Strategies to Increase the Stability of Intermediate Moisture Foods

Food Microbiology xxx (2014) 1e7

Contents lists available at ScienceDirect

Food Microbiology

journal homepage: www.elsevier.com/locate/fm

Strategies to increase the stability of intermediate moisture foods towards Zygosaccharomyces rouxii: The effect of temperature, ethanol, pH and water activity, with or without the inﬂuence of organic acids

A. Vermeulen a,b,1,2, C.L. Marvig c,1, J. Daelman b, R. Xhaferi a,b,2, D.S. Nielsen c, F. Devlieghere a,b,*,2 a CPMF 2e Flemish Cluster Predictive Microbiology in Foods, Belgium b LFMFP, Laboratory of Food Microbiology and Food Preservation, Department of Food Safety and Food Quality, Ghent University, Coupure Links 653, 9000 Ghent, Belgium c Section of Food Microbiology, Department of Food Science, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg, Denmark article info abstract

Article history: Intermediate moisture foods (IMF) are in general microbiologically stable products. However, due to Received 31 October 2013 health concerns consumer demands are increasingly forcing producers to lower the fat, sugar and pre- Received in revised form servatives content, which impede the stability of the IMF products. One of the strategies to counteract 31 December 2013 these problems is the storage of IMF products at lower temperatures. Accepted 3 January 2014 Thorough knowledge on growth/no growth boundaries of Zygosaccharomyces rouxii in IMF products, Available online xxx also at different storage temperatures is an important tool for ensuring microbiologically stability. In this study, growth/no growth models for Z. rouxii, developed by Vermeulen et al. (2012) were further Keywords: Zygosaccharomyces rouxii extended by incorporating the factor temperature. Three different data sets were build: (i) without Logistic regression organic acids, (ii) with acetic acid (10,000 ppm on product basis) and (iii) with sorbic acid (1500 ppm on Temperature product basis). For each of these data sets three different growth/no growth models were developed after 30, 60 and 90 days. The results show that the influence of temperature is only significant in the lower temperature range (8e15 C). Also, the effect of pH is negligible (pH 5.0e6.2) unless organic acids are present. More specific, acetic acid had only an additive effect to ethanol and aw at low pH, whereas sorbic acid had also an additive effect at the higher pH values. For incubation periods longer than 30 days the growth/no growth boundary remained stable but enlarged gradually between day 60 and 90, except for the lower temperature range (<12 C) where the boundary shifts to more stringent environmental conditions. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction more forced to lower the fat content, sugar content and use of preservatives. This can impede the stability of the products and Sweet intermediate moisture foods (IMF) such as jams, bakery cause spoilage of sweet IMF products by growth of osmophilic products, and chocolate ﬁllings, generally contain between 20 and yeasts and xerophilic moulds, leading to e.g. cracked chocolates. 50% (w/w) of water and a high amount of soluble compounds, Zygosaccharomyces rouxii is the most notorious spoilage yeast in which results in low water activity (aw) values of 0.70e0.90. sweet IMF products causing problems with visible growth and Consequently, these types of products are regarded as microbio- fermentative spoilage (Fleet, 1992; Jermini and Schmidt-Lorenz, logically stable. Due to health concerns and increased consumer 1987; Martorell et al., 2005). This yeast needs a certain amount of demands for preservative free products, producers are more and sugar (lowered water activity) to achieve optimal growth and is able to grow at relatively low water activities (>0.65) (Tokuoka, 1993). Besides this, the yeast is also very acid resistant and toler- * Corresponding author. LFMFP, Laboratory of Food Microbiology and Food ates low pH values (Praphailong and Fleet, 1997; Restaino et al., Preservation, Department of Food Safety and Food Quality, Ghent University, Cou- 1983; Vermeulen et al., 2012). pure Links 653, 9000 Ghent, Belgium. Tel.: þ32 9 264 61 77; fax: þ32 9 225 55 10. Growth/no growth (G/NG) models at 22 C were previously E-mail address: [email protected] (F. Devlieghere). 1 These authors contributed equally to the manuscript. developed for Z. rouxii including the factors aw, pH, ethanol and 2 http://www.cpmf2.be. acetic acid. However, it was demonstrated that at 22 C the

