth in
the SSM (pH 3.5) at 8 °C for 12 days (Table 1). These values and the curvature parameter α were derived from the sensitivity curves, where μmax is a function of the OA concentrations (see Supplementary Fig. 2 for curve examples). Generally, sensitivity to the OAs displayed downward concavity (α > 1), except for LA.

For each OA, strains were ranked from least to most susceptible (i.e., highest to lowest MIC). This ranking was the same for all the OAs tested, indicating that strain sensitivity was independent of OA. At the same time, there were large differences among strains in their sensitivity to a given OA. For instance, in the case of acetic acid, the MIC for *R. glutinis* (0.33 ± 0.02 g/L) was 17-fold lower than that for *S. uvarum* (5.8 ± 0.6 g/L). The observed MICs reflected the variability in MICs seen in previous studies, which reported wide ranges of MICs for different yeasts and moulds (e.g., *S. cerevisiae* and *A. niger*) grown in standard medium at 20–30 °C and a pH of 3.0–4.5 (range in mM of the acid's undissociated form for AA: [4–98.4], LA: [142–220], PA: [3–44], benzoic acid: [0.03–6.4], and sorbic acid: [0.004–3.0]) (Arroyo-López et al., 2008; Bandelin, 1958; Cortés-Zavaleta et al., 2014; Lind et al., 2005; Naren-dranath et al., 2001; Plumridge et al., 2004; Schwenninger et al., 2008; Stratford et al., 2009; Stratford and Anslow, 1998). Sorbic acid and benzoic acid were the most effective preservatives, which fits with past work, illustrating their ability to limit yeast and mould growth in foods (Saad et al., 2005). MICs for LA were extrapolated because exceeding the maximum tested concentration of 50 g/L, resulting in a large standard error and suggesting a lower sensitivity of all strains to LA compared to the other OAs.

<caption>**Table 1**</caption>

Minimum inhibitory concentrations (MICs) of organic acids and the curvature parameter α for *S. uvarum* CLIB 253, *S. paradoxus* CLIB 3821, *B. cinerea* FR89, and *R. glutinis* CLIB 365, which are representative strawberry spoilage strains.

<table><thead><tr><th rowspan="2">Organic acid</th><th rowspan="2">Species</th><th colspan="2">MIC of organic acid ± Confidence interval 95 % expressed as:</th><th rowspan="2">Curvature parameter α<sup>b</sup> ± standard error</th></tr><tr><th>Total concentration, g/L at pH = 3.5</th><th>Concentration (mM) of the undissociated form<sup>c</sup></th></tr></thead><tbody><tr><td rowspan="3">Acetic acid</td><td><i>S. uvarum</i></td><td>5.8 ± 0.6</td><td>92 ± 9</td><td>1.5 ± 0.4</td></tr><tr><td><i>S. paradoxus</i></td><td>4.0 ± 0.1</td><td>64 ± 1</td><td>7.0 ± 1.4</td></tr><tr><td><i>B. cinerea</i></td><td>0.67 ± 0.09</td><td>11 ± 1</td><td>0.92 ± 0.22</td></tr><tr><td rowspan="5">Lactic acid</td><td><i>R. glutinis</i></td><td>0.33 ± 0.02</td><td>5.2 ± 0.3</td><td>5.8 ± 3.2</td></tr><tr><td><i>S. uvarum</i></td><td>1.5 × 10<sup>2</sup> ± 0.5</td><td>1.2 × 10<sup>3</sup> ± 0.4</td><td>1.4 ± 0.3</td></tr><tr><td></td><td>× 10<sup>2</sup></td><td>× 10<sup>3</sup></td><td></td></tr><tr><td><i>S. paradoxus</i></td><td>57 ± 11</td><td>4.5 × 10<sup>2</sup> ± 0.8</td><td>0.7 ± 0.3</td></tr><tr><td><i>B. cinerea</i></td><td>52 ± 7</td><td>4.1 × 10<sup>2</sup> ± 0.5</td><td>0.88 ± 0.28</td></tr><tr><td rowspan="3">Propionic acid</td><td><i>R. glutinis</i></td><td>55 ± 6</td><td>4.4 × 10<sup>2</sup> ± 0.5</td><td>1.1 ± 0.3</td></tr><tr><td><i>S. uvarum</i></td><td>1.8 ± 0.02</td><td>23 ± 0.3</td><td>3.8 ± 0.4</td></tr><tr><td><i>S. paradoxus</i></td><td>1.5 ± 0.02</td><td>20 ± 0.2</td><td>8.0 ± 2.2</td></tr><tr><td rowspan="5">Benzoic acid</td><td><i>B. cinerea</i></td><td>0.50 ± 0.08</td><td>6.5 ± 1.0</td><td>0.60 ± 0.29</td></tr><tr><td><i>R. glutinis</i></td><td>0.33 ± 0.04</td><td>4.3 ± 0.5</td><td>2.0 ± 0.8</td></tr><tr><td><i>S. uvarum</i></td><td>0.13 ± 0.003</td><td>0.86 ± 0.02</td><td>2.7 ± 0.5</td></tr><tr><td><i>S. paradoxus</i></td><td>0.063 ± 0.001</td><td>0.43 ± 0.01</td><td>7.5 ± 3.3</td></tr><tr><td><i>B. cinerea</i></td><td>0.065 ± 0.007</td><td>0.50 ± 0.05</td><td>6.4 ± 4.78</td></tr><tr><td rowspan="5">Sorbic acid</td><td><i>R. glutinis</i></td><td>0.050 ± 0.003</td><td>0.34 ± 0.02</td><td>1.2 ± 0.1</td></tr><tr><td><i>S. uvarum</i></td><td>0.19 ± 0.01</td><td>1.6 ± 0.1</td><td>1.2 ± 0.1</td></tr><tr><td><i>S. paradoxus</i></td><td>0.074 ± 0.009</td><td>0.62 ± 0.08</td><td>4.3 ± 3.1</td></tr><tr><td><i>B. cinerea</i></td><td>0.060 ± 0.018</td><td>0.50 ± 0.15</td><td>2.8 ± 2.4</td></tr><tr><td><i>R. glutinis</i></td><td>0.015 ± 0.0001</td><td>0.13 ± 0.001</td><td>8.0 ± 1.6</td></tr></tbody></table>

