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no growth interfaces of table olive related yeasts for natamycin, citric acid and sodium chloride
International Journal of Food Microbiology 155 (2012) 257–262
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Short Communication
Growth/no growth interfaces of table olive related yeasts for natamycin, citric acid and sodium chloride F.N. Arroyo-López ⁎, J. Bautista-Gallego, V. Romero-Gil, F. Rodríguez-Gómez, A. Garrido-Fernández Departamento de Biotecnología de Alimentos, Instituto de la Grasa (CSIC), Avda. Padre García Tejero nº 4, 41012 Seville, Spain
a r t i c l e
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Article history: Received 31 May 2011 Received in revised form 21 December 2011 Accepted 8 February 2012 Available online 15 February 2012 Keywords: Table olives Natamycin Yeasts Preservatives Logistic model
a b s t r a c t The present work uses a logistic/probabilistic model to obtain the growth/no growth interfaces of Saccharomyces cerevisiae, Wickerhamomyces anomalus and Candida boidinii (three yeast species commonly isolated from table olives) as a function of the diverse combinations of natamycin (0–30 mg/L), citric acid (0.00–0.45%) and sodium chloride (3–6%). Mathematical models obtained individually for each yeast species showed that progressive concentrations of citric acid decreased the effect of natamycin, which was only observed below 0.15% citric acid. Sodium chloride concentrations around 5% slightly increased S. cerevisiae and C. boidinii resistance to natamycin, although concentrations above 6% of NaCl always favoured inhibition by this antimycotic. An overall growth/no growth interface, built considering data from the three yeast species, revealed that inhibition in the absence of citric acid and at 4.5% NaCl can be reached using natamycin concentrations between 12 and 30 mg/L for growth probabilities between 0.10 and 0.01, respectively. Results obtained in this survey show that is not advisable to use jointly natamycin and citric acid in table olive packaging because of the observed antagonistic effects between both preservatives, but table olives processed without citric acid could allow the application of the antifungal. © 2012 Elsevier B.V. All rights reserved.
1. Introduction The preservation of table olives can be achieved by diverse methods according to the international standard for table olives (IOOC, 2004). After a long stay in brine, cured olives are stable during commercialization but not completely fermented olives require heat treatment (pasteurization) or the addition of preservatives to prevent spoilage during shelf life (Garrido Fernández et al., 1997). Potassium sorbate and sodium benzoate are authorized in the CODEX standard for table olives (CODEX, 1987). However, their concentrations in brine progressively decrease with time due to solubilisation into the olive fat (Brenes et al., 2004) or degradation by microorganisms (Casas et al., 2004). This decrease favours the growth of spoiling microorganisms, mainly yeasts, and the production of gas and the subsequent inflating of containers, product rejections, and important economic losses (Arroyo López et al., 2008). Therefore, new preservatives with better performance in table olives are necessary. Natamycin (also known as pimaricin) is a polyene macrolide antimycotic produced by the bacterium Streptomyces natalensis, isolated originally from the soil in Natal (South Africa). However, other strains of Streptomyces are also reported to produce the same chemical compound. This antifungal has several advantages as a preservative because of its broad activity spectrum, efficacy at low levels, lack of resistance
⁎ Corresponding author. Tel.: + 34 954 692 516; fax: + 34 954 691 262. E-mail address: [email protected] (F.N. Arroyo-López). 0168-1605/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.ijfoodmicro.2012.02.007
development and activity over a wide pH range (Thomas and DelvesBroughton, 2003). In foods, it is used to prevent the growth of molds and yeasts in fruit juices, carbonated drinks and sausages but mainly in diverse types of cheeses. However, apparently it has no effect on bacteria (Thomas and Delves-Broughton, 2003). The mode of action of natamycin has been investigated recently. Yeast sterol biosynthetic mutants revealed the importance of the double bonds in the B-ring of ergosterol for the natamycin–ergosterol interaction and the consecutive block of fungal growth. Surprisingly, in strong contrast to nystatin and filipin, natamycin did not change the permeability of the yeast plasma membrane under conditions that growth was blocking (Welscher et al., 2008). Natamycin is habitually used at concentrations between 1 and 10 ppm (mg/L). In general, yeasts are less resistant (minimum inhibitory concentrations below 5 ppm) than molds (minimum inhibitory concentrations above 10 ppm) (Thomas and Delves-Broughton, 2003). Natamycin is permitted as an antimycotic in surface and cheese treatments in 32 countries but its use as a general food additive is more limited. In the European Union, natamycin (designated as preservative E235) is allowed for the surface treatment of hard, semi-hard, and semisoft cheeses as well as dry sausages (European Commission, 1995). The EFSA Panel on Food Additives and Nutrient Sources to Foods has revised its use, concluding that, given that natamycin is very poorly absorbed, there was an adequate margin of safety in its current applications and there was no concern for the induction of antimicrobial resistance (EFSA, 2009). Diverse works have shown its activity against Aspergillus flavus, A. carbonarius, A. niger, A. versicolor, Penicillium chrysogenum, P. glabrum,
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P. commune, P. verrucosum, Byssochlamys and other species of molds (Medina et al., 2007; Rusul and Marth, 1988; Stark, 2003). Natamycin (160 mg/L) inhibited the growth of A. flavus on whole olives for 21 days at 15 °C and for 7 days in olive paste (Mahjoub and Bullerman, 1986). However, studies on yeasts are more limited. York (1966) suggested that the addition of natamycin could retard the spoilage of acidified foods by Saccharomyces cerevisiae in combination with mild heat treatments. Gallo et al. (2006) studied the inactivation curves of S. cerevisiae treated with different levels of natamycin and inoculum sizes in liquid cheese whey. Apparently, a reduction of the yeast population occurred at concentrations of 12.5 mg/L and did not occur immediately but progressively during storage. Recently, Hondrodimou et al. (2011) found that the use of 100 mg/L of natamycin in black olive fermentation inhibited mold spoilage caused by the development of fungal mycelium on the surface of the brine during the traditional anaerobic fermentation system employed in Greece and could be an important component of a processing system to control fungal growth and enhance flavour by the growth of the indigenous population of lactic acid bacteria. Similarly, natamycin could also help to control the spoilage of fresh packed or cured seasoned table olives caused by yeasts (Arroyo-López et al., 2009). S. cerevisiae, Wickerhamomyces anomalus (previously called Pichia anomala) and Candida boidinii, are among the most frequently found yeast species in table olives (Arroyo López et al., 2008). Probabilistic models have been widely used to describe the growth/ no growth interfaces as a function of environmental hurdles (Presser et al., 1998; Ratkowsky and Ross, 1995). The position of the growth/ no growth boundaries of spoilage microorganisms are of interest in establishing conditions for product stabilization. A procedure of forward or backward stepwise regression, with some criteria to include or reject categorical or quantitative explanatory variables, their quadratic terms or their interactions, is normally included in most of the standard statistical software packages (Hajmeer and Basheer, 2003; Hosmer and Lemeshow, 2000). The main goal of this survey was to obtain the growth/no growth interfaces of S. cerevisiae, W. anomalus and C. boidinii for natamycin in combination with salt (NaCl) and citric acid, which was accomplished by means of a logistic/probabilistic model. The results obtained in this survey will be used to assess a possible application of this antimycotic as a preservative agent in table olive packaging.