Table 1 temperature, next to pH, aw and ethanol. In addition, the effect of a Parameter estimates and the goodness-of-ﬁt statistics for the growth/no growth constant concentration of organic acids was tested leading to three models without organic. different data sets: (i) without organic acids, (ii) with acetic acid Parameters 30 days 60 days 90 days (10,000 ppm on product basis) and (iii) with sorbic acid Estimate St. dev. Estimate St. dev. Estimate St. dev. (1,500 ppm on product basis). For each of these data sets three different G/NG models were developed after 30, 60 as well as after aw 2177.3 168.8 1847.0 165.5 716.8 77.7 Eth 8.7 0.9 15.3 1.3 1.0 0.4 90 days. pH 36.8 7.8 18.6 5.6 19.9 4.5 Temp 2.1 0.1 4.9 0.5 4.6 0.3 2 aw 1302.5 102.0 974.4 92.9 372.7 47.1 2. Material and methods Eth2 0.03 0.02 0.05 0.01 0.07 0.005 pH2 2.4 0.6 1.8 0.5 1.9 0.4 2.1. Preparation of growth media Temp2 0.02 0.01 0.02 0.004 0.02 0.002

aw$Eth 8.4 1.0 19.3 1.7 1.9 0.5 aw$pH 13.3 4.6 eeee Sabouraud broth (Oxoid, Basingstoke, UK) was used as basic $ ee aw Temp 2.6 0.5 3.8 0.3 medium for the growth of Z. rouxii. This medium was modified to $ eeee Eth pH 0.2 0.06 mimic chocolate fillings as described in Vermeulen et al. (2012).In $ Eth Temp 0.1 0.02 0.12 0.01 0.04 0.004 e pH$Temp ee0.1 0.03 0.1 0.02 short, high amounts of sugar (50% (w/w), glucose (G-8270, Sigma Goodness-of-fit statistics Aldrich, Steinheim, Germany) and fructose (F-0127, SigmaeAldrich) in a 1:1 ratio) were added. The media were varying in (i) a (0.76e 2log L 937.89 1166.77 1947.27 w e AIC 961.89 1190.77 1971.27 0.88, four equidistant levels) by adding glycerol (Sigma Aldrich), HosmereLemeshow 26.422 19.055 63.881 (ii) pH (5.0e6.2, four equidistant levels) by adding HCl (UN 1789, c-Value 0.991 0.986 0.958 Merck, Darmstadt, Germany) (iii) ethanol concentration (0e4.5% % Correct predicted 95.6 95 90 (w/w) on total medium basis, four equidistant levels) (Merck). All these media were made without organic acids, with acetic acid microbial stability of sweet IMF cannot be guaranteed by simply (10,000 ppm on product basis) (SigmaeAldrich) or with sorbic acid lowering pH and aw. Even with the addition of ethanol and acetic (1,500 ppm on product basis) added as potassium sorbate (Sigmae acid, it was impossible to prevent growth of Z. rouxii at the highest Aldrich). The 10,000 ppm acetic acid on total medium basis was pH (6.2) and aw (0.88) tested (Vermeulen et al., 2012). Therefore, chosen to build up a data set at the same conditions as in the article the use of chemical preservatives seems to be inevitable or other of Vermeulen et al. (2012) at 22 C, only. To establish the water factors need to be taken into consideration. IMF producers involved activity, calibration curves were used describing aw as a function of in this study were interested in the effect of lowering the storage the added concentrations of glycerol to the modified Sabouraud temperature of e.g. chocolate fillings and chocolates, in order to medium (with glucose and fructose) (Vermeulen et al., 2012). The achieve longer shelf-lives or less product loss due to spoilage. exact aw value was determined by a Labmaster aw equipment Based on this, the present study extended the model developed (Novasina, Stork Intermes NV, Berchem, Belgium). The pH was by Vermeulen et al. (2012) by incorporating the factor measured by a pH electrode (Knick pH meter, Berlin, Germany).

Fig. 1. Growth/no growth boundary after 60 days of incubation at 22 C (A), 15 C (B) and 8 C (C) in media without acetic acid at pH 5.0 (thin lines) and 6.2 (bold lines). Lines represent the ordinary logistic regression model predictions p ¼ 0.9 (), p ¼ 0.5 (- -), p ¼ 0.1 (.).

Table 2 Parameter estimates and the goodness-of-ﬁt statistics for the growth/no growth models with acetic acid.