<sup>a</sup> MIC and α values obtained via regression (eqs. 2 and 3; n ≥ 24). The strains were grown in synthetic strawberry medium (pH 3.5) at 8 °C for 12 days.

<sup>b</sup> The curvature parameter α is an indicator of strain sensitivity and describes the concavity of the curve arising from the relationship between organic acid concentration and μmax. If α > 1, the curve has a downward concave shape.

<sup>c</sup> Undissociated concentration of organic acids were calculated with Henderson-Hasselbalch equation (see Supplementary Eq. E1 and Table S2).

## 3.2. Model ability to predict representative strain growth in the synthetic strawberry medium containing a combination of organic acids

The observed and predicted TCG values for the representative strains grown in the SSM containing a combination of LA, AA, and PA present at concentrations similar to the MICs (see Supplementary Table S3 for details on the OA concentrations tested) were compared.

On the graph, the line of equality (y = x) indicates a perfect concordance between observed and predicted data (Fig. 1a); points below the line are fail-safe predictions, while points above the line are fail-dangerous predictions. Most of the data points are close to the line, signalling concordance, or under the line, signalling fail-safe predictions. Thus, the model is generally conservative: observed growth tends to be slower than predicted growth. This stronger-than-expected inhibition could be due to a possible synergy or interaction between the OAs. An interaction term was previously included to account for these dynamics by some authors (Zuliani et al., 2007). An interaction term was included in the model, but it failed to significantly improve performance (data not shown) and was therefore not retained.

Model accuracy appeared to be strain dependent: *S. paradoxus* showed the highest fitting accuracy, while *S. uvarum* observed growth was systematically slower than predicted. Furthermore, model accuracy appeared to be lower at higher values of TCG (i.e., higher OA concentrations). This pattern could result from greater synergies at high OA concentrations; it could also be related to the high estimates for the curvature parameter α. Indeed, when α exceeds 1, changes in μmax are more pronounced at OA concentrations closer to the MICs than at less proximate concentrations. In other words, a slight difference in OA concentration could greatly impact μmax.

The mean error estimated via RMSE was 2.4, and 61 % of the TCG values were accurately predicted (± 20 %); 38 % were fail safe and 1 % were fail dangerous (out of 96 observations).