2.2. Experimental design A full-factorial experimental design, resulting from the combination of 18 levels of natamycin (0, 1, 2, 3, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, and 30 mg/L), 4 levels of NaCl (3, 4, 5 and 6%) and 4 levels of citric acid (0, 0.15, 0.30 and 0.45%), was used in the present study. This made a total of 288 different levels, which were run in duplicate. Thus, a set of 576 data were obtained for each specific yeast cocktail. Natamycin was supplied by DSM Food Specialties (Barcelona, Spain). Required concentrations in the media were obtained from appropriate dilutions of the commercial product known as Delvocid, which has a 4% concentration (40,000 mg/L) of the active compound. Citric acid (99.9% purity) was purchased from Sigma-Aldrich (Steinheim, Germany). Required concentrations of this acid in the different media were obtained from appropriate dilutions of a sterile stock solution (40%). 2.3. Evaluating yeast growth Growth was recorded in a Bioscreen C automated spectrophotometer (Labsystem, Helsinki, Finland) at 30 °C for 100 h with a wideband filter (420–580 nm). Measurements were taken every 2 h after a preshaking of 5 s. The wells of the microplate were filled with 0.01 mL of the specific yeast cocktail suspension and 0.35 ml of YM medium (modified according to the experimental design), always reaching an initial optical density (OD) of approximately 0.2 (initial inoculum level of ~6.0 log10 CFU/mL). The inocula were always above the detection limit of the apparatus, which was determined by comparison with a previously established calibration curve (data not shown). Uninoculated wells for each experimental series were also included in the microplate to determine, and consequently subtract, the noise signal. For each well, growth (coded as 1) was assumed when an OD increase of 0.1 was observed with respect to the initial OD after subtraction of noise signal. On the contrary, no growth (coded as 0) was declared when the initial OD did not increase after 100 h of incubation. Thus, intermediate values were not obtained. Responses for each replicate were recorded independently, and the whole matrix was subjected to statistical analysis. When the experiments were over, randomly selected wells (which included both growth and no growth samples) were spread on YM agar plates and their counts estimated to corroborate growth/no growth assumption.
2. Material and methods 2.4. Logistic model development 2.1. Yeast strains and cocktail preparation A total of 10 yeast isolates, 3 S. cerevisiae strains (TOMC Y4, TOMC Y30 and TOMC Y17), 4 W. anomalus strains (TOMC Y2, TOMC Y10, TOMC Y11 and TOMC Y12) and 3 C. boidinii strains (TOMC Y5, TOMC Y13 and TOMC Y33), were used in the present study. All these yeasts were previously obtained from different table olive processing phases and identified by molecular methods (RFLP 5.8S ITS region and sequencing of D1 and D2 domains of 26S gene). They belong to the table olive microorganism collection (TOMC) of the Instituto de la Grasa (CSIC, Spain). The study of the effects of natamycin, salt and citric acid was carried out on a representative specific cocktail for each yeast species. Prior to each experiment, the different strains were inoculated separately into 5 ml of a Yeast–Malt–peptone–glucose broth medium (YM, Difco, Becton and Dickinson Company, Sparks, USA) and incubated at 30 °C for 48 h. Then the tubes were centrifuged at 9000 x g for 15 min and the pellets re-suspended separately into 5 ml of a sterile saline solution (0.9% w/v). To form the cocktails, the different strain suspensions belonging to the same yeast species were combined in the same proportion, reaching an inoculum level of approximately 7 log10 CFU/mL. In this way, 3 different yeast cocktails (S. cerevisiae, W. anomalus and C. boidinii) were obtained.
A logistic regression model relates the probability of occurrence of an event (Y) conditional on a vector (x) of explanatory variables (Hosmer and Lemeshow, 2000). The quantity p(x) = E(Y/x) represents the conditional mean of y (for example growth probability) given x (environmental factors) when the logistic distribution is used. Accordingly, the specific model built for the probability of yeast growth, including only three terms to simplify, would be: pðxÞ ¼ exp½β0 þ Sβi xi =ð1 þ expβ0 þ Sβi xi Þ
ð1Þ
And the logit transformation of p(x) is defined as: LogitðpÞ ¼ ln½pðxÞ=ð1−pðxÞÞ ¼ β0 þ Sβi xi þ ε
ð2Þ
where β0 and βi are the intercept and the coefficients of the polynomial function, respectively, xi are the respective variables and ε is a term for error. In our case the model also included quadratic, two way interactions, three way interaction and categorical variables (when appropriate) terms.
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The predicted survival probability, at each variable combination, may be estimated as: p^ ¼ expðlogitðpÞÞ=ð1 þ expðlogitðpÞÞ
ð3Þ
From the above equations, the growth/no growth interface for a selected probability ( p^ ) can be deduced by substituting logit (p) by the corresponding polynomial equation and plotting the resulting equation as a function of two or three variables, while maintaining the rest of them as predetermined levels. 2.5. Statistical analyses The logistic regression model described above was fitted to the growth/no growth data obtained for the three yeast cocktails using SYSTAT 12 software package (Systat Software Inc., 2007, Washington, USA), by introducing the following model into the logit regression module of the mentioned software: 2 2 2 Logitðp^ Þ ¼ Intercept þ S þ N þ C þ S þ N þ C þ S N þ S C þ N C þ S N C
ð4Þ where S, N, and C stand for salt (NaCl, %), natamycin (mg/L), and citric acid (%) concentrations, respectively. The output provided the intercept and the significant coefficients for the model (Eq. (4)). The automatic variable stepwise selection (maximum number of allowable runs set at 100) with forward option was used to choose the coefficients. Retained coefficients were selected with the option likelihood ratio test (chi-square difference). The log-likelihood ratio statistic (Hosmer and Lemeshow, 2000) was used to assess the importance of each of the explanatory variable on the response (growth/no growth data of yeast). This statistic indicates if the coefficients of the model are significantly different from zero. It takes into account the number of explanatory variables (degrees of freedom) used in the model and shows a chi-squared distribution. The McFadden's rho-squared (a transformation of the likelihood statistic) and Nagelkerke's R2 statistics (a modification of the Cox and Snell R-square to range from 0 to 1) were also used to measure the goodness of fit of the logistic model. They are intended to mimic the coefficient of determination R2. As both statistics range from 0 to 1, values closer to 1 indicate a better fit of the model, although these statistics must be used with caution because its meaning is not the same as in the ordinary least square regression (the proportion of variance explained). Overall hit rate (number of correct prediction divided by sample size), sensitivity (percent of correct prediction in the reference category), and sensibility (percent correct prediction in the given category) were also estimated. The coefficients of the logit model are the odds ratio (in natural log units) that an event would happen (growth, p) or would not happen (no growth, 1 − p) and represent the changes in the log of the odds due to one unit change in the quantitative variable or due to the change from one level with respect to the reference level for categorical variables. A more convenient way of interpreting these coefficients is through the odds ratio = exp(b), derived from the multiplicative form of logit (p). Then, exp(b) means the change in the odds ratio due to one unit change in the variable under study when no change in the others is introduced. In this case, a value of 1 (e0 = 1) indicates that the variable under study does not cause any effect on the odds ratio. Values below 1 indicate a decrease while values higher than 1 mean an increase. The predicted growth/no growth interfaces for specific levels of probability (p) were produced from logit ( p^ ) = ln(p/(1 − p) with STATISTICA 7.0 software package (StatSoft Inc, 2001, Tulsa, Okla, USA). The limits were estimated for probability values (p) between 0.10 and 0.01 and expressed in two dimensions graphs as a function of the explanatory variables.
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3. Results The total number of treatments analyzed in the present work was 1728 (576 for each yeast cocktail) and the distribution of the growth/ no growth data was 438/138, 480/96, and 502/74 for the S. cerevisiae, W. anomalus, and C. boidinii cocktails, respectively. As mentioned previously, the presence/absence of growth was determined by an increase of 0.1 with respect to the initial OD, but randomly selected wells were also spread on YM agar to corroborate growth/no growth assumption. This task was carried out by comparing counts after 100 h of incubation with respect to the initial inoculum level obtained in the Bioscreen wells (~6 log10 CFU/mL). In all analyzed cases, the assumption was satisfactorily confirmed by both methodologies (data not shown). The average pH of the media for the different citric acid levels was 5.70± 0.10 for the absence of citric acid, 3.74 ± 0.07 for 0.15% citric acid, 3.32 ± 0.05 for 0.30% citric acid, and 3.06 ± 0.04 for 0.45% citric acid. Thus, the addition of citric acid led to a subsequent decrease in the pH value of the medium. 3.1. Individual logistic models Mathematical models were species-dependent, which was previously deduced from a logistic regression fit considering yeast species as categorical variables (see Table S1 and Figure S1 in the supplementary material). For this reason, individual fits for each yeast cocktail were considered as the best choice for analyzing data. This also led to a reduction of the number of erroneous assignations by the logistic models (see footnotes in Tables S1, S2, S3 and S4, supplementary material). The individual model deduced for the S. cerevisiae cocktail retained all the terms included in the logistic equation, except the two way interaction S*C (Table S2). In this way, the model for the S. cerevisiae cocktail had the following equation: Logitðp^ Þ ¼ −19:30 þ 15:95 S−2:58 N þ 35:47 C−1:81 S 2
2
2
þ 0:03 N −79:07 C þ 0:17 S N þ 3:43 N C−0:42 S N C
ð5Þ
The growth/no growth interfaces for S. cerevisiae can be deduced from Eq. (5), solving for natamycin. A simple and intuitive graph of the effect of the natamycin concentration as a function of NaCl in the absence of citric acid is shown in Fig. 1. The model fit closely follows the actual growth/no growth events (square = growth, circle = no growth) in the absence of citric acid. The closeness of the interface for the three probability levels must also be emphasized. However, in media containing 0.15% citric acid, the model failed to adjust the
Fig. 1. Growth/no growth interfaces (p = 0.01, 0.05 and 0.10) of the S. cerevisiae cocktail as a function of natamycin and NaCl concentrations in the absence of citric acid.