Parameters 30 days 60 days 90 days

Estimate St. dev. Estimate St. dev. Estimate St. dev.

aw 1573.167 384.529 2905.619 292.578 2335.246 298.865 Eth 0.671 2.619 5.497 1.254 17.466 2.659 pH 105.853 16.324 72.771 11.889 138.432 15.973 Temp 6.495 0.861 3.546 1.022 7.364 0.757 2 aw 1274.555 206.584 1669.194 172.157 1162.987 146.469 Eth2 0.102 0.024 ee0.031 0.015 pH2 ee6.310 0.992 8.460 0.751 Temp2 0.046 0.010 0.034 0.005 0.025 0.004

aw$Eth 6.052 2.499 4.399 1.308 14.611 2.182 aw$pH 115.205 19.473 ee45.253 12.261 aw$Temp ee5.681 0.958 7.453 0.847 Eth$pH 1.128 0.202 0.213 0.100 0.753 0.162 Eth$Temp eeee0.057 0.011 Fig. 2. Growth/no growth boundaries for the models with acetic acid (10,000 ppm) pH$Temp 1.620 0.148 0.502 0.086 ee (bold lines) and sorbic acid (1500 ppm) (thin lines) after 60 days of incubation at 22 C Goodness-of-fit statistics without addition of ethanol. Lines represent the ordinary logistic regression model predictions p ¼ 0.9 (), p ¼ 0.5 (- -), p ¼ 0.1 (.). 2log L 458.688 730.243 839.136 AIC 480.688 752.243 865.136 Hosmere 1177.607 94.656 157.515 Lemeshow Vermeulen et al. (2012). Briefly, 180 ml of the specific media were c-Value 0.996 0.990 0.986 added to 96-well microplates (TC-plates, Greiner Bio-one). To % Correct 97.5 93.6 95.3 avoid changes in the specific media due to the addition of the predicted inoculum the pre-culture was diluted to the appropriate inoculation level using the specific medium. Next, the inoculum culture (20 ml) was added to the microplates to obtain a final inoculation 2.2. Inoculum preparation and inoculation density of approximately 4.5 log CFU/ml. This inoculation density is as close as possible to the detection limit implying that any For model development the same strain was used as in growth, if possible, will be detected. The inoculum density was Vermeulen et al. (2012) (MUCL 44388, isolated from honey). The determined by plating on TSA (CM131, Oxoid) supplemented with strain was taken from a stock culture stored at 75 C, inoculated in 15% (w/v) sugar (7.5% fructose and 7.5% glucose). Finally the 5 ml Sabouraud medium and incubated at 30 C for 48 h. The purity microplates were first covered with a Breath-easy film (Fiers NV, of the culture was checked on Trypton Soy Agar (TSA, Oxoid) sup- Kuurne, Belgium) and afterwards closed with a lid. The lid was Ò plemented with 15% (w/v) glucose-fructose (1:1 ratio). To stan- sealed with silicone (RubsoneHenkel) and parafilm M (Novolab, dardize the inoculum a second subculture was taken and incubated Geraardsbergen, Belgium). The microplates were stored under at 30 C for 48 h. constant relative humidity by storing them in closed jars that were To determine growth or no growth optical measurement data filled for 1/10 with a glycerol-solution with the same aw as the were used. Therefore, the same procedure was used as in media in the plate.

2.3. Data generation for model development Table 3 Parameter estimates and the goodness-of-ﬁt statistics for the growth/no growth models with sorbic acid. All combinations of aw, pH and ethanol were tested in 16 replicates at four different temperatures (8, 15, 22 and 25 C). Separate Parameters 30 days 60 days 90 days data sets were generated for the model without organic acids, with Estimate St. dev. Estimate St. dev. Estimate St. dev. acetic acid or with sorbic acid. Only for the model with acetic acid,