## 3.3. Model ability to predict representative strain growth in the strawberry purée containing a single organic acid

The model's predictions for the growth of representative strains in strawberry purée containing a single OA (LA, AA, or PA) were also evaluated (Fig. 1b, see Supplementary Table S4 for details on the OA concentrations tested). The mean error estimated via RMSE was 1.9, and 74 % of TCG values were accurately predicted (± 20 %). The percentages of fail-safe and fail-dangerous predictions were 16 % and 10 %, respectively (out of 50 observations). Once more, model accuracy was strain dependent, with more fail-dangerous predictions for *S. paradoxus* and more fail-safe predictions for *B. cinerea*. For *R. glutinis*, growth did not reach a 3 log increase until day 12 of incubation, which was consistent with predictions (see Supplementary Table S1 for the growth parameters in both media). The time to 1.5 log increase was therefore chosen instead of time to 3 log increase to evaluate the model for that particular strain (see Supplementary Fig. S3). However, the strain failed to grow under any of the experimental conditions, except for the control. This degree of inhibition was stronger than predicted, which was also the case for *B. cinerea*. This may be due to other inhibitory compounds in the strawberry purée, such as phenolic compounds. For instance, strawberries contain phenolic compounds such as flavonoids and phenolic acids (Buendía et al., 2010) exhibiting antifungal activity (Broberg et al., 2007; Salazar-Aranda et al., 2015). Although predictions were based on optimum growth parameters in strawberry purée, meaning in the presence of phenolic compounds, potential interactions that could increase inhibitory effects may not have been accounted for.

## 3.4. Growth of representative strains versus spoilage microbiota in the strawberry purée

Next, the model developed with the representative strains was used to predict the growth of the spoilage microbiota in the fresh strawberry
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Relationship between observed and predicted TCG (days) for *S. uvarum* CLIB 253 (green •), *S. paradoxus* CLIB 3821 (purple •), *B. cinerea* FR89 (blue •), and *R. glutinis* CLIB 365 (red •) grown in (A) SSM containing a combination of OAs at 8°C (n = 96) and (B) strawberry purée containing one OA at 8°C (n = 50). The predictions were generated by the model described in section 2.6. The observations between the dashed lines are in concordance with the predictions (± 20 %).</figcaption></figure>

purée. *S. uvarum* was used as the reference strain, as it displayed the lowest sensitivity to the OAs. The OAs were added individually or in combination at concentrations expected to delay the growth of *S. uvarum*, based on model predictions. The total TCG values for the four fresh strawberry purées were similar to or longer than those predicted for *S. uvarum* (± 2 days; Fig. 2). This result indicates that the spoilage microbiota were equally sensitive or more sensitive to the OAs than *S. uvarum*, suggesting that *S. uvarum* could serve as a biological indicator in challenge tests and when predicting the growth of spoilage strains or the efficacy of OAs. OA sensitivity greatly differed among the spoilage microbiota in the four fresh strawberry purées. While the spoilage microbiota from purées #3 and #4 displayed similar sensitivity to *S. uvarum*, those from purées #1 and #2 displayed greater sensitivity.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Relationship between observed TCG (days) for spoilage yeasts and moulds in four fresh strawberry purées (purées #1, #2, #3, and #4 in green •, purple •, blue •, and orange •, respectively) containing an OA at 8°C (n = 24) and predicted TCG (days) for *S. uvarum* CLIB 253. The results show that the observed growth for the spoilage yeasts and moulds was slower than the predicted growth for *S. uvarum*. The predictions were generated by the model described in section 2.6. The observations between the dashed lines are in concordance with the predictions (± 2 days).</figcaption></figure>

### 3.5. Sensitivity of representative strains versus spoilage strains to the organic acids

We wondered if the pronounced differences in yeast and mould counts reflected differences in OA sensitivity among the most prevalent spoilage strains in the strawberry purées. Therefore, spoilage strains were isolated from two of the four fresh strawberry purées stored for 10 days at 8 °C until visible spoilage; these strains were then identified, and their sensitivity to the OAs was determined. Only yeasts were detected in the spoiled purées. One predominant strain, identified as *Hanseniaspora uvarum* (CLIB 4026), was isolated from strawberry purée #3, while four predominant strains, identified as *Curvibasidium nothofagi* (CLIB 4024), *Rhodototurla graminis* (CLIB 4025), *Candida saitona* (CLIB 4022), and *Candida sake* (CLIB 4023), were isolated from strawberry purée #2. The strains *C. sake*, *R. graminis*, and *H. uvarum* have previously been isolated from fresh or fermented strawberries (Debode et al., 2013; Hidalgo et al., 2013; Tournas et al., 2006).