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growth/no growth interface at high NaCl concentrations (Figure S2, supplementary material). Thus, as the concentration of citric acid increased, the natamycin activity loss was higher. Consequently, the number of wells with inhibition decreased and practically disappeared when its concentration reached 0.30% (Figure S2). Thus, in the higher citric acid concentrations, the model could not be fitted because of the lack of no growth data, especially at 0.45% (data not shown). The model deduced for the W. anomalus cocktail included all the linear, two way and tree way interactions and quadratic effects (except citric and salt square) (Table S3). The model fit was fairly adequate as deduced from the goodness of fit statistics and the pseudo R-squared. The model also produced a good discrimination between growth/no growth data; specificity was 0.917 (the highest in this study) while the sensitivity was also good (0.975), with an overall hit rate of 0.965. The model took the form: Logitðp^ Þ ¼ 182:75−24:25 S−10:33 N−453:54 C þ 0:072 N þ 1:11 S N þ 74:21 S C þ 28:18 N C−4:43 S NC
2
ð6Þ
The growth/no growth interfaces for the diverse citric acid concentrations were built by solving Eq. (6) for natamycin and substituting citric acid and logit (p) for their respective values. The overall response of this microorganism to natamycin in the presence of citric was similar to S. cerevisiae and the inhibitory action of the antifungal was progressively lost in the presence of increasing concentrations of acid (Figure S3). In the absence of citric acid, the interface closely followed the actual experimental trend and changed slightly for the diverse probabilities. Contrary to the salt effect on S. cerevisiae (also in the absence of citric acid), there was no growth stimulating effect at 5% NaCl, and 6% NaCl markedly favoured inhibition (Fig. 2). In the presence of 0.15% citric acid, it was also possible to build the growth/no growth interface, which was moved upward with respect to 0.00% citric. Only the interface corresponding to p = 0.10 could be drawn because, for lower p values, the interfaces were located outside of the environmental conditions range (Figure S3). Finally, at 0.30% citric acid, only a few no growth cases were observed, while at 0.45% there was not any inhibition treatment even in the presence of 30 mg/L of natamycin (data not shown). As a result of the above mentioned behavior of W. anomalus against natamycin, this yeast showed a slightly higher resistance to the antifungal in the absence of citric acid than S. cerevisiae. The model for the C. boidinii cocktail retained all variables, quadratic terms, two way interactions (except N*C) and the three way interaction. The model fit was also appropriate, according to the diverse parameters used to test the goodness of fit (Table S4). The specificity was 0.805 and the sensitivity 0.976 with an overall hit rate of 94.6%, similar to values
Fig. 2. Growth/no growth interfaces (p = 0.01, 0.05 and 0.10) of the W. anomalus cocktail as a function of natamycin and NaCl concentrations in the absence of citric acid.
observed in the specific fit model for the other two yeasts. The equation took the form: Logitðp^ Þ ¼ 17:67 þ 12:98 S−3:95 N þ 177:38 C−1:94 S 2
2
2
þ 0:04 N −46:82 C þ 0:28 S N−31:20 S C þ 0:36 S N C
ð7Þ
With respect to the growth/no growth interface for C. boidinii (Fig. 3), it must be emphasized that, overall, this species was slightly more resistant to natamycin than the other two yeasts. Therefore, the growth/no growth curves for the diverse levels of probability were moved upward with respect to the other yeasts. In addition, the shapes of such curves reflected that intermediate NaCl contents (between 4 and 5%) had a slight stimulating effect (required more natamycin), similarly to S. cerevisiae, but concentrations of 6% NaCl (and possibly above) had a marked inhibitory effect. However, the influence of citric acid was also outstanding and of the same strong intensity because the inhibitory effect of natamycin was markedly reduced as the citric acid contents were higher (Figure S4, supplementary material). The effects were more evident because the growth/no growth curves were already even more elevated in the absence of citric acid. In fact, in the presence of 0.15-0.45% citric acid, growth/no growth interfaces could not be built because practically all data were in the growth region. In Fig. 3, it is also observed that separation between growth/no growth interfaces for the diverse probabilities were also very close for C. boidinii. In general, the behaviors of S. cerevisiae and C. boidinii showed a rather similar inhibitory trend, although the last one was slightly more resistant to natamycin in the environmental conditions assayed. 3.2. Global logistic model Usually, in real fermentations, there is no presence of only one yeast species or strain, but a mixture of them. Therefore, a growth/no growth interface built in such a way that took into account the possible simultaneous presence of the three species is also of interest. Thus, a global model was deduced considering data from one hypothetic yeast cocktail composed by the different S. cerevisiae, W. anomalus, and C. boidinii strains, which should have the highest combined resistance against natamycin. In this model, 75% (1296 experiments) of the data, randomly chosen, were used to build the model while the other 25% (432 experiments) were used for model validation. The obtained logistic model was relatively simple because it only retained, significant at p≤0.05 or below, the linear and quadratic effects of NaCl and natamycin, the quadratic effect of citric acid and the interaction N*C (Table S5). The parameters used to assess the goodness of fit were all significant and a
Fig. 3. Growth/no growth interfaces (p = 0.01, 0.05 and 0.10) of the C. boidinii cocktail as a function of natamycin and NaCl concentrations in the absence of citric acid.
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Fig. 4. Growth/no growth overall interfaces at different probabilities (p = 0.01, 0.05 and 0.10) in the absence of citric acid built with 75% of data obtained from the three yeast cocktails.
good agreement between experimental data and model can be expected. However, the McFadden's rho-squared (0.6396) and the Nagelkerke's R-square (0.741) were slightly lower than those found in the individual models but specificity was moderately low (0.701). On the contrary, sensitivity (0.966) was of similar order to other fits. The overall hit ratio was also slightly low (91.9). These characteristics were fairly similar to those obtained using the categorical variable (Table S1). Validation data results were approximately the same (0.693, 0.957 and 90.8 for specificity, sensitivity and overall hit rate, respectively). The equation took the form: 2
Logitðp^ Þ ¼ −3:55 þ 6:29 S−0:80 N−0:72 S þ 0:01 2
2
N −14:46 C þ 1:08 N C
ð8Þ
The equation also looks fairly similar to that obtained considering yeast species as a categorical variable (Table S1). Due to the great number of data points for each combination of factors (NaCl, Natamycin, citric acid, and yeast species), a representation of the real growth/no growth points is impractical. Thus, only the interfaces are represented in Fig. 4. Within the range of the experimental variables used in this study, the interfaces for p = 0.10, 0.05 and 0.01 can only be represented for the experiments made in the absence of citric acid, while the interface for 0.15% citric acid was the only one that could be drawn for p = 0.10 (data not shown). In the absence of citric acid, the diverse interfaces represent the limits above which the growth of any of the yeasts under study can be inhibited at a predetermined probability. As usual, as the probability decreases, one should use more preservative or more salt level particularly above 6%. As two of the studied cocktails, S. cerevisiae and C. boidinii, showed stimulating effects around 5% NaCl, the overall model has also retained this shape in the overall growth/no growth interface curves.
