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Modeling the influence of water activity and ascospore age on the growth of Neosartorya fischeri in pineapple juice
LWT - Food Science and Technology 44 (2011) 239e243
Contents lists available at ScienceDirect
LWT - Food Science and Technology journal homepage: www.elsevier.com/locate/lwt
Modeling the influence of water activity and ascospore age on the growth of Neosartorya fischeri in pineapple juice Morgana Zimmermann a, Suzane Miorelli a, Pilar Rodriguez Massaguer b, Glaucia Maria Falcão Aragão a, * a
Department of Chemistry Engineering and Food Engineering, Federal University of Santa Catarina e UFSC, Campus Universitário, Cx. Postal 476, 88040-900, Florianópolis, SC, Brazil Department of Chemical Process, Faculty of Chemical Engineering, State University of Campinas e UNICAMP, Av. Albert Einstein, 500, Cx. Postal 6066, 13083-852, Campinas, SP, Brazil
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 4 June 2009 Received in revised form 29 June 2010 Accepted 30 June 2010
The ascospores of resistant fungi, Neosartorya fischeri, can survive commercial pasteurization, diminishing the shelf life of these products. The time that the ascospores remain in the environment and the effect that they can cause on mold growth are still unknown. This study is aimed to evaluate the influence of water activity (aw) from 0.90 to 0.99 and the ascospore age (I) from 30 to 90 days of vitro incubation on the growth of N. fischeri in pineapple juice by mathematical modeling. The growth parameters on pineapple juice: adaptation phase (l), maximum specific growth rate (mmax) and maximum diameter reached by the colony (A) were obtained by fitting Modified Gompertz and Logistic models to the experimental data. Both models were able to describe microbial growth in pineapple juice, but the Modified Gompertz model presented a slightly superior performance based on statistical indices (correlation coefficients (R2), mean square error (MSE), Bias Factor and Accuracy Factor). The minimum values of l and A, calculated by the Modified Gompertz model, were 64.7 h and 6.3 mm, while the maximum values were 178.2 h and 20.8 mm, respectively. The result showed that ascospore age did not influence the growth but aw was statistically significant to the growth parameters l and A. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Neosartorya fischeri Modeling of growth Water activity Ascospore age Pineapple juice
1. Introduction The majority of mold species present low heat resistance, having their vegetative structure (conidia and hyphae) easily destroyed by heat (Tournas, 1994). However, some mold species show characteristics that make them more heat resistant due to their capacity of ascospore production. The thermal resistant fungal species reported in fruit based products are: Byssochlamys nivea and Byssochlamys fulva, Neosartorya fischeri, Talaromyces sp. and Eupenicillium sp. (Hocking & Pitt, 1984; Splittstoesser, Kuss, & Harrison, 1970). Species like Byssochlamys sp. and N. fischeri are reported as mycotoxin producers (Pitt & Hocking, 1999, p. 593; Sant’Ana, Rosenthal, & Massaguer, 2008; Tournas, 1994). The preservation methods usually employed in the fruit juice industry are based on heat application. Therefore, the occurrence of contamination by heat resistant mold has often been detected in fruit juice during processing, in which the high temperatures used not only denature vegetative mold structures, but also activate dormant ascospores that may possibly be
* Corresponding author. Tel.: þ55 48 3721.9448; fax: þ55 48 3721.9687. E-mail address: [email protected] (G.M. Falcão Aragão). 0023-6438/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.lwt.2010.06.034
present. In this way, during juice storage, the mold can germinate and develop into the package, causing deterioration of the product, mycotoxin production and economic losses (Engel & Teuber, 1991). Spore age is defined as the time that the microorganism remains in dormant state. Slongo, Miorelli, and Aragão (2005) reported that ascospore age influences on the thermal resistance of N. fischeri but, in the literature, there are few studies that report whether this factor influences or not the growth of molds. Water activity is an intrinsic factor that has great influence on microorganism growth. The literature reports that the minimum aw necessary for the development of N. fischeri is 0.915 (Baglioni, Gumerato, & Massaguer, 1999). Predictive microbiology is based on the application of mathematical models to describe the behavior of microorganisms under different conditions of growth or inactivation. These models can predict the microbiological safety and the shelf life of products under commercial conditions (Zwietering, Koos, Hasenack, Wit, & Van’t Riet, 1991; Cheroutre-Vialete, Lebert, Hebraud, Labadie, & Lebert, 1998). The aim of this work was to determine the influence of water activity and ascospore age on the growth of N. fischeri in pineapple juice by mathematical modeling.
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M. Zimmermann et al. / LWT - Food Science and Technology 44 (2011) 239e243
2. Materials and methods
2.5. Mathematical modeling
2.1. Microorganisms and ascospore suspension
Primary models of growth of Modified Gompertz and Logistic were adjusted to the experimental data using the software Statistica 6.0. Equation (1) shows the Modified Gompertz model and Equation (2) shows the Logistic model.