The OA sensitivity of these spoilage strains was assessed by comparing their LICs for LA, AA, PA, sorbic acid, and benzoic acid to those of the representative strains (Table 2). It is worth noting that the MICs (Table 1) and LICs obtained in the SMM (Table 2) were consistent with each other even though they were determined using slightly different methods. For all the OAs, the sensitivity of the spoilage strains was similar to or greater than the sensitivity of the representative strains, confirming that the selected representative strains were relevant for studying the behaviour of spoilage strains in fresh strawberry purée. The LIC ranking showed that strains from purée #2 were more sensitive than *H. uvarum*, itself more sensitive than *S. uvarum*, aligning with the sensitivity ranking of the total spoilage microbiota.

The LICs were determined both under optimum conditions (ME at 30 °C and a pH of 4.8) and in a food-like medium incubated under refrigeration conditions (SSM at 8 °C). Strain sensitivity ranks were largely similar between the two media; thus, growth in ME appears to be a good general indicator of strain sensitivity. Sensitivity to OAs based on growth in ME was comparable to previous findings in standard laboratory media at 30 °C and pH ranging from 4.5 to 5.0 (range in mM of the acid's undissociated form for AA: [12–78], LA: [5–37], PA: [4–26], sorbic acid: [0.01–5.8], and benzoic acid: [0.01–6.8] (Arroyo-López et al., 2008; Bandelin, 1958; Lind et al., 2005; Schwenninger et al., 2008).
<caption>**Table 2**</caption>

Lowest inhibitory concentrations (LICs*) of organic acids for *S. uvarum* CLIB 253, *S. paradoxus* CLIB 3821, *B. cinerea* FR89, and *R. glutinis* CLIB 365, and **spoilage strains** (in bold) isolated from strawberry purée, expressed as undissociated concentration of organic acids (mM**).