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diverse proportions of citric acid and NaCl. The results show that in the absence of citric acid, yeasts were inhibited at fairly low concentrations of the antifungal (10–22 mg/L) in synthetic laboratory medium. The levels were higher than those obtained from the literature which reports values of 0.15 mg/L and 1.0–2.5 mg/L as minimum inhibitory concentrations for S. cerevisiae and Candida albicans, respectively, but similar to those reported by Gallo et al. (2006) (12.5 mg/L) for S. cerevisiae. The models also found a strong disturbing effect of citric acid against natamycin activity. In fact, 30 mg/L of natamycin, a level markedly above that required to inhibit olive yeasts in the absence of citric acid, could not assure stability in table olive packing using 0.40–0.50% of citric acid. The loss of natamycin activity may be due to its hydrolysis in the acid media, which pH decreased as the proportion of citric acid was higher. Spectrophotometric assays (Capitan-Vallvey, 2000) performed in our laboratory revealed a reduction of 75% absorbance at 305 nm (wavelength for maximum natamycin absorption) in a 0.45% citric acid aqueous solution after 2 weeks of incubation at 30 °C. This effect of citric acid was unexpected due to the scarce reports of this factor on natamycin activity and because most of the applications of natamycin in foods indicated that the principle was active through a wide range of pH (Smith, 2010; Thomas and Delves-Broughton, 2003). Each yeast species had its peculiar response with respect to the salt stress, and diverse inhibitory profiles were detected. The behaviors may be related to the different contents of ergosterol of the yeast cell membranes, which is the target compound for natamycin (Brick, 1976; Welscher et al., 2008). Martinez-Montañés et al. (2011) recently showed that the content of ergosterol in S. cerevisiae yeast cells decreased (approximately 40%) in response to the stress originated by high concentrations of NaCl (1 M). In our case, concentrations around 5% produced a slight antagonistic effect with natamycin on S. cerevisiae and C. boidinii, which could be due to a reduction in the ergosterol yeast content in both species at this NaCl level. However, no influence of salt contents below 6% on W. anomalus was noticed. In summary, this work has shown that natamycin at low concentrations can efficiently inhibit the growth of three yeast species widely found in table olives. The deduced growth/no growth interfaces indicated that the effect was species-dependent with slight differences among yeasts, so global models based on the most resistant species would also be advisable. The presence of salt and citric acid had a noticeable effect on this antimycotic. Therefore, physicochemical characteristics of the cover brines may play an essential role on the inhibitory effect of natamycin against yeasts in table olive storage or packaging. Acknowledgements This work was supported by the European Union (PROBIOLIVES, contract 243471), Spanish Government (projects AGL-2006-03540/ ALI, AGL2009-07436/ALI and AGL2010-15494/ALI, partially financed by European regional development funds, ERDF), CSIC (project 201070E058), and Junta de Andalucía (through financial support to group AGR-125). J. Bautista-Gallego and F.N. Arroyo-López want to thank CSIC for their JAE predoctoral fellowship and JAE-DOC postdoctoral research contract, respectively. We also thank D. Ricardo Lemos, from DSM Food Specialties, for providing commercial Delvocid for the experiments.
4. Discussion Appendix A. Supplementary data Although the inhibitory effect of natamycin on yeasts and molds is widely known (Thomas and Delves-Broughton, 2003), the interaction with different environmental factors has not yet been studied in detail. In table olives, Hondrodimou et al. (2011) assayed the use of natamycin as a fungal control agent in naturally black olive fermentation, reporting that brines containing 100 mg/L of natamycin were able to maintain the surface in contact with air free of fungal mycelium for 60 days. In this work, we have modelled the inhibitory effect of natamycin on three relevant yeast cocktails from table olives in combination with
Supplementary data to this article can be found online at doi:10. 1016/j.ijfoodmicro.2012.02.007. References Arroyo López, F.N., Querol, A., Bautista Gallego, J., Garrido Fernández, A., 2008. Role of yeast in table olive production. International Journal of Food Microbiology 128, 189–196. Arroyo-López, F.N., Bautista-Gallego, J., Segovia Bravo, K.A., García García, P., Durán Quintana, M.C., Romero, C., Rodríguez Gómez, F., Garrido-Fernández, A., 2009. Instability profile of
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fresh packed “seasoned” Manzanilla-Aloreña table olives. LWT- Food Science and Technology 42, 1629–1639. Brenes, M., Romero, C., García, P., Garrido, A., 2004. Absorption of sorbic and benzoic acids in the flesh of table olives. European Food Research and Technology 219, 75–79. Brick, H., 1976. New high molecular decomposition products of natamycin (pimaricin) with intact lactone-ring. The Journal of Antibiotics 6, 632–637. Capitan-Vallvey, L.F., 2000. Rapid ultraviolet spectrophotometric and liquid chromatographic methods for the determination of natamycin in lactoserum matrix. Journal of AOAC International 83, 802–808. Casas, E., De Ancos, M.J., Valderrama, P., Peinado, J.M., 2004. Pentadiene production from potassium sorbate by osmotolerant yeasts. International Journal of Food Microbiology 94, 93–96. CODEX, 1987. Codex Standard for Table Olives. Codex Stan 66–198 (Rev. 1–1987). Codex Alimentarius Commission. FAO/WHO, Rome. Italy. Commission, European, 1995. Directive (EC) nº 95/2 of 20 February on food additives other than colours and seweeteners. Off. J. Eur, Communities. L6. EFSA (European Food Safety Agency), 2009. Scientific opinion on the use of natamycin (E235) as a food additive. EFSA Journal 7, 1412. Gallo, L.I., Jagus, R.J., Piloasof, A.M., 2006. Modelling Saccharomyces cerevisiae inactivation by natamycin in liquid cheese whey. Brazilian Journal of Food Technology 9, 311–316. Garrido Fernández, A., Fernández Díez, M.J., Adams, R.M., 1997. Table olive. Production and processing. Chapman & Hall, London. Hajmeer, M., Basheer, I., 2003. Comparison of logistic regression and neural networkbased classifiers for bacterial growth. Food Microbiology 20, 43–55. Hondrodimou, O., Kourcoutas, Y., Panagou, E.Z., 2011. Efficacy of natamycin to control fungal growth in natural black olive fermentation. Food Microbiology 28, 621–627. Hosmer, D.W., Lemeshow, S., 2000. Applied logistic regression (second edition). John Willey & Sons, New York. IOOC (International Olive Oil Council), 2004. Trade Standard Applying to Table Olives. IOC/OT/NC nº1. December 2004. Madrid, Spain.
Mahjoub, A., Bullerman, B., 1986. Effects of natamycin and potassium sorbate on growth and aflatoxin production in olives. Archives of Institute Pasteur Tunis 63, 513–525. Martinez-Montañés, F., Pascual-Ahuir, A., Proft, M., 2011. Repression of ergosterol biosynthesis is essential for stress resistance and is mediated by the Hog1 MAP kinase and the Mot3 and Rox1 transcription factors. Molecular Microbiology 79, 1008–1023. Medina, A., Jiménez, M., Mateo, R., Magan, N., 2007. Efficacy of natamycin for control of growth and ochratoxin A production by Aspergillus carbonarius strains under different environmental conditions. Journal of Applied Microbiology 103, 2234–2239. Presser, K.A., Ross, T., Ratkowsky, D.A., 1998. Modelling the growth limits (growth/no growth interface) of Escherichia coli as a function of temperature, pH, lactic acid and water activity. Applied and Environmental Microbiology 64, 1773–1779. Ratkowsky, D.A., Ross, T., 1995. Modelling the bacterial growth/no growth interface. Letters in Applied Microbiology 20, 29–33. Rusul, G., Marth, E.H., 1988. Growth and aflatoxin production by Aspergillus parasiticus in a medium at different pH values and with and without pimaricin. European Food Research and Technology 187, 436–439. Smith, R.T., 2010. Beverage preservative system containing pimaricin-cyclodextrin complex. Patent with international publication number: WO 2010/148045 A1. Stark, J., 2003. Natamycin: an effective fungicide for food and beverages. In: Roller, S. (Ed.), Natural antimicrobials for the minimal processing of foods. Woodhead Publishing Limited, Cambridge, pp. 82–97. Thomas, L.V., Delves-Broughton, J., 2003. Natamycin. In: Caballero, G. (Ed.), Encyclopedia of Food Sciences and Nutrition. Academic Press, Elsevier Science and Technology, pp. 4110–4115. Welscher, Y.M., Napel, H.H., Balagué, M.M., Souza, C.M., Riezman, H., Kruijff, B., Breukink, E., 2008. Natamycin blocks fungal growth by binding specifically to ergosterol without permeabilizing the membrane. Journal of Biological Chemistry 283, 6393–9401. York, G.K., 1966. Effect of pimaricin on the resistance of Saccharomyces cerevisiae to heat, freezing, and ultraviolet irradiation. Applied Microbiology 14, 451–455.