The N. fischeri strain was isolated from the apple nectar processing facility and identified by Salomão, Massaguer, and Aragão (2008). First, N. fischeri was plated on Potato Dextrose Agar (PDA) and incubated for 7 days at 30 C. After that the colonies were suspended in sterile distilled water. Roux bottles containing 180 mL of Malt Extract Agar (MEA) were inoculated with 0.5 mL of this pre-suspension, following the procedure of Salomão, Slongo, and Aragão (2007) to promote spore production. The incubation was carried out at 30 C for 30, 51, 60, 69 and 90 days (ascospore ages (I)). The harvest of ascospores was performed using the methodology described by Salomão et al. (2007). After each desired age, ascospores were harvested by flooding the medium surface with 25 mL sterile water and gently rubbing the mold from the surface with a sterile glass rod and some glass pearls. The suspension was filtered through layers of gauze and centrifuged at 2000g for 15 min. It was washed three times with 25 mL sterile water, followed by centrifugation. The final suspension was prepared resuspending the precipitate in a small volume of water and stored under refrigeration at 4 C for future use. The ascospore concentration in the suspension was determined after thermal activation at 80 C for 30 min (Rajashekhara, Suresh, & Ethiraj, 1996), followed by serial dilutions and pour plating in PDA added to 50 mg/L rose bengal and acidified with a solution of 100 g/L tartaric acid until pH 3.5 (Baglioni et al., 1999). The plates were incubated at 30 C for 5 days. 2.2. Growth media All experiments were carried using commercial pineapple juice (pH 3.69 and 12.3 Brix) from the same brand and production lot. Sucrose and distilled water were used to adjust aw values to 0.90, 0.93, 0.95, 0.96 and 0.99. Water activity of the adjusted media was determined using an Aqualab (Models Series 3TE). The pineapple juice was submitted to heat treatment of 110 C for 15 min, followed by cooling to room temperature. 2.3. Preparation of inoculum Experiments were conducted in 250 mL PET (polyethylene terephthalate) bottles, previously sanitized with an aqueous solution of 3 mL/L of peracetic acid (P3 Oxônia Ativo 150, Ecolab), during 30 min. The caps were sanitized using 0.5 mL/L of the same product, with the same contact time. After the contact time, each bottle and cap were rinsed twice with sterile distilled water (Silva & Massaguer, 2005). The ascospore suspension was activated at 80 C for 30 min, and then inoculated into the juice in order to obtain a final concentration of approximately 1 ascospore/mL. This concentration was chosen considering the low incidence of thermal resistant molds in fruit products (Baglioni et al., 1999). 2.4. Growth monitoring The inoculated bottles were inclined 45 to increase the surface area and to facilitate the measurement of the colonies’ diameter. Bottles were stored at 30 C. The monitoring of N. fischeri growth was performed at least three times a day using the measurement of the radial growth, based on the method reported by Peña, Faria, and Massaguer (2004) and Valík and Piecková (2001), using a flashlight and a graduated ruler. Experiments were performed in duplicate.
ln y ¼ Aexp
ln y ¼
exp
mmax e A
ðl tÞ
A f1 þ expðD BtÞg
þ1
(1)
(2)
The diameter of the colony is y [mm] at a given time t [h], l is the length of adaptation phase [h]; mmax is the maximum specific growth rate [mm/h], A is the maximum diameter reached by the colony [mm], e is a constant (2.7182); D is a dimensionless parameter and B is the relative growth in half of the time of the exponential phase [h1]. D and B are used to determine the microbiological parameters of growth l and mmax (Equations (3) and (4)).
mmax ¼ l ¼
AB 4
(3)
ðD 2Þ B
(4)
General secondary models (power model presented on Equation (5), and linear model presented on Equation (6)) were used to describe the influence of the statistical significant factors on the growth parameters, using Excel software.
y ¼ axb
(5)
y ¼ ax þ b
(6)
The empirical parameters of the equation are a, b and c; x corresponds to values of aw or I; and y corresponds to values of l, mmax or A. 2.6. Statistical analysis For analysis of adjustment of the models to experimental data, the following statistical parameters were calculated: Correlation Coefficient (R2), Mean Square Error (MSE), Bias Factor and Accuracy Factor. The Correlation Coefficient (R2) represents the fraction of the variation that is explained. The higher value, the better the data are predicted by the model. The Mean Square Error (MSE) is given by Equation (7) and describes the error of the model compared to experimental data. The closer to zero, the better is the fit. The Bias Factor (Equation (8)) and the Accuracy Factor (Equation (9)) test the hypothesis that the model under evaluation predicts the true meaning or represents it better than another model. A Bias Factor < 1 indicates that the model is, in general, fail proof (Ross, 1996). The Bias Factor is an estimate of the average difference between observed and predicted values. The closer to 1, the better is the fit. The Accuracy Factor is the most suitable and accurate statistical parameter because it calculates the percentage of error in the prediction. This factor takes into consideration only the absolute values. The closer to 1, the lower is the percentage of error.
RSS MSE ¼ ¼ n P Bias ¼ 10
2 P vobserved vpredicted np
log
vobserved =vpredicted
. n
(7)
(8)
M. Zimmermann et al. / LWT - Food Science and Technology 44 (2011) 239e243
P jlog Accuracy ¼ 10
vobserved =vpredicted =n
(9)
The value of experimental data is given by vobserved; the value estimated by the model is given by vpredicted; n is the number of experimental observations and p is the number of parameters of the model. After the statistical analysis, the model that best fitted the experimental data was identified and the parameters of growth obtained by this model were submitted to analysis of variance (ANOVA) with a ¼ 0.05. Statistical analysis was performed using the software Statistica 6.0.