aw 1954.372 304.112 1748.280 248.287 1689.772 147.010 combinations with the highest ethanol concentrations (14.5% in Eth 11.116 3.640 17.013 2.153 4.127 0.644 water phase) were not tested. The optical density (OD) of the media pH 331.679 191.989 113.915 33.065 9.079 0.525 was measured at 600 nm using a VERSAmaxÔ microplate reader Temp 7.635 1.235 9.013 0.986 4.654 0.543 2 (Molecular Devices, Sunnyvale, CA, USA). The wells were measured aw 1093.887 176.426 1092.443 141.480 963.465 86.273 Eth2 eeeeee for 90 days three times a week, leading to at least 20 data points for pH2 26.582 16.077 11.386 2.601 ee each growth curve. The data were processed by the software 2 Ò Temp 0.090 0.011 0.094 0.008 0.049 0.004 package SOFTmaxPRO (Molecular Devices). To deﬁne growth, the a $Eth ee7.097 2.335 ee w same procedure was used as in Vermeulen et al. (2012). If the dif- aw$pH ee41.291 14.757 ee aw$Temp 4.815 1.142 6.441 0.944 3.268 0.539 ference between the OD and the ODblank was consistently above Eth$pH 2.118 0.616 2.103 0.253 0.844 0.004 three times the standard deviation of the signal of the blank, this Eth$Temp eeeeee well was considered as showing growth. Data were automatically $ eeeeee Ò pH Temp processed using a custom Excel macro application. OD-growth Goodness-of-ﬁt statistics curves of all wells were visually checked to prevent false positive 2log L 336.656 476.444 934.387 results (e.g. air bubbles). AIC 354.656 496.444 950.387 Hosmere 0.085 2063.173 277.035 At the end of the incubation period, strain purity was checked by Lemeshow streaking out on TSA agar (CM131, Oxoid) supplemented with 15% c-Value 0.996 0.992 0.976 (w/v) sugar (7.5% fructose and 7.5% glucose). Wells that did not % Correct 98.6 98.0 95.5 show any turbidity were directly plated on the same agar to assess predicted whether inactivation occurred.

Fig. 3. Growth/no growth boundaries after 60 days of incubation at 22 C for models (A) without organic acids (bold lines) and with acetic acid (10,000 ppm) (thin lines) at pH 5.4 and (B) without organic acids (bold lines) and with sorbic acid (1,500 ppm) (thin lines) at pH 6.2. Grey area indication area of extrapolation for model with acetic acid. Lines represent the ordinary logistic regression model predictions p ¼ 0.9 (), p ¼ 0.5 (- -), p ¼ 0.1 (.).

2.4. Development of growth/no growth models 3. Results and discussion

The G/NG data were used to develop several G/NG models for Previous studies with Z. rouxii have shown that this yeast is very Z. rouxii with temperature, pH, aw and ethanol concentration as tolerant towards low water activity, pH and acetic acid (Tokuoka, explanatory variables, after 30, 60 days and 90 days of incubation. 1993; Restaino et al., 1983; Martorell et al., 2007; Jermini and Separate models were developed for the conditions without Schmidt-Lorenz, 1987; Vermeulen et al., 2012). This makes the organic acid, with acetic acid and with sorbic acid. In all cases an yeast notorious for many intermediate moisture foods, particularly ordinary logistic regression model was used to describe the data. these with high sugar content. As IMF producers are still looking for The equation of this model consists of a polynomial (right-hand alternative strategies to stabilize their products, an extension of the side) and logit (p) ¼ (left-hand side) with p the probability of existing models made by Vermeulen et al. (2012) was performed by growth (Eq. (1)). incorporating temperature and presence of sorbic acid as extra variables. As the previous study proved that incorporating time as 2 logitðpÞ¼b0 þ b1$pH þ b2$aw þ b3$Eth þ b4$ Temp þ b5$pH an explanatory variable in the model had more disadvantages (i.e. broadening of the growth/no growth boundary) than advantages þ $ 2 þ $ 2 þ $ 2 þ $ $ b6 aw b7 Eth b8 Temp b9 pH aw (i.e. predictions at intermediate time are possible), separate models þ b10$aw$Eth þ b11$aw$Temp þ b12$Temp$pH for 30, 60 and 90 days were performed (Vermeulen et al., 2012).