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Short Communication
Growth/no growth interfaces of table olive related yeasts for natamycin, citric acid and sodium chloride F.N. Arroyo-López ⁎, J. Bautista-Gallego, V. Romero-Gil, F. Rodríguez-Gómez, A. Garrido-Fernández Departamento de Biotecnología de Alimentos, Instituto de la Grasa (CSIC), Avda. Padre García Tejero nº 4, 41012 Seville, Spain
a r t i c l e
i n f o
Article history: Received 31 May 2011 Received in revised form 21 December 2011 Accepted 8 February 2012 Available online 15 February 2012 Keywords: Table olives Natamycin Yeasts Preservatives Logistic model
a b s t r a c t The present work uses a logistic/probabilistic model to obtain the growth/no growth interfaces of Saccharomyces cerevisiae, Wickerhamomyces anomalus and Candida boidinii (three yeast species commonly isolated from table olives) as a function of the diverse combinations of natamycin (0–30 mg/L), citric acid (0.00–0.45%) and sodium chloride (3–6%). Mathematical models obtained individually for each yeast species showed that progressive concentrations of citric acid decreased the effect of natamycin, which was only observed below 0.15% citric acid. Sodium chloride concentrations around 5% slightly increased S. cerevisiae and C. boidinii resistance to natamycin, although concentrations above 6% of NaCl always favoured inhibition by this antimycotic. An overall growth/no growth interface, built considering data from the three yeast species, revealed that inhibition in the absence of citric acid and at 4.5% NaCl can be reached using natamycin concentrations between 12 and 30 mg/L for growth probabilities between 0.10 and 0.01, respectively. Results obtained in this survey show that is not advisable to use jointly natamycin and citric acid in table olive packaging because of the observed antagonistic effects between both preservatives, but table olives processed without citric acid could allow the application of the antifungal. © 2012 Elsevier B.V. All rights reserved.
1. Introduction The preservation of table olives can be achieved by diverse methods according to the international standard for table olives (IOOC, 2004). After a long stay in brine, cured olives are stable during commercialization but not completely fermented olives require heat treatment (pasteurization) or the addition of preservatives to prevent spoilage during shelf life (Garrido Fernández et al., 1997). Potassium sorbate and sodium benzoate are authorized in the CODEX standard for table olives (CODEX, 1987). However, their concentrations in brine progressively decrease with time due to solubilisation into the olive fat (Brenes et al., 2004) or degradation by microorganisms (Casas et al., 2004). This decrease favours the growth of spoiling microorganisms, mainly yeasts, and the production of gas and the subsequent inflating of containers, product rejections, and important economic losses (Arroyo López et al., 2008). Therefore, new preservatives with better performance in table olives are necessary. Natamycin (also known as pimaricin) is a polyene macrolide antimycotic produced by the bacterium Streptomyces natalensis, isolated originally from the soil in Natal (South Africa). However, other strains of Streptomyces are also reported to produce the same chemical compound. This antifungal has several advantages as a preservative because of its broad activity spectrum, efficacy at low levels, lack of resistance
⁎ Corresponding author. Tel.: + 34 954 692 516; fax: + 34 954 691 262. E-mail address: [email protected] (F.N. Arroyo-López). 0168-1605/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.ijfoodmicro.2012.02.007
development and activity over a wide pH range (Thomas and DelvesBroughton, 2003). In foods, it is used to prevent the growth of molds and yeasts in fruit juices, carbonated drinks and sausages but mainly in diverse types of cheeses. However, apparently it has no effect on bacteria (Thomas and Delves-Broughton, 2003). The mode of action of natamycin has been investigated recently. Yeast sterol biosynthetic mutants revealed the importance of the double bonds in the B-ring of ergosterol for the natamycin–ergosterol interaction and the consecutive block of fungal growth. Surprisingly, in strong contrast to nystatin and filipin, natamycin did not change the permeability of the yeast plasma membrane under conditions that growth was blocking (Welscher et al., 2008). Natamycin is habitually used at concentrations between 1 and 10 ppm (mg/L). In general, yeasts are less resistant (minimum inhibitory concentrations below 5 ppm) than molds (minimum inhibitory concentrations above 10 ppm) (Thomas and Delves-Broughton, 2003). Natamycin is permitted as an antimycotic in surface and cheese treatments in 32 countries but its use as a general food additive is more limited. In the European Union, natamycin (designated as preservative E235) is allowed for the surface treatment of hard, semi-hard, and semisoft cheeses as well as dry sausages (European Commission, 1995). The EFSA Panel on Food Additives and Nutrient Sources to Foods has revised its use, concluding that, given that natamycin is very poorly absorbed, there was an adequate margin of safety in its current applications and there was no concern for the induction of antimicrobial resistance (EFSA, 2009). Diverse works have shown its activity against Aspergillus flavus, A. carbonarius, A. niger, A. versicolor, Penicillium chrysogenum, P. glabrum,
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P. commune, P. verrucosum, Byssochlamys and other species of molds (Medina et al., 2007; Rusul and Marth, 1988; Stark, 2003). Natamycin (160 mg/L) inhibited the growth of A. flavus on whole olives for 21 days at 15 °C and for 7 days in olive paste (Mahjoub and Bullerman, 1986). However, studies on yeasts are more limited. York (1966) suggested that the addition of natamycin could retard the spoilage of acidified foods by Saccharomyces cerevisiae in combination with mild heat treatments. Gallo et al. (2006) studied the inactivation curves of S. cerevisiae treated with different levels of natamycin and inoculum sizes in liquid cheese whey. Apparently, a reduction of the yeast population occurred at concentrations of 12.5 mg/L and did not occur immediately but progressively during storage. Recently, Hondrodimou et al. (2011) found that the use of 100 mg/L of natamycin in black olive fermentation inhibited mold spoilage caused by the development of fungal mycelium on the surface of the brine during the traditional anaerobic fermentation system employed in Greece and could be an important component of a processing system to control fungal growth and enhance flavour by the growth of the indigenous population of lactic acid bacteria. Similarly, natamycin could also help to control the spoilage of fresh packed or cured seasoned table olives caused by yeasts (Arroyo-López et al., 2009). S. cerevisiae, Wickerhamomyces anomalus (previously called Pichia anomala) and Candida boidinii, are among the most frequently found yeast species in table olives (Arroyo López et al., 2008). Probabilistic models have been widely used to describe the growth/ no growth interfaces as a function of environmental hurdles (Presser et al., 1998; Ratkowsky and Ross, 1995). The position of the growth/ no growth boundaries of spoilage microorganisms are of interest in establishing conditions for product stabilization. A procedure of forward or backward stepwise regression, with some criteria to include or reject categorical or quantitative explanatory variables, their quadratic terms or their interactions, is normally included in most of the standard statistical software packages (Hajmeer and Basheer, 2003; Hosmer and Lemeshow, 2000). The main goal of this survey was to obtain the growth/no growth interfaces of S. cerevisiae, W. anomalus and C. boidinii for natamycin in combination with salt (NaCl) and citric acid, which was accomplished by means of a logistic/probabilistic model. The results obtained in this survey will be used to assess a possible application of this antimycotic as a preservative agent in table olive packaging.
2.2. Experimental design A full-factorial experimental design, resulting from the combination of 18 levels of natamycin (0, 1, 2, 3, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, and 30 mg/L), 4 levels of NaCl (3, 4, 5 and 6%) and 4 levels of citric acid (0, 0.15, 0.30 and 0.45%), was used in the present study. This made a total of 288 different levels, which were run in duplicate. Thus, a set of 576 data were obtained for each specific yeast cocktail. Natamycin was supplied by DSM Food Specialties (Barcelona, Spain). Required concentrations in the media were obtained from appropriate dilutions of the commercial product known as Delvocid, which has a 4% concentration (40,000 mg/L) of the active compound. Citric acid (99.9% purity) was purchased from Sigma-Aldrich (Steinheim, Germany). Required concentrations of this acid in the different media were obtained from appropriate dilutions of a sterile stock solution (40%). 2.3. Evaluating yeast growth Growth was recorded in a Bioscreen C automated spectrophotometer (Labsystem, Helsinki, Finland) at 30 °C for 100 h with a wideband filter (420–580 nm). Measurements were taken every 2 h after a preshaking of 5 s. The wells of the microplate were filled with 0.01 mL of the specific yeast cocktail suspension and 0.35 ml of YM medium (modified according to the experimental design), always reaching an initial optical density (OD) of approximately 0.2 (initial inoculum level of ~6.0 log10 CFU/mL). The inocula were always above the detection limit of the apparatus, which was determined by comparison with a previously established calibration curve (data not shown). Uninoculated wells for each experimental series were also included in the microplate to determine, and consequently subtract, the noise signal. For each well, growth (coded as 1) was assumed when an OD increase of 0.1 was observed with respect to the initial OD after subtraction of noise signal. On the contrary, no growth (coded as 0) was declared when the initial OD did not increase after 100 h of incubation. Thus, intermediate values were not obtained. Responses for each replicate were recorded independently, and the whole matrix was subjected to statistical analysis. When the experiments were over, randomly selected wells (which included both growth and no growth samples) were spread on YM agar plates and their counts estimated to corroborate growth/no growth assumption.