Table 1 Statistical parameters obtained for the fit of Modified Gompertz (MG) and Logistic (L) models to N. fischeri growth curves in pineapple juice when ascospore age was 30 days. Test
aw
Model
MSE
Bias factor
Accuracy factor
R2
1
0.90
2
0.93
3
0.95
4
0.96
5
0.99
GM L GM L GM L GM L GM L
0.033 0.050 0.029 0.023 0.030 0.030 0.037 0.028 0.023 0.015
1.003 0.997 1.002 1.004 0.999 0.998 0.999 0.997 0.997 0.991
1.003 1.001 1.002 1.007 1.005 1.003 1.006 1.003 1.005 1.004
0.990 0.989 0.995 0.993 0.992 0.988 0.996 0.994 0.979 0.976
3. Results Fig. 1 shows representative fits of Modified Gompertz (MG) and Logistic (L) models to experimental data. Similar behavior was observed for the other tested conditions. Through the visual analysis of Fig. 1, it can be noticed that both models fitted well to the experimental data. However, the Modified Gompertz model had a slightly superior performance on the transition between the adaptation phase and exponential growth phase. Table 1 presents an example of the statistical parameters obtained by fitting both models to growth curves. For all experimental data, the models have shown similar performance. It can be observed that both models showed good fit to the experimental data, since MSE was close to 0, and the Bias Factor, Accuracy Factor and Correlation Coefficient were close to 1. Although the models have shown very similar statistical parameters, the Modified Gompertz model was chosen because this model presented the best fit based on the analysis of R2 (data in boldface on Table 1). The statistical parameters, mainly R2, and the visual analysis of Fig. 1 show that this model provides the best prediction of growth of N. fischeri in pineapple juice. The standard deviation was not presented again on Figs. 2 and 3 in order to facilitate the visualization of the growth curves, but it followed the same proportion as presented on Fig. 1. The growth curves of N. fischeri in pineapple juice when aw was 0.95 at all ascospore ages studied are shown in Fig. 2. It is possible to see that the ascospore age showed no influence on growth parameters of N. fischeri, since the curves were almost overlapped. The observed variation on adaptation phase length cannot be direct related to ascospore ages. Similar behavior was observed in other conditions studied. The values of growth parameters l, mmax, and A
241
obtained from the Modified Gompertz model for all experimental conditions were submitted to the analysis of variance (ANOVA) with significance level a ¼ 0.05. Ascospore age did not show significant influence statistically on any parameter of growth in the range studied (p-value > 0.187 for all parameters). When the ascospore age was 51 days at all aw studied (Fig. 3), it can be observed that the increase of water activity caused an increase in the maximum diameter reached by the colony and, also, a reduction in the adaptation phase. These changes in aw facilitate the identification of the contaminant mold colony by naked eye in a very short time when compared to the commercial juice’s shelf life of 240 days. For example, at the condition of aw 0.99 the colony mold was identified in approximately 10 h of incubation at 30 C. Statistically, water activity presented significant influence on the growth parameters l and A, presenting a p-value equal 0.000 for both cases, but it did not show significant influence on the parameter mmax (p-value equal 0.142) in the range studied. As aw was statistically significant to the growth parameters l and A, secondary mathematical models (Equations (5) and (6)) were applied using the data previously obtained by primary modeling of the experimental data. Table 2 presents the average of all studied ages (30, 51, 60, 69 and 90 days) and standard deviation of l and A obtained in each aw condition. Equation (10) gives the mathematical relation between aw and l obtained by fitting the power model, as described by Corradini and Peleg (2005). This model showed a very good prediction, once the correlation coefficient (R2) obtained was 0.970.
l ¼ 55:88a10:33 w
(10)
3
ln Diameter
ln Diameter
2
1
2
1
0 0
50
100
150
200
Time (h)
0 0
50
100
150
200
250
300
350
Time (h) Fig. 1. Growth curve of N. fischeri in pineapple juice when water activity was adjusted to 0.95 and ascospore age was 60 days. The dotted line represents the fit of the Modified Gompertz model (MG) and the continuous line represents the fit of the Logistic model (L). The points (-) represent the average of the duplicate to each experimental data, and the bars represent the standard deviation. The diameter was measured in mm.
Fig. 2. Growth curves of N. fischeri in pineapple juice for ascospore ages of 30 (A), 51 (-), 60 (:), 69 (C) and 90 ( ) days, studied at aw of 0.95. The continuous line represents the fit of the Modified Gompertz model (MG) to the experimental data and the symbols represent the average of the duplicate to each experiment. The diameter was measured in mm.
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M. Zimmermann et al. / LWT - Food Science and Technology 44 (2011) 239e243
ln Diameter
3
2
1
0 0
50
100
150
200
250
300
350
Time (h) Fig. 3. Growth curves of N. fischeri in pineapple juice for aw conditions of 0.90 (A), 0.93 (-), 0.95 (:), 0.96 (C) and 0.99 ( ), studied at the ascospore age of 51 days. The continuous line represents the fit of Modified Gompertz model (MG) to the experimental data and the symbols represent the average of the duplicate to each experiment. The diameter was measured in mm.
The mathematical relation between aw and A is presented on Equation (11). This model also showed a good prediction, once the correlation coefficient (R2) obtained was 0.943.
A ¼ 161:84aw 139:05
(11)
4. Discussion The minimum diameter of the colony of N. fischeri growing in pineapple juice was identified by naked eye, measuring approximately 2 mm. According to Gibson, Baranyi, Pitt, Eyles, and Roberts (1994), the minimum size for viewing colonies is 3 mm, which is enough to cause rejection of the product by the consumer. This improvement in colony observation may be due to the use of a flashlight. Ascospore age did not showed statistically significant influence on any parameter of growth in the range studied. The effects of this factor on the heat inactivation of mold spores have been reported in the literature. Slongo and Aragão (2006) found that ascospore age influences the heat resistance of N. fischeri in papaya and pineapple juices. However, little information is available related to the effect of this factor on the growth of molds. Araujo and Rodrigues (2004) reported that young spores of Aspergillus fumigatus and Aspergillus niger showed higher germination rates than old spores, but spore age did not affected the germination of Aspergillus flavus. According to Dantigny and Nanguy (2009), there is a tendency of an increase of the germination time as the spore age is increased, although this behavior was not observed in this study. Water activity presented significant influence statistically on the growth parameters l and A, but it did not show significant influence statistically on the parameter mmax in the range studied. Romero, Patriarca, Fernández Pinto, and Vaamonde (2007), studying the growth of Aspergillus carbonarius, showed that aw was statistically Table 2 Average of l and A for all ascospore ages in each condition of aw during the growth of N. fischeri in pineapple juice. aw
l SD (h)
A SD (mm)
0.90 0.93 0.95 0.96 0.99
178.2 5.4 107.7 39.0 92.3 10.8 85.9 14.4 64.7 49.5
6.3 0.4 12.3 1.6 12.8 2.7 17.9 2.6 20.8 3.3
SD e standard deviation; aw e water activity; l e adaptation phase; A e diameter reached by the colony.
