Predicting shelf-life of chilled pork sold in China

Predicting shelf-life of chilled pork sold in China

Food Control 32 (2013) 334e340 Contents lists available at SciVerse ScienceDirect Food Control journal homepage: www.elsevier.com/locate/foodcont P...

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Food Control 32 (2013) 334e340

Contents lists available at SciVerse ScienceDirect

Food Control journal homepage: www.elsevier.com/locate/foodcont

Predicting shelf-life of chilled pork sold in China Xiaoyang Tang a, b, Xiaohong Sun a, b,1, Vivian C.H. Wu d, Jing Xie a, b, Yingjie Pan a, b, Yong Zhao a, b, *, Pradeep K. Malakar c a

College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Hucheng Huan Road #999, Shanghai 201306, China c Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, United Kingdom d Department of Food Science and Human Nutrition, University of Maine, Orono, ME 04469-5735, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 July 2012 Received in revised form 3 December 2012 Accepted 8 December 2012

Three methods for defining or assessing the shell life of chilled pork sold in China were evaluated in this study. These methods included evaluation of sensory parameters of meat spoilage using a consumer sensory panel, electronic sensing of volatile compounds produced during meat spoilage and mathematical modeling of the growth of total aerobic viable microorganisms. Storage under isothermal conditions (4, 7, 10 and 15  C) and storage under sequential isothermal conditions from 4 to 15  C were evaluated. We assumed that chilled pork was spoiled when the total bacterial load was approximately 107 CFU/g and evaluated the time taken to reach this level (shelf life). An analysis of variance (ANOVA) of the shelf life indicated that there were no significant differences in the 3 assessment methods and therefore mathematical modeling can determine, adequately, the shelf life of chilled pork stored under isothermal and sequential isothermal conditions. Information derived from the mathematical model can contribute to effective management of chilled pork quality in the retail chill chain. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Shelf life Chilled pork Predictive modeling Electronic nose Dynamic temperatures

1. Introduction Chilled pork is popular with its nutrient and good taste, also, it’s one of the most important meat consumed in China. In the open market, it was served as fillets in individual foam trays and overwrapped with preservative film, being stored aerobically in refrigerators, which is always open to the air and the temperatures could not be constantly maintained in 4  C. The shelf life and quality of meat are largely dependent on the initial bacterial load and control of temperature in the chill chain. Poor temperature control leads to sensory deterioration, shorter shelf life and causes economic loss. Sensory deterioration occurs when meat nutrients such as glucose, free amino acids and volatile compounds are metabolized during microbial growth and this process contributes to development of off-flavors and off-odors (Ercolini, Russo, Nasi, Ferranti, & Villani, 2009; Fu & Li, 2006; Li, Sun, Zhou, Xu, & Wu, 2008; Mataragas, Skandamis, Nychas, & Drosinos, 2007). Therefore, elevated temperature is a crucial environmental factor influencing growth of microorganisms in foods. Surveys have shown

* Corresponding author. Shanghai Engineering Research Center of AquaticProduct Processing & Preservation, Hucheng Huan Road #999, Shanghai 201306, China. Tel./fax: þ86 21 61900503. E-mail addresses: [email protected] (X. Sun), [email protected] (Y. Zhao). 1 Tel./fax: þ86 21 61900503. 0956-7135/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodcont.2012.12.010

that temperatures at or above 10  C do occur during transportation, retail storage and consumer handling of meat products (Giannakourou, Koutsoumanis, Nychas, & Taoukis, 2001; Gill et al., 2002), and that these elevated temperatures support elevated microbial growth. Therefore, it is important for producers and retailers to evaluate the freshness and remaining shelf life of meat under these changing storage temperatures. A number of tools have been used for shelf life evaluation. Sensory evaluation using a consumer sensory panel is useful for determining the different sensory attributes of meat and could be adapted for shelf life prediction (Vaikousi, Biliaderis, & Koutsoumanis, 2009). Electronic sensing using an array of sensors is a recent development and is proving to be a useful tool for rapid diagnosis of food quality (Blixt & Borch, 1999; Hansen, Petersen, & Byrne, 2005). However, both of these methods can be expensive and time consuming. Mathematical modeling, where sensory qualities, chemical properties or microbial degradation of foods are quantified algebraically, can also be used to quantify shelf life or freshness of meat. Once the parameters of these mathematical models are known, these models can be used to predict freshness of meat under different storage conditions (Limbo, Torri, Sinelli, Franzetti, & Casiraghi, 2010). The overall objective of this study is to evaluate the effectiveness of three assessment methods for assessing the shelf life of chilled pork. We will assess the shelf life of chilled pork under isothermal