2. Material and methods 2.4. Logistic model development 2.1. Yeast strains and cocktail preparation A total of 10 yeast isolates, 3 S. cerevisiae strains (TOMC Y4, TOMC Y30 and TOMC Y17), 4 W. anomalus strains (TOMC Y2, TOMC Y10, TOMC Y11 and TOMC Y12) and 3 C. boidinii strains (TOMC Y5, TOMC Y13 and TOMC Y33), were used in the present study. All these yeasts were previously obtained from different table olive processing phases and identified by molecular methods (RFLP 5.8S ITS region and sequencing of D1 and D2 domains of 26S gene). They belong to the table olive microorganism collection (TOMC) of the Instituto de la Grasa (CSIC, Spain). The study of the effects of natamycin, salt and citric acid was carried out on a representative specific cocktail for each yeast species. Prior to each experiment, the different strains were inoculated separately into 5 ml of a Yeast–Malt–peptone–glucose broth medium (YM, Difco, Becton and Dickinson Company, Sparks, USA) and incubated at 30 °C for 48 h. Then the tubes were centrifuged at 9000 x g for 15 min and the pellets re-suspended separately into 5 ml of a sterile saline solution (0.9% w/v). To form the cocktails, the different strain suspensions belonging to the same yeast species were combined in the same proportion, reaching an inoculum level of approximately 7 log10 CFU/mL. In this way, 3 different yeast cocktails (S. cerevisiae, W. anomalus and C. boidinii) were obtained.
A logistic regression model relates the probability of occurrence of an event (Y) conditional on a vector (x) of explanatory variables (Hosmer and Lemeshow, 2000). The quantity p(x) = E(Y/x) represents the conditional mean of y (for example growth probability) given x (environmental factors) when the logistic distribution is used. Accordingly, the specific model built for the probability of yeast growth, including only three terms to simplify, would be: pðxÞ ¼ exp½β0 þ Sβi xi =ð1 þ expβ0 þ Sβi xi Þ
ð1Þ
And the logit transformation of p(x) is defined as: LogitðpÞ ¼ ln½pðxÞ=ð1−pðxÞÞ ¼ β0 þ Sβi xi þ ε
ð2Þ
where β0 and βi are the intercept and the coefficients of the polynomial function, respectively, xi are the respective variables and ε is a term for error. In our case the model also included quadratic, two way interactions, three way interaction and categorical variables (when appropriate) terms.
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The predicted survival probability, at each variable combination, may be estimated as: p^ ¼ expðlogitðpÞÞ=ð1 þ expðlogitðpÞÞ
ð3Þ
From the above equations, the growth/no growth interface for a selected probability ( p^ ) can be deduced by substituting logit (p) by the corresponding polynomial equation and plotting the resulting equation as a function of two or three variables, while maintaining the rest of them as predetermined levels. 2.5. Statistical analyses The logistic regression model described above was fitted to the growth/no growth data obtained for the three yeast cocktails using SYSTAT 12 software package (Systat Software Inc., 2007, Washington, USA), by introducing the following model into the logit regression module of the mentioned software: 2 2 2 Logitðp^ Þ ¼ Intercept þ S þ N þ C þ S þ N þ C þ S N þ S C þ N C þ S N C
ð4Þ where S, N, and C stand for salt (NaCl, %), natamycin (mg/L), and citric acid (%) concentrations, respectively. The output provided the intercept and the significant coefficients for the model (Eq. (4)). The automatic variable stepwise selection (maximum number of allowable runs set at 100) with forward option was used to choose the coefficients. Retained coefficients were selected with the option likelihood ratio test (chi-square difference). The log-likelihood ratio statistic (Hosmer and Lemeshow, 2000) was used to assess the importance of each of the explanatory variable on the response (growth/no growth data of yeast). This statistic indicates if the coefficients of the model are significantly different from zero. It takes into account the number of explanatory variables (degrees of freedom) used in the model and shows a chi-squared distribution. The McFadden's rho-squared (a transformation of the likelihood statistic) and Nagelkerke's R2 statistics (a modification of the Cox and Snell R-square to range from 0 to 1) were also used to measure the goodness of fit of the logistic model. They are intended to mimic the coefficient of determination R2. As both statistics range from 0 to 1, values closer to 1 indicate a better fit of the model, although these statistics must be used with caution because its meaning is not the same as in the ordinary least square regression (the proportion of variance explained). Overall hit rate (number of correct prediction divided by sample size), sensitivity (percent of correct prediction in the reference category), and sensibility (percent correct prediction in the given category) were also estimated. The coefficients of the logit model are the odds ratio (in natural log units) that an event would happen (growth, p) or would not happen (no growth, 1 − p) and represent the changes in the log of the odds due to one unit change in the quantitative variable or due to the change from one level with respect to the reference level for categorical variables. A more convenient way of interpreting these coefficients is through the odds ratio = exp(b), derived from the multiplicative form of logit (p). Then, exp(b) means the change in the odds ratio due to one unit change in the variable under study when no change in the others is introduced. In this case, a value of 1 (e0 = 1) indicates that the variable under study does not cause any effect on the odds ratio. Values below 1 indicate a decrease while values higher than 1 mean an increase. The predicted growth/no growth interfaces for specific levels of probability (p) were produced from logit ( p^ ) = ln(p/(1 − p) with STATISTICA 7.0 software package (StatSoft Inc, 2001, Tulsa, Okla, USA). The limits were estimated for probability values (p) between 0.10 and 0.01 and expressed in two dimensions graphs as a function of the explanatory variables.
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3. Results The total number of treatments analyzed in the present work was 1728 (576 for each yeast cocktail) and the distribution of the growth/ no growth data was 438/138, 480/96, and 502/74 for the S. cerevisiae, W. anomalus, and C. boidinii cocktails, respectively. As mentioned previously, the presence/absence of growth was determined by an increase of 0.1 with respect to the initial OD, but randomly selected wells were also spread on YM agar to corroborate growth/no growth assumption. This task was carried out by comparing counts after 100 h of incubation with respect to the initial inoculum level obtained in the Bioscreen wells (~6 log10 CFU/mL). In all analyzed cases, the assumption was satisfactorily confirmed by both methodologies (data not shown). The average pH of the media for the different citric acid levels was 5.70± 0.10 for the absence of citric acid, 3.74 ± 0.07 for 0.15% citric acid, 3.32 ± 0.05 for 0.30% citric acid, and 3.06 ± 0.04 for 0.45% citric acid. Thus, the addition of citric acid led to a subsequent decrease in the pH value of the medium. 3.1. Individual logistic models Mathematical models were species-dependent, which was previously deduced from a logistic regression fit considering yeast species as categorical variables (see Table S1 and Figure S1 in the supplementary material). For this reason, individual fits for each yeast cocktail were considered as the best choice for analyzing data. This also led to a reduction of the number of erroneous assignations by the logistic models (see footnotes in Tables S1, S2, S3 and S4, supplementary material). The individual model deduced for the S. cerevisiae cocktail retained all the terms included in the logistic equation, except the two way interaction S*C (Table S2). In this way, the model for the S. cerevisiae cocktail had the following equation: Logitðp^ Þ ¼ −19:30 þ 15:95 S−2:58 N þ 35:47 C−1:81 S 2
2
2
þ 0:03 N −79:07 C þ 0:17 S N þ 3:43 N C−0:42 S N C
ð5Þ
The growth/no growth interfaces for S. cerevisiae can be deduced from Eq. (5), solving for natamycin. A simple and intuitive graph of the effect of the natamycin concentration as a function of NaCl in the absence of citric acid is shown in Fig. 1. The model fit closely follows the actual growth/no growth events (square = growth, circle = no growth) in the absence of citric acid. The closeness of the interface for the three probability levels must also be emphasized. However, in media containing 0.15% citric acid, the model failed to adjust the
Fig. 1. Growth/no growth interfaces (p = 0.01, 0.05 and 0.10) of the S. cerevisiae cocktail as a function of natamycin and NaCl concentrations in the absence of citric acid.