<table><thead><tr><th rowspan="2">Experimental conditions</th><th rowspan="2">Strains</th><th colspan="5">Organic acid<sup>a</sup></th></tr><tr><th>Acetic acid</th><th>Lactic acid</th><th>Propionic acid</th><th>Benzoic acid</th><th>Sorbic acid</th></tr></thead><tbody><tr><td rowspan="10">SSM at 8 °C</td><td><b>S. uvarum</b></td><td>68 [47–95]</td><td>&gt;4.0 × 10<sup>2</sup></td><td>19 [14–23]</td><td>0.097 [0.070–0.120]</td><td>0.11 [0.09–0.12]</td></tr><tr><td><b>S. paradoxus</b></td><td>53 [47–55]</td><td>2.2 × 10<sup>2</sup> [1.6–2.3 × 10<sup>2</sup>]</td><td>14 [9–16]</td><td>0.040 [0.030–0.050]</td><td>0.045 [0.037–0.045]</td></tr><tr><td><b>R. glutinis</b></td><td>9.0 [6.3–11.8]</td><td>4.0 × 10<sup>2</sup> [3.0–4.0 × 10<sup>2</sup>]</td><td>3.9 [3.2–3.9]</td><td>0.030 [0.025–0.030]</td><td>0.011 [0.010–0.011]</td></tr><tr><td><b>B. cinerea</b></td><td>12 [7–15]</td><td>&gt;4.0 × 10<sup>2</sup></td><td>8.1 [6.1–8.1]</td><td>0.063 [0.063–0.095]</td><td>0.071 [0.047–0.071]</td></tr><tr><td><b>Cb. nothofagi</b></td><td>13 [8–20]</td><td>&gt;4.0 × 10<sup>2</sup></td><td>7.1 [5.8–8.4]</td><td>0.037 [0.025–0.050]</td><td>0.026 [0.022–0.026]</td></tr><tr><td><b>R. graminis</b></td><td>11 [8–11]</td><td>&gt;4.0 × 10<sup>2</sup></td><td>4.6 [3.2–4.9]</td><td>0.047 [0.035–0.050]</td><td>0.013 [0.010–0.015]</td></tr><tr><td><b>C. saitona</b></td><td>20 [13–24]</td><td>3.0 × 10<sup>2</sup> [2.3–3.2 × 10<sup>2</sup>]</td><td>7.1 [5.8–8.4]</td><td>0.050 [0.035–0.060]</td><td>0.026 [0.022–0.026]</td></tr><tr><td><b>C. sake</b></td><td>24 [16–28]</td><td>&gt;4.0 × 10<sup>2</sup></td><td>16 [11–19]</td><td>0.073 [0.050–0.0100]</td><td>0.056 [0.045–0.056]</td></tr><tr><td><b>H. uvarum</b></td><td>44 [36–47]</td><td>&gt;4.0 × 10<sup>2</sup></td><td>1.3 [1.0–1.3]</td><td>0.070 [0.050–0.070]</td><td>0.076 [0.052–0.090]</td></tr><tr><td rowspan="10">ME at 30 °C (yeasts) or 23 °C (B. cinerea)</td><td><b>S. uvarum</b></td><td>54 [44–60]</td><td>&gt;62</td><td>20 [15–22]</td><td>0.54 [0.08–0.82]</td><td>0.64 [0.26–0.81]</td></tr><tr><td><b>S. paradoxus</b></td><td>79 [64–87]</td><td>&gt;62</td><td>46 [36–51]</td><td>1.0 [0.4–1.2]</td><td>4.3 [2.4–4.8]</td></tr><tr><td><b>R. glutinis</b></td><td>4.5 [2.4–6.4]</td><td>&gt;62</td><td>5.4 [2.3–6.9]</td><td>0.23 [0.08–0.41]</td><td>0.16 [0.08–0.16]</td></tr><tr><td><b>B. cinerea</b></td><td>14 [11–14]</td><td>&gt;62</td><td>7.1 [5.8–7.3]</td><td>0.77 [0.49–0.82]</td><td>4.3 [3.2–4.5]</td></tr><tr><td><b>Cb. nothofagi</b></td><td>&lt;2.4</td><td>&gt;62</td><td>&lt;2.2</td><td>&lt;0.05</td><td>0.16 [0.13–0.16]</td></tr><tr><td><b>R. graminis</b></td><td>&lt;2.5</td><td>&gt;62</td><td>&lt;2.9</td><td>&lt;0.07</td><td>&lt;0.064</td></tr><tr><td><b>C. saitona</b></td><td>18 [10–21]</td><td>&gt;62</td><td>6.8 [4.2–13.5]</td><td>0.21 [0.16–0.21]</td><td>0.55 [0.43–0.64]</td></tr><tr><td><b>C. sake</b></td><td>ND<sup>d</sup></td><td>ND</td><td>ND</td><td>ND</td><td>ND</td></tr><tr><td><b>H. uvarum</b></td><td>37 [28–40]</td><td>&gt;62</td><td>13 [9–15]</td><td>0.36 [0.28–0.44]</td><td>2.1 [1.6–2.1]</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** MICs of compounds with previously reported antifungal properties targeting yeasts (red •) or moulds (blue •) and the concentrations of these compounds in the fermented juice (green •). All concentrations are expressed in mM; in the case of the organic acids, it is the concentration of their undissociated form, given experimental medium pH. In the box plots, the central box contains the middle 50% of the data (between the lower and upper quartiles); the line within the box is the median, and the extension lines correspond to the interquartile range. The number of data represented in each box plot is indicated (n<sub>y</sub> for yeasts and n<sub>m</sub> for moulds). Data were taken from the following references: Arroyo-López et al., 2010; Aunsbjerg et al., 2015; Broberg et al., 2007; Cortés-Zavaleta et al., 2014; Eloff et al., 2007; Jay, 1982; Lavermicocca et al., 2000; León Peláez et al., 2012; Narendranath et al., 2001; Pedroso et al., 2019; Ryan et al., 2011; Sakko et al., 2014; Schwenninger et al., 2008; Stratford et al., 2009; Ström et al., 2002; Svanström et al., 2013.</figcaption></figure>

<footnote>a LIC, lowest concentration of OA inhibiting strain growth after 12 days; mean of three values. LICs were determined by growing strains in a synthetic strawberry medium (SSM; pH 3.5) at 8 °C or in malt extract broth (ME; pH 4.8) at 30 °C (yeasts) or 23 °C (B. cinerea).</footnote>

<footnote>b Undissociated concentrations of OA were calculated based on medium pH. See Supplementary Table S5 for LIC values expressed as total OA concentration.</footnote>

<footnote>c [x, y] is the last concentration interval within which growth was seen.</footnote>