þ b13$Temp$Eth þ b14$pH$Eth 3.1. Effect of temperature (1) In this equation Eth (% (w/w)) is the ethanol concentration in the For the data set without organic acids three different models (30 water phase. aw, pH and Temp are, respectively the water activity, days, 60 days and 90 days) were fitted based on Eq. (1). The pH and temperature. The water phase was defined as the mass of parameter estimates with standard deviations, and the goodness- water added to the medium (Dang et al., 2011). bi (i ¼ 0, ., 14) are of-fit statistics are given in Table 1. From these results it can be the parameters to be estimated. The models were fitted in SPSS 21.0 deduced that for the model after 60 and 90 days the same variables (SPSS, Inc., Chicago IL., USA) using linear logistic regression, ac- were selected in the model equations by the stepwise procedure. cording to the procedure described in Vermeulen et al. (2007a). For the model at 30 days, less interaction terms with the variable Briefly, this implies that the main effects were forced to stay in the Temperature were selected. The goodness-of-fit statistics and final model equation independent on their p-value. The quadratic predictive power indicate a relatively high c-value and % predicted and interaction terms were selected by the forward stepwise pro- correct, particularly for the model after 30 and 60 days. Also the cedure, based on the significance of the likelihood-ratio criterion HosmereLemeshow statistic, 2 log L, and AIC value show relative (p ¼ 0.001). Model building stopped when no more variables met low values indicating a good fit of the model. Regarding the models entry or removal criteria. The predicted G/NG interfaces were without organic acids, the model after 90 days performs the worst Ò plotted in Matlab R2012b (Mathworks, Inc., Natick, MA, USA). for all the goodness-of-fit statistics and predictive power. This is Goodness-of-fit statistics considered were: (i) 2 log L with L mainly caused by the larger G/NG boundary leading to more the likelihood in its optimum, (ii) Akaike’s Information Criterion combinations of environmental conditions with partially growth in (AIC ¼2 (maximum log likelihood number of parameters in the 16 replicates. Within this G/NG boundary it is more difficult to model)) and (iii) HosmereLemeshow statistic. perform a correct prediction in contrast to conditions far from this The predictive power was measured by the % corrected pre- boundary. These latter conditions are clearly within the growth or dicted and by the c-value (the concordance index), which is equal to no growth zone and will, by consequence, improve the predictive the area under the ROC-curve (Receiver Operating Curve) (Agresti, power. 2002). All models showed that the effect of pH is very small (between Data that showed clear anomalies (e.g. the growth probability pH 5.0e6.2) if no organic acid are present in the substrate (Fig. 1). decreased by decreasing environmental stress conditions) were left This was observed at all temperatures, times and aw’s modelled. It is out. These unexpected growth data could be caused by biological in good correlation with previous studies where pH values below variability and/or experimental errors. After removing these data 2.5 (Membré et al., 1999) and 2.8 (Rojo et al., 2014) were necessary points the model was fitted again to obtain the final outcome. to change the length of the lag-phase for Z. rouxii strains even at

Table 4 c-Values of the models developed in this study and the study of Vermeulen et al., (2012) tested with the different datasets.

Time (days) 1% Acetic acid (on product bases) Data set Model

30 Absence A 0.994 0.968 30 Absence B 0.918 0.954 30 Presence A 0.996 0.898 30 Presence B 0.950 0.989 60 Absence A 0.965 0.982 60 Absence B 0.963 0.986 60 Presence A 0.996 0.864 60 Presence B 0.923 0.982

A: Data set/model incorporating acetic acid (Vermeulen et al., 2012). B: Data set/model incorporating temperature (present study).

the temperature further to refrigerated conditions (8 C) clearly reduced the growth zone (Fig. 1C), which indicates a large increase in the microbial stability of the product. At higher ethanol concentrations (9% (w/w) on the water phase) this major shift in the G/ NG boundary already occurs between 15 and 22 C. This proves that storing IMF products under refrigerated conditions will enhance the microbial stability substantially. However this implies a higher energy consumption and a monitoring of the cold chain, leading to higher costs. Besides, these refrigerated temperatures can lead to changes in the product itself, e.g. mouth feeling and formation of condense which should be further validated in some typical IMF products such as chocolate ﬁllings.