significant (a ¼ 0.05) on the adaptation phase. Bouras, Kim, and Strelkov (2009) reported that even under the most favorable temperature conditions (25 C), mold growth decreased dramatically when aw was reduced from 0.99 to 0.95. When the adaptation phase is finished, the growth of the microorganisms can hardly be controlled and the colony can soon be identified. From Table 2 is possible to observe that the increase of water activity led to a decrease on the length of the adaptation phase. The increase of water activity resulted in an increase of the colony size (Table 2). The magnitude of observed standard deviation, mainly for the parameter l, at aw ¼ 0.99, could be explained by the fast microorganism growth, which does this measurement difficult to perform. From the results obtained in this work, models to describe the influence of water activity on N. fischeri growth in pineapple juice were suggested. These models can be applied independently of the ascospore age, in the range of aw studied. References Araujo, R., & Rodrigues, A. G. (2004). Variability of germinative potential among pathogenic species of Aspergillus. Journal of Clinical Microbiology, 42, 4335e4337. Baglioni, F., Gumerato, H. F., & Massaguer, P. R. (1999). Ocorrência de fungos filamentosos termorresistentes em polpa de tomate envasada assepticamente. Ciência e Tecnologia de Alimentos, 19(2), 258e263. Bouras, N., Kim, Y. M., & Strelkov, S. E. (2009). Influence of water activity and temperature on growth and mycotoxin production by isolates of Pyrenophora tritici-repentis from wheat. International Journal of Food Microbiology, 131, 251e255. Cheroutre-Vialete, M., Lebert, I., Hebraud, M., Labadie, J. C., & Lebert, A. (1998). Effects of pH, aw and stress on growth of Listeria monocytogenes. International Journal of Food Microbiology, 42, 71e77. Corradini, M. G., & Peleg, M. (2005). Estimating non-isothermal bacterial growth in foods from isothermal experimental data. Journal of Applied Microbiology, 99(1), 187e200. Dantigny, P., & Nanguy, S. P. (2009). Significance of the physiological state of fungal spores. International Journal of Food Microbiology, 134, 16e20. Engel, G., & Teuber, M. (1991). Heat resistance of Byssochlamys nivea in milk and cream. International Journal of Food Microbiology, 12, 225e234. Gibson, A. M., Baranyi, J., Pitt, J. I., Eyles, M. J., & Roberts, T. A. (1994). Predicting fungal growth: the effect of water activity on Aspergillus flavus and related species. International Journal of Food Microbiology, 23, 419e431. Hocking, A. D., & Pitt, J. I. (1984). Food spoilage fungi. II. Heat resistant fungi. CSIRO Division of Food Research, North Ryde, N.S.W., 2113, 44(4), 73e82. Peña, W., Faria, J. A., & Massaguer, P. R. (2004). Development of predictive model on the growth of the spoilage mould, Paecilomyces variotti in pineapple juice. Fruit Processing, 14(6), 420e426. Pitt, J. I., & Hocking, A. D. (1999). Fungi and food spoilage (2nd ed.). Gaithersburg: Aspen Publishers Inc. Rajashekhara, E., Suresh, E. R., & Ethiraj, S. (1996). Influence of different heating media on thermal resistance of Neosartorya fischeri isolated from papaya fruit. Journal of Applied Bacteriology, 81, 337e340. Romero, S. M., Patriarca, A., Fernández Pinto, V., & Vaamonde, G. (2007). Effect of water activity and temperature on growth of ochratoxigenic strains of Aspergillus carbonarius isolated from Argentinean dried vine fruits. International Journal of Food Microbiology, 115, 140e143. Ross, T. (1996). Indices for performance evaluation of predictive models in food microbiology. Journal of Applied Bacteriology, 81, 501e508. Salomão, B. C. M., Massaguer, P. R., & Aragão, G. M. F. (2008). Isolation and selection of heat resistant molds in the production process of apple nectar. Ciência e Tecnologia de Alimentos, 28, 116e121. Salomão, B. C. M., Slongo, A. P., & Aragão, G. M. F. (2007). Heat resistance of Neosartorya fischeri in various juices. LWT e Food Science and Technology, 40, 676e680. Sant’Ana, A. S., Rosenthal, A., & Massaguer, P. R. (2008). The fate of patulin in apple juice processing: a review. Food Research International, 41, 441e453. Silva, A.R., Massaguer, P.R. (2005). Radial growth modelling of Aspergillus niger in mango nectar as a function of aw, pH, and temperature in PET package. As presented at: V Congresso Iberoamericano de Ingenieria de Alimentos CIBIA V, 04 a 07/09/2005, Puerto Vallarta, Jalisco, Mexico. Slongo, A. P., & Aragão, G. M. F. (2006). Factors affecting the thermal activation of Neosartorya fischeri in pineapple and papaya nectars. Brazilian Journal of Microbiology, 37, 266e270. Slongo, A. P., Miorelli, S., & Aragão, G. M. F. (2005). Influência de diferentes fatores na termorresistência de Neosartorya fischeri em suco de mamão. Alimentos e Nutrição e Araraquara, 16(4), 377e387.
M. Zimmermann et al. / LWT - Food Science and Technology 44 (2011) 239e243 Splittstoesser, D. F., Kuss, F. R., & Harrison, W. (1970). Enumeration of Byssochlamys and other heat-resistant molds. Applied Microbiology, 20, 393e397. Tournas, V. (1994). Heat resistant fungi of importance to the food and beverage industry. Critical Review Microbiology, 20(4), 243e263.