<footnote>d Not determined.</footnote>
## 3.6. Model ability to predict representative strain growth in the presence of a biopreservation solution

### 3.6.1. Quantification of antifungal compounds other than OA

The fermented juice, a vegetable juice enriched in OAs via fermentation, was used to evaluate the model's ability to assess the antifungal capacity of biopreservation solutions. In addition to OAs, the fermented juice contained other antifungal compounds (diacetyl, 3-[4-hydroxyphenyl]lactic acid, 2-hydroxyisocaproic acid, caffeic acid, 3-phenyllactic acid, ethanol, succinic acid, and mevalonolactone; Le Lay et al., 2016a; Leyva Salas et al., 2017) (Fig. 3). However, given the low concentrations of these compounds, it was hypothesised that they would not actually have any inhibitory effects. Indeed, except for succinic acid and mevalonolactone, all compounds were present at concentrations at least 10-fold lower than reported MICs for yeasts and moulds growing at a pH of 3–6 (Arroyo-López et al., 2010; Aunsbjerg et al., 2015; Broberg et al., 2007; Cortés-Zavaleta et al., 2014; Eloff et al., 2007; Jay, 1982; Lavermicocca et al., 2000; Pedroso et al., 2019; Ryan et al., 2011; Sakko et al., 2014; Schwenninger et al., 2008; Ström et al., 2002; Svanström et al., 2013). Although MICs for these compounds were not determined for the representative strains, it is reasonable to expect them to be

similar in magnitude, given the similarity between the MICs associated with the OAs in this study and those in the literature. In the case of mevalonolactone, no MIC for fungi was found in the literature. However, it was shown to inhibit the growth of *Fusarium avenaceum* by 10% at 0.077 mM, which is 4.5 fold the concentration at which it occurred in the fermented juice (Niku-Paavola et al., 1999). Succinic acid occurred at a concentration of 21.6 ± 0.2 mM, close to reported MICs (Schwenninger et al., 2008). Consequently, the MICs of succinic acid for the representative strains were determined and found to be greater than 87 mM (data not shown). Considering that the fermented juice was diluted (30%) when mixed into the strawberry purée, the concentrations of mevalonolactone and succinic acid were at least 10-fold lower than the reported MICs. Their effects were therefore likely to be negligible as well.

### 3.6.2. Application of the biopreservation solution to inhibit representative strains

The ability of the fermented juice (FJ) to inhibit the growth of *S. uvarum*, *S. paradoxus*, and *R. glutinis* in the SSM (Fig. 4, SSM + FJ) was estimated via comparisons to growth in the control SSM. Based on model predictions, concentrations of 5%, 20% and 30% of FJ in SSM were

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

**Fig. 4.** Inhibitory capacity of SSM containing different percentages (5–30%, v/v) of the fermented juice (SSM + FJ) compared to the inhibitory capacity of the SSM containing: an equivalent percentage of OAs (SSM + OAs), an unfermented version of the juice (SSM + UFJ), the unfermented juice and the OAs (SSM + UFJ + OAs), and a synthetic version of the unfermented juice and the OAs (SSM + SUFJ + OAs). Inhibitory capacity was assessed by monitoring the TCG of *S. uvarum* CLIB 253 (A), *S. paradoxus* CLIB 3821 (B), and *R. glutinis* CLIB 365(C) grown at 8°C. Observed growth was compared to predicted growth (described in section 2.6) (---). The hatched growth bar indicates a case in which no growth was observed before the end of the 12-day incubation period.
required to expect a delayed growth of *R. glutinis*, *S. paradoxus* and *S. uvarum*, respectively. The same SSM dilution rates with water had no effect on strain growth (data not shown). Overall, the fermented juice had (i) lower inhibitory effects than predicted and (ii) significantly lower inhibitory effects than those of the same concentrations OAs in the SSM: growth in the SSM was significantly faster than growth in the SSM that contained OAs (*p* < 0.001) but was not significantly different from growth in the SSM that contained the FJ (*p* > 0.24). To explore whether the fermented juice's weaker inhibitory effect resulted from a growth stimulant in the juice, strain growth in an SSM containing an unfermented version of the juice (UFJ) was studied. Generally, this unfermented juice did not significantly improve growth in the SSM, with only a marginal effect for *S. paradoxus*. Therefore, it seems unlikely that the juice's weaker inhibitory effect relative to the OAs was due to compounds in the juice that favoured growth. Interestingly, when the SSM contained the OAs and the unfermented juice, growth was significantly faster than in the SSM containing just the OAs (*p* ≤ 0.01). This result suggests the presence of compounds in the unfermented juice that enhanced yeast resistance to OAs or that interacted with OAs. To potentially identify those compounds, a synthetic unfermented juice (SUFJ) was developed containing the principal compounds found in the juice: vitamins, sugars, minerals, fibres, and fatty acids. The growth of *S. paradoxus* was significantly faster in an SSM containing the synthetic unfermented juice and the OA than in the SSM containing solely the OA (*p* < 0.01), but the difference in growth seen in the SSM containing the OA and the unfermented juice was not fully accounted for (*p* < 0.01). Moreover, the growth of *S. uvarum* and *R. glutinis* did not significantly differ between the SSM containing the synthetic unfermented juice and the OA and the SSM containing solely the OA (*p* > 0.8). Because the synthetic solution did not fully replicate the fermented juice, there may have been an effect by some other unidentified compounds.