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growth/no growth interface at high NaCl concentrations (Figure S2, supplementary material). Thus, as the concentration of citric acid increased, the natamycin activity loss was higher. Consequently, the number of wells with inhibition decreased and practically disappeared when its concentration reached 0.30% (Figure S2). Thus, in the higher citric acid concentrations, the model could not be fitted because of the lack of no growth data, especially at 0.45% (data not shown). The model deduced for the W. anomalus cocktail included all the linear, two way and tree way interactions and quadratic effects (except citric and salt square) (Table S3). The model fit was fairly adequate as deduced from the goodness of fit statistics and the pseudo R-squared. The model also produced a good discrimination between growth/no growth data; specificity was 0.917 (the highest in this study) while the sensitivity was also good (0.975), with an overall hit rate of 0.965. The model took the form: Logitðp^ Þ ¼ 182:75−24:25 S−10:33 N−453:54 C þ 0:072 N þ 1:11 S N þ 74:21 S C þ 28:18 N C−4:43 S NC
2
ð6Þ
The growth/no growth interfaces for the diverse citric acid concentrations were built by solving Eq. (6) for natamycin and substituting citric acid and logit (p) for their respective values. The overall response of this microorganism to natamycin in the presence of citric was similar to S. cerevisiae and the inhibitory action of the antifungal was progressively lost in the presence of increasing concentrations of acid (Figure S3). In the absence of citric acid, the interface closely followed the actual experimental trend and changed slightly for the diverse probabilities. Contrary to the salt effect on S. cerevisiae (also in the absence of citric acid), there was no growth stimulating effect at 5% NaCl, and 6% NaCl markedly favoured inhibition (Fig. 2). In the presence of 0.15% citric acid, it was also possible to build the growth/no growth interface, which was moved upward with respect to 0.00% citric. Only the interface corresponding to p = 0.10 could be drawn because, for lower p values, the interfaces were located outside of the environmental conditions range (Figure S3). Finally, at 0.30% citric acid, only a few no growth cases were observed, while at 0.45% there was not any inhibition treatment even in the presence of 30 mg/L of natamycin (data not shown). As a result of the above mentioned behavior of W. anomalus against natamycin, this yeast showed a slightly higher resistance to the antifungal in the absence of citric acid than S. cerevisiae. The model for the C. boidinii cocktail retained all variables, quadratic terms, two way interactions (except N*C) and the three way interaction. The model fit was also appropriate, according to the diverse parameters used to test the goodness of fit (Table S4). The specificity was 0.805 and the sensitivity 0.976 with an overall hit rate of 94.6%, similar to values
Fig. 2. Growth/no growth interfaces (p = 0.01, 0.05 and 0.10) of the W. anomalus cocktail as a function of natamycin and NaCl concentrations in the absence of citric acid.
observed in the specific fit model for the other two yeasts. The equation took the form: Logitðp^ Þ ¼ 17:67 þ 12:98 S−3:95 N þ 177:38 C−1:94 S 2
2
2
þ 0:04 N −46:82 C þ 0:28 S N−31:20 S C þ 0:36 S N C
ð7Þ
With respect to the growth/no growth interface for C. boidinii (Fig. 3), it must be emphasized that, overall, this species was slightly more resistant to natamycin than the other two yeasts. Therefore, the growth/no growth curves for the diverse levels of probability were moved upward with respect to the other yeasts. In addition, the shapes of such curves reflected that intermediate NaCl contents (between 4 and 5%) had a slight stimulating effect (required more natamycin), similarly to S. cerevisiae, but concentrations of 6% NaCl (and possibly above) had a marked inhibitory effect. However, the influence of citric acid was also outstanding and of the same strong intensity because the inhibitory effect of natamycin was markedly reduced as the citric acid contents were higher (Figure S4, supplementary material). The effects were more evident because the growth/no growth curves were already even more elevated in the absence of citric acid. In fact, in the presence of 0.15-0.45% citric acid, growth/no growth interfaces could not be built because practically all data were in the growth region. In Fig. 3, it is also observed that separation between growth/no growth interfaces for the diverse probabilities were also very close for C. boidinii. In general, the behaviors of S. cerevisiae and C. boidinii showed a rather similar inhibitory trend, although the last one was slightly more resistant to natamycin in the environmental conditions assayed. 3.2. Global logistic model Usually, in real fermentations, there is no presence of only one yeast species or strain, but a mixture of them. Therefore, a growth/no growth interface built in such a way that took into account the possible simultaneous presence of the three species is also of interest. Thus, a global model was deduced considering data from one hypothetic yeast cocktail composed by the different S. cerevisiae, W. anomalus, and C. boidinii strains, which should have the highest combined resistance against natamycin. In this model, 75% (1296 experiments) of the data, randomly chosen, were used to build the model while the other 25% (432 experiments) were used for model validation. The obtained logistic model was relatively simple because it only retained, significant at p≤0.05 or below, the linear and quadratic effects of NaCl and natamycin, the quadratic effect of citric acid and the interaction N*C (Table S5). The parameters used to assess the goodness of fit were all significant and a
Fig. 3. Growth/no growth interfaces (p = 0.01, 0.05 and 0.10) of the C. boidinii cocktail as a function of natamycin and NaCl concentrations in the absence of citric acid.
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Fig. 4. Growth/no growth overall interfaces at different probabilities (p = 0.01, 0.05 and 0.10) in the absence of citric acid built with 75% of data obtained from the three yeast cocktails.
good agreement between experimental data and model can be expected. However, the McFadden's rho-squared (0.6396) and the Nagelkerke's R-square (0.741) were slightly lower than those found in the individual models but specificity was moderately low (0.701). On the contrary, sensitivity (0.966) was of similar order to other fits. The overall hit ratio was also slightly low (91.9). These characteristics were fairly similar to those obtained using the categorical variable (Table S1). Validation data results were approximately the same (0.693, 0.957 and 90.8 for specificity, sensitivity and overall hit rate, respectively). The equation took the form: 2
Logitðp^ Þ ¼ −3:55 þ 6:29 S−0:80 N−0:72 S þ 0:01 2
2
N −14:46 C þ 1:08 N C
ð8Þ
The equation also looks fairly similar to that obtained considering yeast species as a categorical variable (Table S1). Due to the great number of data points for each combination of factors (NaCl, Natamycin, citric acid, and yeast species), a representation of the real growth/no growth points is impractical. Thus, only the interfaces are represented in Fig. 4. Within the range of the experimental variables used in this study, the interfaces for p = 0.10, 0.05 and 0.01 can only be represented for the experiments made in the absence of citric acid, while the interface for 0.15% citric acid was the only one that could be drawn for p = 0.10 (data not shown). In the absence of citric acid, the diverse interfaces represent the limits above which the growth of any of the yeasts under study can be inhibited at a predetermined probability. As usual, as the probability decreases, one should use more preservative or more salt level particularly above 6%. As two of the studied cocktails, S. cerevisiae and C. boidinii, showed stimulating effects around 5% NaCl, the overall model has also retained this shape in the overall growth/no growth interface curves.