X. Tang et al. / Food Control 32 (2013) 334e340

and shifted isothermal storage conditions. An analysis of variance (ANOVA) of the predicted shelf life of chilled pork will be performed to compare the effectiveness of the three assessments. 2. Materials and methods 2.1. Chilled pork samples Chilled fresh ham of pork, with the bone and skin removed, was purchased from a supermarket served by a large commercial meat plant in Shanghai, China. This chilled pork was packed in insulated polystyrene boxes containing ice and transported to the laboratory within 1.5 h after purchase. 2.2. Preparation of chilled pork samples Fat and sinews were removed from the boneless fresh ham of pork and then sliced to approximately 25 g portions. The fillets were placed in individual retail foam trays and over-wrapped with preservative film. This method is usually applied in China to store the meat aerobically. All these processes were performed under aseptic condition. Packaged meat pieces were stored under controlled isothermal conditions in high precision low temperature incubators (model MIR 154; Sanyo Electric Co.,) at temperature, T, of 4, 7, 10 and 15  C. Measurements of selected quality indices were conducted at scheduled time intervals during the constant temperature storage. Similarly packaged meat was stored under a variable temperature regime. Two time-temperature programs were tested. The first shifted isothermal experimental cycle was 12 h at 4  C and 12 h at 7  C (storage regime f1). The second cycling temperature was 12 h at 4  C, 6 h at 10  C and 6 h at 15  C (storage regime f2). Measurements of selected quality indices were conducted once daily during the shifted isothermal temperature storage. 2.3. Microbiological analysis Samples weighing 25 g were transferred aseptically into sterile stomacher bags (Interscience, France) containing 225 ml sterilized 0.1% peptone water (0.85% NaCl). The mixture in the stomachers bag was then homogenized for 120 s using a Stomacher (BagMixer400, Interscience, France) at room temperature. For microbial enumeration, 0.1 ml samples of 10-fold serial dilutions of chilled pork homogenates were spread on the surface of plate count agar (Beijing Land Bridge Technology Company Ltd., Beijing, P.R. China) in Petri dishes for enumeration of total aerobic viable count, TVC, and incubated at 37  C for 48 h. Two replicates of at least two appropriate dilutions depending on the sampling day were enumerated and sampling was performed once every day under 4, 7, 10  C and twice a day under 15  C. 2.4. Mathematical model of growth 2.4.1. Primary and secondary modeling of isothermal growth Bacterial growth curves at various isothermal storage temperatures, T ( C), were generated by fitting the microbial growth data to the linear growth model (Zwietering, Dewit, Cuppers, & Vantriet, 1994), which was selected as the primary model in the present study. The linear growth model was fitted using the DMFit program (Institute of Food Research, Norwich, UK). A preliminary comparison of primary growth models e linear growth model, R2 ¼ 0.95, modified Gompertz growth model, R2 ¼ 0.98, Baranyi growth model, R2 ¼ 0.98 e indicated that the linear growth model adequately fitted the data in this study.