3.2. Effects of organic acids

For each of the data sets with organic acids (acetic acid and sorbic acid) three different models (30 days, 60 days and 90 days) were fitted based on Eq. (1). The parameter estimates with standard deviations, and the goodness-of-fit statistics are given in Tables 2and 3. The predictive power statistics indicate a high c- value and % predicted correct for the six models. The Hosmere Lemeshow statistic only indicates a good fit for the sorbic acid model at 30 days. However, this statistic can be largely influenced by one or a few bad predictions and, by consequence, it should be interpreted with caution (Agresti, 2002). The 2log L and AIC value show relative low values indicating a good fit of the model. Contrary to the models without organic acids, the effect of pH plays an important role in conditions where organic acids are present. In the medium containing 10,000 ppm acetic acid (21,000e 31000 ppm in water phase, depending on the aw of the medium), the G/NG boundary was reached only at low pH (<5.0) when incubated at 22 C(Fig. 2). Addition of sorbic acid, however, was more efficient in inhibiting growth of Z. rouxii and the G/NG boundary shifted to pH < Fig. 4. Growth/no growth boundaries for models without organic acid after incubation 5.4 at the same conditions of aw, temperature and ethanol (Fig 2). at 22 C with pH 5.4 and 0% ethanol for A) 30 days, B) 60 days and C) 90 days. Lines Fig. 2 also indicated that the effect of pH in combination with an represent the ordinary logistic regression model predictions p ¼ 0.9 (), p ¼ 0.5 (- -), organic acid (acetic or sorbic acid) is much more pronounced at the p ¼ 0.1 (.). lower aw’s(<0.80). At an aw of 0.76 the G/NG boundary was even reached by pH 6.0 when sorbic acid was present in the medium, low aw (0.788 and 0.744, respectively). High concentrations of sugar compared to pH 5.5 in the presence of acetic acid (Fig. 2). have been suggested to have a positive effect on the ability of some Comparing the models with organic acids shows that sorbic acid is yeasts to initiate growth at low pH (Praphailong and Fleet, 1997). much more efficient than acetic acid. The concentration of sorbic acid The addition of ethanol, however, significantly inhibited growth at used is much lower than the one of acetic acid and the G/NG boundary concentrations higher than 9% (w/w) in the water phase at tem- is situated in much less stringent conditions of pH, water activity and peratures of more than 15 C. At lower temperatures, lower temperature. It can be assumed that the molecular structure of sorbic amounts of ethanol could be used in order to achieve a microbio- acid leads to a higher antimicrobial activity. Besides, it is known that logically stable product. sorbic acid is also active against Z. rouxii in the dissociated (pH higher The model proved that lowering the temperature to 15 C had than pKa value) form and even activity at pH 6.5, has been reported almost no influence on the G/NG boundary, except at the lower (Stratford and Anslow,1996,1998). This can be due to the decrease in water activities. In this region a clear shift of the G/NG interface protoplast volume and, by consequence, changes in morphology could be observed between 15 and 22 C(Fig. 1A and B). Lowering induced by the presence of sorbic acid (Beales, 2004).

Fig. 5. Graphical representation of the model of Vermeulen et al. (2012) (thin lines) and the present model (bold lines) at 0 ppm acetic acid/9% ethanol (A) and 10,000 ppm acetic acid/% ethanol (B) both after 60 days incubation at 22 C.