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Valík, L., & Piecková, E. (2001). Growth modeling of heat resistant fungi: the effect of water activity. International Journal of Food Microbiology, 63, 11e17. Zwietering, M. H., Koos, J. T., Hasenack, B. E., Wit, J. C., & Van’t Riet, K. (1991). Modeling of bacterial growth as a function of temperature. Applied and Environmental Microbiology, 57(4), 1094e1101.
Contents lists available at ScienceDirect
LWT - Food Science and Technology journal homepage: www.elsevier.com/locate/lwt
Modeling the influence of water activity and ascospore age on the growth of Neosartorya fischeri in pineapple juice Morgana Zimmermann a, Suzane Miorelli a, Pilar Rodriguez Massaguer b, Glaucia Maria Falcão Aragão a, * a
Department of Chemistry Engineering and Food Engineering, Federal University of Santa Catarina e UFSC, Campus Universitário, Cx. Postal 476, 88040-900, Florianópolis, SC, Brazil Department of Chemical Process, Faculty of Chemical Engineering, State University of Campinas e UNICAMP, Av. Albert Einstein, 500, Cx. Postal 6066, 13083-852, Campinas, SP, Brazil
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 4 June 2009 Received in revised form 29 June 2010 Accepted 30 June 2010
The ascospores of resistant fungi, Neosartorya fischeri, can survive commercial pasteurization, diminishing the shelf life of these products. The time that the ascospores remain in the environment and the effect that they can cause on mold growth are still unknown. This study is aimed to evaluate the influence of water activity (aw) from 0.90 to 0.99 and the ascospore age (I) from 30 to 90 days of vitro incubation on the growth of N. fischeri in pineapple juice by mathematical modeling. The growth parameters on pineapple juice: adaptation phase (l), maximum specific growth rate (mmax) and maximum diameter reached by the colony (A) were obtained by fitting Modified Gompertz and Logistic models to the experimental data. Both models were able to describe microbial growth in pineapple juice, but the Modified Gompertz model presented a slightly superior performance based on statistical indices (correlation coefficients (R2), mean square error (MSE), Bias Factor and Accuracy Factor). The minimum values of l and A, calculated by the Modified Gompertz model, were 64.7 h and 6.3 mm, while the maximum values were 178.2 h and 20.8 mm, respectively. The result showed that ascospore age did not influence the growth but aw was statistically significant to the growth parameters l and A. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Neosartorya fischeri Modeling of growth Water activity Ascospore age Pineapple juice
1. Introduction The majority of mold species present low heat resistance, having their vegetative structure (conidia and hyphae) easily destroyed by heat (Tournas, 1994). However, some mold species show characteristics that make them more heat resistant due to their capacity of ascospore production. The thermal resistant fungal species reported in fruit based products are: Byssochlamys nivea and Byssochlamys fulva, Neosartorya fischeri, Talaromyces sp. and Eupenicillium sp. (Hocking & Pitt, 1984; Splittstoesser, Kuss, & Harrison, 1970). Species like Byssochlamys sp. and N. fischeri are reported as mycotoxin producers (Pitt & Hocking, 1999, p. 593; Sant’Ana, Rosenthal, & Massaguer, 2008; Tournas, 1994). The preservation methods usually employed in the fruit juice industry are based on heat application. Therefore, the occurrence of contamination by heat resistant mold has often been detected in fruit juice during processing, in which the high temperatures used not only denature vegetative mold structures, but also activate dormant ascospores that may possibly be
* Corresponding author. Tel.: þ55 48 3721.9448; fax: þ55 48 3721.9687. E-mail address: [email protected] (G.M. Falcão Aragão). 0023-6438/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.lwt.2010.06.034
present. In this way, during juice storage, the mold can germinate and develop into the package, causing deterioration of the product, mycotoxin production and economic losses (Engel & Teuber, 1991). Spore age is defined as the time that the microorganism remains in dormant state. Slongo, Miorelli, and Aragão (2005) reported that ascospore age influences on the thermal resistance of N. fischeri but, in the literature, there are few studies that report whether this factor influences or not the growth of molds. Water activity is an intrinsic factor that has great influence on microorganism growth. The literature reports that the minimum aw necessary for the development of N. fischeri is 0.915 (Baglioni, Gumerato, & Massaguer, 1999). Predictive microbiology is based on the application of mathematical models to describe the behavior of microorganisms under different conditions of growth or inactivation. These models can predict the microbiological safety and the shelf life of products under commercial conditions (Zwietering, Koos, Hasenack, Wit, & Van’t Riet, 1991; Cheroutre-Vialete, Lebert, Hebraud, Labadie, & Lebert, 1998). The aim of this work was to determine the influence of water activity and ascospore age on the growth of N. fischeri in pineapple juice by mathematical modeling.
240
M. Zimmermann et al. / LWT - Food Science and Technology 44 (2011) 239e243
2. Materials and methods
2.5. Mathematical modeling
2.1. Microorganisms and ascospore suspension
Primary models of growth of Modified Gompertz and Logistic were adjusted to the experimental data using the software Statistica 6.0. Equation (1) shows the Modified Gompertz model and Equation (2) shows the Logistic model.