335

The temperature, T ( C), of the growth parameters generated from the primary linear growth model, the specific growth rate mmax (1/h), the lag time l (h) and the asymptote, A (log10 CFU/g), were then fitted using a linear equation (model for mmax), a power equation (model for l) and a linear equation (model for A). Microsoft Excel spreadsheet software was used to fit these models. 2.4.2. Growth during shifted isothermal storage We have used the scheme derived by Zwietering et al. (1994), to model bacterial growth in chilled pork during shifted isothermal storage. These authors have shown that temperature shifts within a lag phase, which means that a temperature shift occurs when the lag phase has not been completed, can be handled by adding relative parts of the lag time still to be completed. Additionally, for time shifts during the exponential phase, no additional lag time occurs. Zwietering et al. (1994) validated this observation using growth of Lactobacillus plantarum. A similar scheme was used by Malakar, Barker, and Peck (2011) for quantitative risk assessment for hazards that arise from non-proteolytic Clostridium botulinum in minimally processed chilled dairy-based foods. Different batches of pork which was purchased from supermarkets were used to validate the model constructed under constant temperatures. 2.4.3. Evaluation of model performance The accuracy of the models was determined according to bias factors (Bf) and accuracy factors (Af), where Bf indicates the mean difference between the observed and predicted value, while Af provides the accuracy of the model, which reflects how close the predicted values are to the observed values (Ross, 1996).

Bf ¼ 10

Pn

Af ¼ 10

i¼1

Pn i¼1



logðpredictedi =observedi Þ=n



jlogðpredictedi =observedi Þj=n

where observedi is the observed values; predictedi is the predicted values; n is the number of observations. Bf is a reliable indices to evaluate the performance of developed models, which could be considered as good when Bf value ranges from 0.9 to 1.05, while as acceptable when Bf value ranges from 0.7 to 0.9 or 1.06 to 1.15 (Mataragas, Drosinos, Vaidanis, & Metaxopoulos, 2006). The agreement between predictions and observations is better when Bf is closer to 1 (Ross, 1996). 2.5. Sensory analysis Sensory evaluation of chilled pork samples was performed by a six member sensory panel composed of laboratory staff. The panels were blind to the age and temperature history of the product and sensory evaluation was carried out under artificial light. Panelists were asked to score firstly, the appearance, odor and texture of raw meat and secondly the flavor and tenderness of meat which was boiled for 15 min and thirdly the overall quality of the meat. A score was assigned for these attributes based on an integer hedonic scale ranging from 1 to 6 (Land & Shepherd, 1988) where the highest quality score was 6 and the lowest quality score was 1. A preliminary trial using these scales indicated that panel members could distinguish the sensory qualities of chilled pork. The sensory quality of a pork sample was quantified by the average of these scores with an average acceptability score of 3 corresponding to spoilage. 2.6. Electronic nose analysis The electronic nose used for this study was a Fox 4000 (ALPHA MOS, Toulouse France) with 18 sensors (LY2/AA, LY2/G, LY2/gCT,

X. Tang et al. / Food Control 32 (2013) 334e340

LY2/gCTl, LY2/GH, LY2/LG, P10/1, P10/2, P30/1, P30/2, P40/1, P40/2, PA/2, T30/1, T40/2, T70/2, T40/1, TA/2) which measured volatile compounds generated by the natural flora in the chilled pork samples. The operating conditions for the electronic nose system are presented in Table 1. At each storage time point and at each temperature, six replicate samples were analyzed. A calibration of the equipment was performed every two weeks to address possible sensor drift. Principle component analysis (PCA) of the e-nose data generated by the sensors (Cozzolino, Cynkar, Dambergs, & Smith, 2010) was used to discriminate between fresh and spoiled samples and this analysis was performed using the XLSTAT software (version 2012.2.01).