Comparing the models without and with organic acids show three different storage times proves that storage longer than 30 that the G/NG boundary of the model without organic acid is spread days makes almost no difference for the G/NG boundary, except over a broader range of ethanol when the aw is decreased (Fig. 3). except at temperatures below 12 C(Fig. 4). At these lower tem- For the model at 10,000 ppm acetic acid, this large G/NG boundary peratures a significant shift in the G/NG boundary appears towards was observed at much higher aw (>0.80) (Fig. 3A), while for the lower water activities. After 90 days, the growth zone itself was not model with sorbic acid the G/NG boundary remained equidistant enlarged but a lot of conditions within the no growth zone showed over the whole aw range (Fig. 3B). partially growth. This led to a significant broadening of the G/NG At pH 5.4 the G/NG boundary for the model with acetic acid was boundary and, by consequence, a reduction of the no growth zone shifted towards lower ethanol concentrations compared to the (Fig. 4C). model without organic acids (Fig. 3A). The combined effect of sorbic In the presence of acetic acid and sorbic acid the same trends acid together with ethanol was even much more pronounced. In were observed at the lower temperatures with a major shift in the the presence of sorbic acid (1500 ppm) at pH 6.2 the G/NG G/NG boundary between 30 and 60 days. However, the broadening boundary was shifted from 10% to 4.5% ethanol and from 12% to 2% of the G/NG boundary after 90 days of incubation did not occur in ethanol at aw 0.88 and 0.76, respectively (Fig. 3B), indicating a the presence of acetic acid (data not shown). higher effect of sorbic acid and ethanol at lower aw’s. Summarized, it can be expected that the growth zone remains Regardless the clearly positive effect of organic acids and ethanol stable after 30 days of incubation, except for the temperatures on the microbial stability, using these additives also has some major below 12 C. This means that if IMF producers perform microbial drawbacks for food producers: (i) it can alter the taste of the product, stability tests at room temperature, the stability can be assured for a (ii) it should be labelled and/or (iii) it can be restricted by legislation. much longer shelf-life. However, using the model of 30 days at Acetic acid can be added quantum satis (EU 1129/2011) but is char- temperatures below 12 C can be at risk as growth can appear later acterized by a low taste threshold (0.2%) (Mojet et al., 2001). Besides, on during the shelf-life. the activityof acetic acid in the higher pH ranges of most IMF products (more than 5.7) is limited. Sorbic acid is within EU limited to 3.4. Validation with the models previously developed 1500 ppm (EU 1129/2011) and should be labelled on the product. Besides, it should be noted that in some cases Z. rouxii is able to As validation, a comparison was made between the previously metabolize sorbates to 1,3 pentadiene which has a petroleum-like developed model at 22 C with acetic acid incorporated as variable odour (Casas et al., 2004). Ethanol should be labelled if the product (Vermeulen et al., 2012) and the new developed model (with contains more than 1.2 volume-% according to EU 2000/13 (EU, 2000) temperature as variable included). Common conditions between and has also a relative low taste threshold (1e2%) (Mattes and the two data sets were the combinations of pH and ethanol, with DiMeglio, 2001). Besides, chocolate producers, for example, try to 10,000 ppm acetic acid or without acetic acid, incubated at 22 C for avoid ethanol in their products to increase the sales towards con- 30 and 60 days. Therefore, these data were selected from the sumers who avoid alcohol e.g. because of cultural reasons. However, complete data sets. To quantify the performance of the models on ethanol can be used in much higher concentrations in specific liqueur the different data sets, the c-values were calculated (Table 4) chocolates. These types of chocolates are by consequence less sus- (Vermeulen et al., 2007b). From these results it is clear that the ceptible to microbial spoilage. To the author’s knowledge, no research models can describe both data sets satisfyingly as the c-values are on this type of products (i.e. low water activity combined with high in most cases above 0.900. Obviously, the model, which was built ethanol concentrations) has been published to date. on the data-set, could describe these data the best. However, for the The effect of temperature was the same for the models data set from Vermeulen et al. (2012) without acetic acid and after regardless the presence of acetic acids. The largest effect of tem- 60 days of incubation the newly developed model incorporating perature was observed between 8 and 15 C. However, for the temperature showed a higher c-value than the original model. In model with sorbic acid this effect was observed over the whole general, the c-values are higher for the models and datasets range from 8 to 22 C. without acetic acid, which can also be deduced from the graphical comparison (Fig. 5). In the absence of acetic acid the G/NG 3.3. Effect of time boundary of the two models is located at the same aw’s. However, if 10,000 ppm acetic acid is present a larger deviation of the G/NG As expected the G/NG boundary becomes broader if time in- boundary can be found at the lower aw’s. This can be explained by creases as in more conditions, which are stressful for Z. rouxii the fact that in the new model acetic acid is kept as a constant (1% growth will eventually occur. The comparison of the models after on product basis) while it is included as a variable in the model