The N. fischeri strain was isolated from the apple nectar processing facility and identified by Salomão, Massaguer, and Aragão (2008). First, N. fischeri was plated on Potato Dextrose Agar (PDA) and incubated for 7 days at 30 C. After that the colonies were suspended in sterile distilled water. Roux bottles containing 180 mL of Malt Extract Agar (MEA) were inoculated with 0.5 mL of this pre-suspension, following the procedure of Salomão, Slongo, and Aragão (2007) to promote spore production. The incubation was carried out at 30 C for 30, 51, 60, 69 and 90 days (ascospore ages (I)). The harvest of ascospores was performed using the methodology described by Salomão et al. (2007). After each desired age, ascospores were harvested by flooding the medium surface with 25 mL sterile water and gently rubbing the mold from the surface with a sterile glass rod and some glass pearls. The suspension was filtered through layers of gauze and centrifuged at 2000g for 15 min. It was washed three times with 25 mL sterile water, followed by centrifugation. The final suspension was prepared resuspending the precipitate in a small volume of water and stored under refrigeration at 4 C for future use. The ascospore concentration in the suspension was determined after thermal activation at 80 C for 30 min (Rajashekhara, Suresh, & Ethiraj, 1996), followed by serial dilutions and pour plating in PDA added to 50 mg/L rose bengal and acidified with a solution of 100 g/L tartaric acid until pH 3.5 (Baglioni et al., 1999). The plates were incubated at 30 C for 5 days. 2.2. Growth media All experiments were carried using commercial pineapple juice (pH 3.69 and 12.3 Brix) from the same brand and production lot. Sucrose and distilled water were used to adjust aw values to 0.90, 0.93, 0.95, 0.96 and 0.99. Water activity of the adjusted media was determined using an Aqualab (Models Series 3TE). The pineapple juice was submitted to heat treatment of 110 C for 15 min, followed by cooling to room temperature. 2.3. Preparation of inoculum Experiments were conducted in 250 mL PET (polyethylene terephthalate) bottles, previously sanitized with an aqueous solution of 3 mL/L of peracetic acid (P3 Oxônia Ativo 150, Ecolab), during 30 min. The caps were sanitized using 0.5 mL/L of the same product, with the same contact time. After the contact time, each bottle and cap were rinsed twice with sterile distilled water (Silva & Massaguer, 2005). The ascospore suspension was activated at 80 C for 30 min, and then inoculated into the juice in order to obtain a final concentration of approximately 1 ascospore/mL. This concentration was chosen considering the low incidence of thermal resistant molds in fruit products (Baglioni et al., 1999). 2.4. Growth monitoring The inoculated bottles were inclined 45 to increase the surface area and to facilitate the measurement of the colonies’ diameter. Bottles were stored at 30 C. The monitoring of N. fischeri growth was performed at least three times a day using the measurement of the radial growth, based on the method reported by Peña, Faria, and Massaguer (2004) and Valík and Piecková (2001), using a flashlight and a graduated ruler. Experiments were performed in duplicate.
ln y ¼ Aexp
ln y ¼
exp
mmax e A
ðl tÞ
A f1 þ expðD BtÞg
þ1
(1)
(2)
The diameter of the colony is y [mm] at a given time t [h], l is the length of adaptation phase [h]; mmax is the maximum specific growth rate [mm/h], A is the maximum diameter reached by the colony [mm], e is a constant (2.7182); D is a dimensionless parameter and B is the relative growth in half of the time of the exponential phase [h1]. D and B are used to determine the microbiological parameters of growth l and mmax (Equations (3) and (4)).
mmax ¼ l ¼
AB 4
(3)
ðD 2Þ B
(4)
General secondary models (power model presented on Equation (5), and linear model presented on Equation (6)) were used to describe the influence of the statistical significant factors on the growth parameters, using Excel software.
y ¼ axb
(5)
y ¼ ax þ b
(6)
The empirical parameters of the equation are a, b and c; x corresponds to values of aw or I; and y corresponds to values of l, mmax or A. 2.6. Statistical analysis For analysis of adjustment of the models to experimental data, the following statistical parameters were calculated: Correlation Coefficient (R2), Mean Square Error (MSE), Bias Factor and Accuracy Factor. The Correlation Coefficient (R2) represents the fraction of the variation that is explained. The higher value, the better the data are predicted by the model. The Mean Square Error (MSE) is given by Equation (7) and describes the error of the model compared to experimental data. The closer to zero, the better is the fit. The Bias Factor (Equation (8)) and the Accuracy Factor (Equation (9)) test the hypothesis that the model under evaluation predicts the true meaning or represents it better than another model. A Bias Factor < 1 indicates that the model is, in general, fail proof (Ross, 1996). The Bias Factor is an estimate of the average difference between observed and predicted values. The closer to 1, the better is the fit. The Accuracy Factor is the most suitable and accurate statistical parameter because it calculates the percentage of error in the prediction. This factor takes into consideration only the absolute values. The closer to 1, the lower is the percentage of error.
RSS MSE ¼ ¼ n P Bias ¼ 10
2 P vobserved vpredicted np
log
vobserved =vpredicted
. n
(7)
(8)
M. Zimmermann et al. / LWT - Food Science and Technology 44 (2011) 239e243
P jlog Accuracy ¼ 10
vobserved =vpredicted =n
(9)
The value of experimental data is given by vobserved; the value estimated by the model is given by vpredicted; n is the number of experimental observations and p is the number of parameters of the model. After the statistical analysis, the model that best fitted the experimental data was identified and the parameters of growth obtained by this model were submitted to analysis of variance (ANOVA) with a ¼ 0.05. Statistical analysis was performed using the software Statistica 6.0.