10

8

log10CFU/g

336

The linear model o 4 C o 7 C o 10 C o 15 C

6

2.7. Index for comparing 3 methods of determining shelf life A Total Viable Counts (TVC) of 107 CFU/g was used as an index to compare the three approaches for evaluating shelf life of chilled pork. Bruckner, Albrecht, Petersen, and Kreyenschmidt (2012), indicated that greater differences were observed for counts of Pseudomonas spp. when compared to TVC at the beginning of storage of chilled pork and therefore TVC was chosen as an index for reproducibility of results. A survey of the literature showed that a TVC of 107 CFU/g (Dainty & Mackey, 1992; Nychas, Skandamis, Tassou, & Koutsoumanis, 2008) adequately represents spoilage in pork. 3. Results and discussion The growth of TVC of chilled pork stored under aerobic conditions at isothermal temperature 4, 7, 10 and 15  C are shown in Fig. 1. At the beginning of storage, the initial values of TVC were between 104.4 and 105.1 CFU/g, and while attaining the stationary phase, the values of TVC increased to the range between 108.6 and 109.2 CFU/g. The primary linear model, either a biphasic model or a trilinear model, used by Zwietering et al. (1994) adequately fitted the TVC data on chilled pork in the present study (Fig. 1). Additional information on the biphasic and trilinear models can be accessed at the ComBase website (http://modelling.combase.cc/HelpA.aspx). The parameters, including mmax, l, N0 and A, which were derived from the fit of the linear model to the TVC data are given in Table 2. The range of the 95 percent confidence intervals of the parameters shown in Table 2 is small and shows that the use of the primary linear models is justified. The models for fitting the parameters of the primary models, mmax (1/h), l (h) and A (log10 CFU/g) are given in Table 3. The choice of using these secondary models is based on the work of Zwietering, de Koos, Hasenack, de Witt, and van’t Riet (1991) on validation of a model for the effect of temperature on bacterial growth. Zwietering et al. (1991) concluded that the product of mmax (1/h) and l (h) for a range of temperatures was approximately constant. Our data shows that product of mmax (1/h) and l (h) at 4  C, 7  C, 10  C, 15  C is 1. This result provides validation for using these secondary models for predicting spoilage in a shifted isothermal storage regime. Table 1 The operating conditions for the FOX 4000 electronic nose system.

4

0

50

100

150

200

250

Time (hours) Fig. 1. Total viable count (TVC) in chilled pork stored at 4  C (-), 7  C (C), 10  C (:) and 15  C (;) and fits using a linear model ().

To validate the applicability of the constructed models in the present study, the models were applied under two shifted isothermal storage conditions (storage regime f1 and f2), which were shown in Figs. 2 and 3. These two figures show the prediction of TVC growth where temperature shifts within a lag period is handled by adding relative parts of the lag time still to be completed, and where temperature shifts within the exponential phase results in no additional lag time. In general, at the both shifted storage regime f1 and f2, overall predictions for TVC growth on chilled pork was in agreement with observed viable counts. The average Bf and Af values in both shifted temperature storage conditions were 0.996 and 1.017 respectively, which are similar to the Bf and Af of pork shelf life predictive models constructed by Bruckner, Albrecht, Petersen, and Kreyenschmidt (2013) (with the mean Bf and Af were 0.93 and 1.13 respectively). This result further suggests that this modeling approach is shown to be adequate for both shifted storage regime f1 and f2. One exception is the underestimation at 100 h during shifted storage regime f2 (Fig. 3). This underestimation is not considered serious since spoilage has probably occurred. The modeling approach used in this study is considered desirable in terms of food safety as the predictions do not express a fail dangerous prediction. A fail dangerous prediction is considered unacceptable in food production (Chapman, Jensen, Ross, & Cole, 2006). Comparatively our modeling approach is similar to the modeling approach of Koutsoumanis, Stamatiou, Skandamis, and Nychas (2006) and Kreyenschmidt et al. (2010). The major difference is the handling of the duration of the lag phase during temperature fluctuations. Koutsoumanis et al. (2006)

Table 2 Estimates of the kinetic parameters, inclusive of the 95 percent confidence intervals, derived from fitting a linear growth model to total aerobic viable counts (TVC) in chilled pork at temperature T ( C).

Quantity of sample in the vial

2.0 g

T ( C)

mmax (1/h)

Total volume of the vial Headspace generation time Headspace generation temperature Syringe volume Injected volume Injected speed Acquisition time

10 ml 600 s 40  C 2500 ml 800 ml 1500 ml s1 120 s

4 7 10 15

0.02 0.03 0.06 0.08

   

0.003 0.006 0.011 0.01

l (h) 45.1 14.7 14.7 18.7

N0 (log CFU/g)    

21.0 22.9 7.6 3.7

4.9 4.6 4.2 5.0

   

0.3 0.5 0.3 0.2

A (log CFU/g) 8.7 8.7 8.8 8.9

   

0.4 0.5 0.3 0.2

R2 0.97 0.95 0.99 0.99

mmax: the specific growth rate, l: the lag phase duration, N0: initial counts of TVC on

chilled pork, A: the stationary phase population of TVC on chilled pork, R2: coefficient of determination.