## 4. Conclusion

This work confirms the relevance of primary and secondary models developed several decades ago for description and prediction of the behaviour of very diverse microorganisms in very diverse foods. Working with rarely-studied spoilage strains, conservative predictions on their growth potential (with mean error of 1.9–2.4 days for growth processes lasting for nearly two weeks) have been obtained with a proper model development; then validation in a real food matrix (here fresh strawberry puree). The diversity of the responses of microorganisms in the fresh strawberry purée environment was captured by tests on both collection and strawberry-isolated strains. Although this cannot be generalized to other applications, isolated strains behaved similarly to collection strains regarding growth and sensitivity to OAs.

Although the model effectively predicted OA-mediated inhibition, it was less effective with the biopreservation solution, likely due to other interfering compounds. In general terms, food microstructure and composition may influence the quality predictions of microbiological growth models (Pinon et al., 2004; Verheyen and Van Impe, 2021). These interfering compounds may include proteins, lipids, carbohydrates, cations (Davidson et al., 2013), or other bioactive compounds such as terpenes, alkaloids, and phenolics. For instance, citral has been shown to enhance *Pseudomonas aeruginosa* resistance to antibiotics (Tetard et al., 2021). To identify them, metabolomics or chromatographic fractionation methods could be employed (Caesar and Cech, 2019). The roles of these compounds must be investigated further and may range from protective effects on microorganisms to chemical interactions with OAs. For example, negatively charged proteins are supposed to interact with the positively charged chitosan, preventing chitosan from interacting with the surface of microorganisms (Devlieghere et al., 2004). Identifying these compounds and understanding their role could help to develop predictive models that are more based on the physicochemical properties of plant-based matrices, facilitating their application to diverse matrices and their use for the

development of biopreservation solutions.

### CRediT authorship contribution statement

**Clarisse Breard:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Camille Duc:** Validation, Supervision, Methodology, Formal analysis, Conceptualization. **Christian Ginies:** Writing – review & editing, Methodology, Formal analysis. **Cécile Grondin:** Writing – review & editing, Methodology, Formal analysis. **Aurélie Cendrès:** Validation, Supervision, Project administration, Methodology, Formal analysis, Conceptualization. **Isabelle Souchon:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Frédéric Carlin:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Investigation, Formal analysis, Conceptualization.

### Funding sources

This work was performed in collaboration with Atelier du Fruit (Cavaillon, France) and funded by INRAE and the French National Research and Technology Agency (ANRT-CIFRE 2021/1738).

### Declaration of competing interest

None.

### Acknowledgments

We would like to thank Jean-Luc Legras for his technical advice on strains selection and Doriane Dumont for her technical advice on OA quantification. We also thank Cyrielle Kalifa, Stéphanie Chamot and Stéphanie Oriol for skillful technical assistance. We want to thank Alain Etievant for funding. Finally, we thank Jessica Pierce for English proofreading.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijfoodmicro.2025.111396.

### Data availability

Research data are available at: Breard C. et al., "Research data to Breard et al. (2025), submitted", Recherche Data Gouv, V2, 2025, https://doi.org/10.57745/OTOHC8

Research data to Breard et al. (2025), Biopreservation strategies: can organic acid concentrations be sufficient to predict yeast and mould growth in strawberry purée?, submitted for publication to In (Original data) (Dataverse)

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