Table 1 Statistical parameters obtained for the fit of Modified Gompertz (MG) and Logistic (L) models to N. fischeri growth curves in pineapple juice when ascospore age was 30 days. Test
aw
Model
MSE
Bias factor
Accuracy factor
R2
1
0.90
2
0.93
3
0.95
4
0.96
5
0.99
GM L GM L GM L GM L GM L
0.033 0.050 0.029 0.023 0.030 0.030 0.037 0.028 0.023 0.015
1.003 0.997 1.002 1.004 0.999 0.998 0.999 0.997 0.997 0.991
1.003 1.001 1.002 1.007 1.005 1.003 1.006 1.003 1.005 1.004
0.990 0.989 0.995 0.993 0.992 0.988 0.996 0.994 0.979 0.976
3. Results Fig. 1 shows representative fits of Modified Gompertz (MG) and Logistic (L) models to experimental data. Similar behavior was observed for the other tested conditions. Through the visual analysis of Fig. 1, it can be noticed that both models fitted well to the experimental data. However, the Modified Gompertz model had a slightly superior performance on the transition between the adaptation phase and exponential growth phase. Table 1 presents an example of the statistical parameters obtained by fitting both models to growth curves. For all experimental data, the models have shown similar performance. It can be observed that both models showed good fit to the experimental data, since MSE was close to 0, and the Bias Factor, Accuracy Factor and Correlation Coefficient were close to 1. Although the models have shown very similar statistical parameters, the Modified Gompertz model was chosen because this model presented the best fit based on the analysis of R2 (data in boldface on Table 1). The statistical parameters, mainly R2, and the visual analysis of Fig. 1 show that this model provides the best prediction of growth of N. fischeri in pineapple juice. The standard deviation was not presented again on Figs. 2 and 3 in order to facilitate the visualization of the growth curves, but it followed the same proportion as presented on Fig. 1. The growth curves of N. fischeri in pineapple juice when aw was 0.95 at all ascospore ages studied are shown in Fig. 2. It is possible to see that the ascospore age showed no influence on growth parameters of N. fischeri, since the curves were almost overlapped. The observed variation on adaptation phase length cannot be direct related to ascospore ages. Similar behavior was observed in other conditions studied. The values of growth parameters l, mmax, and A
241
obtained from the Modified Gompertz model for all experimental conditions were submitted to the analysis of variance (ANOVA) with significance level a ¼ 0.05. Ascospore age did not show significant influence statistically on any parameter of growth in the range studied (p-value > 0.187 for all parameters). When the ascospore age was 51 days at all aw studied (Fig. 3), it can be observed that the increase of water activity caused an increase in the maximum diameter reached by the colony and, also, a reduction in the adaptation phase. These changes in aw facilitate the identification of the contaminant mold colony by naked eye in a very short time when compared to the commercial juice’s shelf life of 240 days. For example, at the condition of aw 0.99 the colony mold was identified in approximately 10 h of incubation at 30 C. Statistically, water activity presented significant influence on the growth parameters l and A, presenting a p-value equal 0.000 for both cases, but it did not show significant influence on the parameter mmax (p-value equal 0.142) in the range studied. As aw was statistically significant to the growth parameters l and A, secondary mathematical models (Equations (5) and (6)) were applied using the data previously obtained by primary modeling of the experimental data. Table 2 presents the average of all studied ages (30, 51, 60, 69 and 90 days) and standard deviation of l and A obtained in each aw condition. Equation (10) gives the mathematical relation between aw and l obtained by fitting the power model, as described by Corradini and Peleg (2005). This model showed a very good prediction, once the correlation coefficient (R2) obtained was 0.970.
l ¼ 55:88a10:33 w
(10)
3
ln Diameter
ln Diameter
2
1
2
1
0 0
50
100
150
200
Time (h)
0 0
50
100
150
200
250
300
350
Time (h) Fig. 1. Growth curve of N. fischeri in pineapple juice when water activity was adjusted to 0.95 and ascospore age was 60 days. The dotted line represents the fit of the Modified Gompertz model (MG) and the continuous line represents the fit of the Logistic model (L). The points (-) represent the average of the duplicate to each experimental data, and the bars represent the standard deviation. The diameter was measured in mm.
Fig. 2. Growth curves of N. fischeri in pineapple juice for ascospore ages of 30 (A), 51 (-), 60 (:), 69 (C) and 90 ( ) days, studied at aw of 0.95. The continuous line represents the fit of the Modified Gompertz model (MG) to the experimental data and the symbols represent the average of the duplicate to each experiment. The diameter was measured in mm.
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M. Zimmermann et al. / LWT - Food Science and Technology 44 (2011) 239e243
ln Diameter
3
2
1
0 0
50
100
150
200
250
300
350
Time (h) Fig. 3. Growth curves of N. fischeri in pineapple juice for aw conditions of 0.90 (A), 0.93 (-), 0.95 (:), 0.96 (C) and 0.99 ( ), studied at the ascospore age of 51 days. The continuous line represents the fit of Modified Gompertz model (MG) to the experimental data and the symbols represent the average of the duplicate to each experiment. The diameter was measured in mm.
The mathematical relation between aw and A is presented on Equation (11). This model also showed a good prediction, once the correlation coefficient (R2) obtained was 0.943.
A ¼ 161:84aw 139:05
(11)
4. Discussion The minimum diameter of the colony of N. fischeri growing in pineapple juice was identified by naked eye, measuring approximately 2 mm. According to Gibson, Baranyi, Pitt, Eyles, and Roberts (1994), the minimum size for viewing colonies is 3 mm, which is enough to cause rejection of the product by the consumer. This improvement in colony observation may be due to the use of a flashlight. Ascospore age did not showed statistically significant influence on any parameter of growth in the range studied. The effects of this factor on the heat inactivation of mold spores have been reported in the literature. Slongo and Aragão (2006) found that ascospore age influences the heat resistance of N. fischeri in papaya and pineapple juices. However, little information is available related to the effect of this factor on the growth of molds. Araujo and Rodrigues (2004) reported that young spores of Aspergillus fumigatus and Aspergillus niger showed higher germination rates than old spores, but spore age did not affected the germination of Aspergillus flavus. According to Dantigny and Nanguy (2009), there is a tendency of an increase of the germination time as the spore age is increased, although this behavior was not observed in this study. Water activity presented significant influence statistically on the growth parameters l and A, but it did not show significant influence statistically on the parameter mmax in the range studied. Romero, Patriarca, Fernández Pinto, and Vaamonde (2007), studying the growth of Aspergillus carbonarius, showed that aw was statistically Table 2 Average of l and A for all ascospore ages in each condition of aw during the growth of N. fischeri in pineapple juice. aw
l SD (h)
A SD (mm)
0.90 0.93 0.95 0.96 0.99
178.2 5.4 107.7 39.0 92.3 10.8 85.9 14.4 64.7 49.5
6.3 0.4 12.3 1.6 12.8 2.7 17.9 2.6 20.8 3.3
SD e standard deviation; aw e water activity; l e adaptation phase; A e diameter reached by the colony.