Please cite this article in press as: Vermeulen, A., et al., Strategies to increase the stability of intermediate moisture foods towards Zygosaccharomyces rouxii: The effect of temperature, ethanol, pH and water activity, with or without the influence of organic acids, Food Microbiology (2014), http://dx.doi.org/10.1016/j.fm.2014.01.003 A. Vermeulen et al. / Food Microbiology xxx (2014) 1e7 7 equation of the previous developed model (Vermeulen et al., 2012). Fleet, G., 1992. Spoilage yeasts. Crit. Rev. Biotechnol. 12, 1e44. Both models should still be validated in industrial made chocolate Jermini, M.F.G., Schmidt-Lorenz, W., 1987. Activity of Na-Benzoate and Ethyl- paraben against osmotolerant yeasts at different water activity values. J. Food filling. Prot. 50, 920e928. Martorell, P., Fernández-Espinar, M.T., Querol, A., 2005. Molecular monitoring of 4. Conclusions spoilage yeasts during the production of candied fruit nougats to determine food contamination sources. Int. J. Food Microbiol. 101, 293e302. Martorell, P., Stratford, M., Steels, H., Fernandez-Espinar, M.T., Querol, A., 2007. This study quantified the combined effect between pH, presence Physiological characterization of spoilage strains of Zygosaccharomyces bailii and Zygosaccharomyces rouxii isolated from high sugar environments. Int. J. of organic acids, ethanol and temperature within the lower aw Food Microbiol. 114, 234e242. ranges. By using these models IMF producers can easily predict the Mattes, R.D., DiMeglio, D., 2001. Ethanol perception and ingestion. Physiol. Behav. long term microbial stability of their products. This will be very 72, 217e229. important to speed up the development of innovative products Membré, J., Kubaczka, M., Chéné, C., 1999. Combined effects of pH and sugar on growth rate of Zygsaccharomyces rouxii, a bakery product spoilage yeast. Appl. with e.g. lower sugar or fat content. Storing IMF foods at refriger- Environ. Microbiol. 65, 4921e4925. ated temperatures will be beneficial by improving the microbial Mojet, J., Christ-Hazelhof, E., Heidema, J., 2001. Taste perception with age: generic stability. However, this also has consequences on the product itself or specific losses in threshold sensitivity to the five basic tastes? Chem. Senses e (texture, taste, mouth feeling, processability, etc.) and on the en- 26, 845 860. Praphailong, W., Fleet, G., 1997. The effect of pH, sodium chloride, sucrose, sorbate ergy requirements in the process chain of these products. This and benzoate on the growth of food spoilage yeasts. J. Food Microbiol. 14, aspect needs further investigation. 459e468. Restaino, L., Bills, S., Tscherneff, K., Lenovich, L.M., 1983. Growth characteristics of Saccharomyces rouxii isolated from chocolate syrup. Appl. Environ. Microbiol. Acknowledgement 45, 1614e1621. Rojo, M.C., Arroyo López, F.N., Lerena, M.C., Mercado, L., Torres, A., Combina, M., This research is supported by the Flemish Agency for Innovation 2014. Effects of pH and sugar concentration in Zygosaccharomyces rouxii growth and time for spoilage in concentrated grape juice at isothermal and non- by Science and Technology (IWT); project n 110193. The authors isothermal conditions. J. Food Microbiol. 38, 143e150. would also like to thank Mr. Chris Drieskens for developing the Stratford, M., Anslow, P., 1996. Comparison of the inhibitory action on Saccharo- Ò Excel macro enabling a fluent data processing. myces cerevisiae of weak-acid preservatives, uncouplers, and medium-chain fatty acids. FEMS Microbiol. Lett. 142, 53e58. Stratford, M., Anslow, P., 1998. Evidence that sorbic acid does not inhibit yeast as a References classic “weak acid preservative”. Lett. Appl. Microbiol. 27, 203e206. Tokuoka, 1993. Sugar-tolerant and salt-tolerant yeasts. J. Appl. Bacteriol. 74, 101e Agresti, A., 2002. Categorical Data Analysis. Wiley, New York. 110. Beales, N., 2004. Adaptation of microorganisms to cold temperatures, weak acid Vermeulen, A., Devlieghere, F., Bernaerts, K., Van Impe, J., Debevere, J., 2007a. preservatives, low pH, and osmotic stress: a review. Compr. Rev. Food Sci. Food Growth/no growth models describing the influence of pH, lactic and acetic acid Saf. 3, 1e10. on lactic acid bacteria developed to determine the stability of sauces. Int. J. Food Casas, E., de Ancos, B., Valderrama, M.J., Cano, P., Peinado, J.M., 2004. Pentadiene Microbiol. 119, 258e269. production from potassium sorbate by osmotolerant yeasts. Int. J. Food Vermeulen,A.,Smigic,N.,Rajkovic,A.,Gysemans,K.,Bernaerts,K., Microbiol. 94, 93e96. Geeraerd, A.H., Van Impe, J.F., Debevere, J., Devlieghere, F., 2007b. Perfor- Dang, T.D.T., Vermeulen, A., Mertens, L., Geeraerd, A.H., Van Impe, J.F., mance of a growth/no growth model for Listeria monocytogenes developed Devlieghere, F., 2011. The importance of expressing antimicrobial agents on for mayonnaise-based salads: influence of strain variability, food matrix, water basis in growth/no growth interface models: a case study for Zygo- inoculation level, and presence of sorbic and benzoic acid. J. Food Prot. 70, saccharomyces bailii. Int. J. Food Microbiol. 145, 258e266. 2118e2126. Directive 2000/13/EU of the European Parliament and of the Council of 20 March Vermeulen, A., Daelman, J., Van Steenkiste, J., Devlieghere, F., 2012. Screening of 2000 on the Approximation of the Laws of the Member States relating to the different stress factors and development of growth/no growth models for Labelling, Presentation and Advertising of Foodstuffs, 2000. Zygosaccharomyces rouxii in modified Sabouraud medium, mimicking inter- EU 1129/2011, 2011. Amending Annex II to Regulation 1333/2008/EC by Establishing mediate moisture foods (IMF). J. Food Microbiol. 32, 389e396. a Union List of Food Additives.