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diverse proportions of citric acid and NaCl. The results show that in the absence of citric acid, yeasts were inhibited at fairly low concentrations of the antifungal (10–22 mg/L) in synthetic laboratory medium. The levels were higher than those obtained from the literature which reports values of 0.15 mg/L and 1.0–2.5 mg/L as minimum inhibitory concentrations for S. cerevisiae and Candida albicans, respectively, but similar to those reported by Gallo et al. (2006) (12.5 mg/L) for S. cerevisiae. The models also found a strong disturbing effect of citric acid against natamycin activity. In fact, 30 mg/L of natamycin, a level markedly above that required to inhibit olive yeasts in the absence of citric acid, could not assure stability in table olive packing using 0.40–0.50% of citric acid. The loss of natamycin activity may be due to its hydrolysis in the acid media, which pH decreased as the proportion of citric acid was higher. Spectrophotometric assays (Capitan-Vallvey, 2000) performed in our laboratory revealed a reduction of 75% absorbance at 305 nm (wavelength for maximum natamycin absorption) in a 0.45% citric acid aqueous solution after 2 weeks of incubation at 30 °C. This effect of citric acid was unexpected due to the scarce reports of this factor on natamycin activity and because most of the applications of natamycin in foods indicated that the principle was active through a wide range of pH (Smith, 2010; Thomas and Delves-Broughton, 2003). Each yeast species had its peculiar response with respect to the salt stress, and diverse inhibitory profiles were detected. The behaviors may be related to the different contents of ergosterol of the yeast cell membranes, which is the target compound for natamycin (Brick, 1976; Welscher et al., 2008). Martinez-Montañés et al. (2011) recently showed that the content of ergosterol in S. cerevisiae yeast cells decreased (approximately 40%) in response to the stress originated by high concentrations of NaCl (1 M). In our case, concentrations around 5% produced a slight antagonistic effect with natamycin on S. cerevisiae and C. boidinii, which could be due to a reduction in the ergosterol yeast content in both species at this NaCl level. However, no influence of salt contents below 6% on W. anomalus was noticed. In summary, this work has shown that natamycin at low concentrations can efficiently inhibit the growth of three yeast species widely found in table olives. The deduced growth/no growth interfaces indicated that the effect was species-dependent with slight differences among yeasts, so global models based on the most resistant species would also be advisable. The presence of salt and citric acid had a noticeable effect on this antimycotic. Therefore, physicochemical characteristics of the cover brines may play an essential role on the inhibitory effect of natamycin against yeasts in table olive storage or packaging. Acknowledgements This work was supported by the European Union (PROBIOLIVES, contract 243471), Spanish Government (projects AGL-2006-03540/ ALI, AGL2009-07436/ALI and AGL2010-15494/ALI, partially financed by European regional development funds, ERDF), CSIC (project 201070E058), and Junta de Andalucía (through financial support to group AGR-125). J. Bautista-Gallego and F.N. Arroyo-López want to thank CSIC for their JAE predoctoral fellowship and JAE-DOC postdoctoral research contract, respectively. We also thank D. Ricardo Lemos, from DSM Food Specialties, for providing commercial Delvocid for the experiments.
4. Discussion Appendix A. Supplementary data Although the inhibitory effect of natamycin on yeasts and molds is widely known (Thomas and Delves-Broughton, 2003), the interaction with different environmental factors has not yet been studied in detail. In table olives, Hondrodimou et al. (2011) assayed the use of natamycin as a fungal control agent in naturally black olive fermentation, reporting that brines containing 100 mg/L of natamycin were able to maintain the surface in contact with air free of fungal mycelium for 60 days. In this work, we have modelled the inhibitory effect of natamycin on three relevant yeast cocktails from table olives in combination with
Supplementary data to this article can be found online at doi:10. 1016/j.ijfoodmicro.2012.02.007. References Arroyo López, F.N., Querol, A., Bautista Gallego, J., Garrido Fernández, A., 2008. Role of yeast in table olive production. International Journal of Food Microbiology 128, 189–196. Arroyo-López, F.N., Bautista-Gallego, J., Segovia Bravo, K.A., García García, P., Durán Quintana, M.C., Romero, C., Rodríguez Gómez, F., Garrido-Fernández, A., 2009. Instability profile of
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fresh packed “seasoned” Manzanilla-Aloreña table olives. LWT- Food Science and Technology 42, 1629–1639. Brenes, M., Romero, C., García, P., Garrido, A., 2004. Absorption of sorbic and benzoic acids in the flesh of table olives. European Food Research and Technology 219, 75–79. Brick, H., 1976. New high molecular decomposition products of natamycin (pimaricin) with intact lactone-ring. The Journal of Antibiotics 6, 632–637. Capitan-Vallvey, L.F., 2000. Rapid ultraviolet spectrophotometric and liquid chromatographic methods for the determination of natamycin in lactoserum matrix. Journal of AOAC International 83, 802–808. Casas, E., De Ancos, M.J., Valderrama, P., Peinado, J.M., 2004. Pentadiene production from potassium sorbate by osmotolerant yeasts. International Journal of Food Microbiology 94, 93–96. CODEX, 1987. Codex Standard for Table Olives. Codex Stan 66–198 (Rev. 1–1987). Codex Alimentarius Commission. FAO/WHO, Rome. Italy. Commission, European, 1995. Directive (EC) nº 95/2 of 20 February on food additives other than colours and seweeteners. Off. J. Eur, Communities. L6. EFSA (European Food Safety Agency), 2009. Scientific opinion on the use of natamycin (E235) as a food additive. EFSA Journal 7, 1412. Gallo, L.I., Jagus, R.J., Piloasof, A.M., 2006. Modelling Saccharomyces cerevisiae inactivation by natamycin in liquid cheese whey. Brazilian Journal of Food Technology 9, 311–316. Garrido Fernández, A., Fernández Díez, M.J., Adams, R.M., 1997. Table olive. Production and processing. Chapman & Hall, London. Hajmeer, M., Basheer, I., 2003. Comparison of logistic regression and neural networkbased classifiers for bacterial growth. Food Microbiology 20, 43–55. Hondrodimou, O., Kourcoutas, Y., Panagou, E.Z., 2011. Efficacy of natamycin to control fungal growth in natural black olive fermentation. Food Microbiology 28, 621–627. Hosmer, D.W., Lemeshow, S., 2000. Applied logistic regression (second edition). John Willey & Sons, New York. IOOC (International Olive Oil Council), 2004. Trade Standard Applying to Table Olives. IOC/OT/NC nº1. December 2004. Madrid, Spain.
Mahjoub, A., Bullerman, B., 1986. Effects of natamycin and potassium sorbate on growth and aflatoxin production in olives. Archives of Institute Pasteur Tunis 63, 513–525. Martinez-Montañés, F., Pascual-Ahuir, A., Proft, M., 2011. Repression of ergosterol biosynthesis is essential for stress resistance and is mediated by the Hog1 MAP kinase and the Mot3 and Rox1 transcription factors. Molecular Microbiology 79, 1008–1023. Medina, A., Jiménez, M., Mateo, R., Magan, N., 2007. Efficacy of natamycin for control of growth and ochratoxin A production by Aspergillus carbonarius strains under different environmental conditions. Journal of Applied Microbiology 103, 2234–2239. Presser, K.A., Ross, T., Ratkowsky, D.A., 1998. Modelling the growth limits (growth/no growth interface) of Escherichia coli as a function of temperature, pH, lactic acid and water activity. Applied and Environmental Microbiology 64, 1773–1779. Ratkowsky, D.A., Ross, T., 1995. Modelling the bacterial growth/no growth interface. Letters in Applied Microbiology 20, 29–33. Rusul, G., Marth, E.H., 1988. Growth and aflatoxin production by Aspergillus parasiticus in a medium at different pH values and with and without pimaricin. European Food Research and Technology 187, 436–439. Smith, R.T., 2010. Beverage preservative system containing pimaricin-cyclodextrin complex. Patent with international publication number: WO 2010/148045 A1. Stark, J., 2003. Natamycin: an effective fungicide for food and beverages. In: Roller, S. (Ed.), Natural antimicrobials for the minimal processing of foods. Woodhead Publishing Limited, Cambridge, pp. 82–97. Thomas, L.V., Delves-Broughton, J., 2003. Natamycin. In: Caballero, G. (Ed.), Encyclopedia of Food Sciences and Nutrition. Academic Press, Elsevier Science and Technology, pp. 4110–4115. Welscher, Y.M., Napel, H.H., Balagué, M.M., Souza, C.M., Riezman, H., Kruijff, B., Breukink, E., 2008. Natamycin blocks fungal growth by binding specifically to ergosterol without permeabilizing the membrane. Journal of Biological Chemistry 283, 6393–9401. York, G.K., 1966. Effect of pimaricin on the resistance of Saccharomyces cerevisiae to heat, freezing, and ultraviolet irradiation. Applied Microbiology 14, 451–455.