significant (a ¼ 0.05) on the adaptation phase. Bouras, Kim, and Strelkov (2009) reported that even under the most favorable temperature conditions (25 C), mold growth decreased dramatically when aw was reduced from 0.99 to 0.95. When the adaptation phase is finished, the growth of the microorganisms can hardly be controlled and the colony can soon be identified. From Table 2 is possible to observe that the increase of water activity led to a decrease on the length of the adaptation phase. The increase of water activity resulted in an increase of the colony size (Table 2). The magnitude of observed standard deviation, mainly for the parameter l, at aw ¼ 0.99, could be explained by the fast microorganism growth, which does this measurement difficult to perform. From the results obtained in this work, models to describe the influence of water activity on N. fischeri growth in pineapple juice were suggested. These models can be applied independently of the ascospore age, in the range of aw studied. References Araujo, R., & Rodrigues, A. G. (2004). Variability of germinative potential among pathogenic species of Aspergillus. Journal of Clinical Microbiology, 42, 4335e4337. Baglioni, F., Gumerato, H. F., & Massaguer, P. R. (1999). Ocorrência de fungos filamentosos termorresistentes em polpa de tomate envasada assepticamente. Ciência e Tecnologia de Alimentos, 19(2), 258e263. Bouras, N., Kim, Y. M., & Strelkov, S. E. (2009). Influence of water activity and temperature on growth and mycotoxin production by isolates of Pyrenophora tritici-repentis from wheat. International Journal of Food Microbiology, 131, 251e255. Cheroutre-Vialete, M., Lebert, I., Hebraud, M., Labadie, J. C., & Lebert, A. (1998). Effects of pH, aw and stress on growth of Listeria monocytogenes. International Journal of Food Microbiology, 42, 71e77. Corradini, M. G., & Peleg, M. (2005). Estimating non-isothermal bacterial growth in foods from isothermal experimental data. Journal of Applied Microbiology, 99(1), 187e200. Dantigny, P., & Nanguy, S. P. (2009). Significance of the physiological state of fungal spores. International Journal of Food Microbiology, 134, 16e20. Engel, G., & Teuber, M. (1991). Heat resistance of Byssochlamys nivea in milk and cream. International Journal of Food Microbiology, 12, 225e234. Gibson, A. M., Baranyi, J., Pitt, J. I., Eyles, M. J., & Roberts, T. A. (1994). Predicting fungal growth: the effect of water activity on Aspergillus flavus and related species. International Journal of Food Microbiology, 23, 419e431. Hocking, A. D., & Pitt, J. I. (1984). Food spoilage fungi. II. Heat resistant fungi. CSIRO Division of Food Research, North Ryde, N.S.W., 2113, 44(4), 73e82. Peña, W., Faria, J. A., & Massaguer, P. R. (2004). Development of predictive model on the growth of the spoilage mould, Paecilomyces variotti in pineapple juice. Fruit Processing, 14(6), 420e426. Pitt, J. I., & Hocking, A. D. (1999). Fungi and food spoilage (2nd ed.). Gaithersburg: Aspen Publishers Inc. Rajashekhara, E., Suresh, E. R., & Ethiraj, S. (1996). Influence of different heating media on thermal resistance of Neosartorya fischeri isolated from papaya fruit. Journal of Applied Bacteriology, 81, 337e340. Romero, S. M., Patriarca, A., Fernández Pinto, V., & Vaamonde, G. (2007). Effect of water activity and temperature on growth of ochratoxigenic strains of Aspergillus carbonarius isolated from Argentinean dried vine fruits. International Journal of Food Microbiology, 115, 140e143. Ross, T. (1996). Indices for performance evaluation of predictive models in food microbiology. Journal of Applied Bacteriology, 81, 501e508. Salomão, B. C. M., Massaguer, P. R., & Aragão, G. M. F. (2008). Isolation and selection of heat resistant molds in the production process of apple nectar. Ciência e Tecnologia de Alimentos, 28, 116e121. Salomão, B. C. M., Slongo, A. P., & Aragão, G. M. F. (2007). Heat resistance of Neosartorya fischeri in various juices. LWT e Food Science and Technology, 40, 676e680. Sant’Ana, A. S., Rosenthal, A., & Massaguer, P. R. (2008). The fate of patulin in apple juice processing: a review. Food Research International, 41, 441e453. Silva, A.R., Massaguer, P.R. (2005). Radial growth modelling of Aspergillus niger in mango nectar as a function of aw, pH, and temperature in PET package. As presented at: V Congresso Iberoamericano de Ingenieria de Alimentos CIBIA V, 04 a 07/09/2005, Puerto Vallarta, Jalisco, Mexico. Slongo, A. P., & Aragão, G. M. F. (2006). Factors affecting the thermal activation of Neosartorya fischeri in pineapple and papaya nectars. Brazilian Journal of Microbiology, 37, 266e270. Slongo, A. P., Miorelli, S., & Aragão, G. M. F. (2005). Influência de diferentes fatores na termorresistência de Neosartorya fischeri em suco de mamão. Alimentos e Nutrição e Araraquara, 16(4), 377e387.
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