X. Tang et al. / Food Control 32 (2013) 334e340 16

Table 3 Secondary models for evaluating the effect of temperature, T ( C), on the specific growth rate mmax (1/h),, the lag phase duration l (h), and the stationary phase population A (log CFU/g) of total aerobic viable counts (TVC) in chilled pork.

A ¼ 0.03*Tþ8.5

A (log CFU/g)

assumed that the product of mmax and l was temperature independent while Kreyenschmidt et al. (2010) assumed an absence of an intermediate lag phase as temperature changes. Fig. 4 shows sensory decrease in terms of the average of the scores of the five sensory attributes and its 95 percent confidence interval for chilled pork stored at 4  C, 7  C, 10  C and 15  C, chilled pork stored for 12 h at 4  C and 12 h at 7  C (storage regime f1) and chilled pork stored for 12 h at 4  C, 6 h at 10  C and 6 h at 15  C (storage regime f2). Sensory acceptance of chilled pork decreased with storage time. A higher storage temperature resulted in a faster decrease in sensory acceptance. A linear model was fitted to the sensory data to calculate the shelf life of chilled pork in terms of sensory acceptance. As shown in Table 4, the predicted time to spoilage of chilled pork stored at 4  C, 7  C, 10  C, 15  C were 114.1 h, 98.7 h, 73.5 h and 23.5 h respectively. Additionally, the corresponding bacterial load corresponding to spoilage (when the sensory score ¼ 3) was w107 CFU/g. A similar trend was obtained from the sensory panel for chilled pork stored at fluctuating temperatures (results not shown). Chilled pork samples were analyzed, daily, by the electronic nose. The sensor responses of electronic nose to chilled pork stored under different temperatures were analyzed by principal component analysis, according to the protocol for multi-sensor equipments data analysis (Limbo et al., 2010). Figs. 5 and 6 show plots of the first and second principle components (PC1, PC2) of the data derived from the 18 sensors of the electronic nose used for examining the microbial quality of chilled pork stored at 4  C and 15  C, the number beside each point indicates the storage time in days. The two first principal components explain together 90% and 96% of the total variance, with PC1 accounted 72% and 76% at 4  C and 15  C respectively, which means these two principle components

12

C

12 o

mmax ¼ 0.0057*T  0.0026 l ¼ 84*T0.7

10 8

8

6 4

4

2 0

20

40

60

80

100

contributed to the majority of the variance of the measurements (>85%). The clustering of the data in Figs. 5 and 6 indicates there is a clear separation between the samples from different storage time. Both the conclusions of the sensory panel and the microbiological examination of pork samples indicate that chilled pork stored at 4  C remains fresh below 4 days of storage. Similarly chilled pork stored at 15  C remains fresh below 1 day of storage (left sided clusters in Figs. 5 and 6). Therefore a change in the production of the volatile components occurred after 4 days of storage at 4  C and one day of storage at 15  C. In particular, it could be concluded from Figs. 5 and 6 that samples are distributed along PC1 according to the storage time. So the trend of PC1 samples scores vs. storage time are considered. Figs. 7 and 8 show the fit of a shifted Gompertz equation (a shift

o

4

4

Sensory score

C

o

Temperature

log10CFU/g

6

6

140

Fig. 3. Total viable count (TVC) in chilled pork (-) stored under aerobic and dynamic temperature (———) conditions (12 h at 4  C, 6 h at 10  C and 6 h at 15  C) and predictions of growth ().

4 C o 7 C o 10 C o 15 C o f1 C o f2 C

6

8

120

Time (hours)

8

8

Temperature

Model equation

16

14

log10CFU/g

Kinetic parameters

mmax (1/h) l (h)

337

----The Organic rejection point from sensory evaluation

4

---------------------------------------------------------------2

0 0

40

80

120

160

Time (hours) 0

20

40

60

80

100

120

140

160

180

200

Time (hours) Fig. 2. Total viable count (TVC) in chilled pork (-) stored under aerobic and dynamic temperature (———) conditions (12 h at 4  C and 12 h at 7  C) and predictions of growth ().

Fig. 4. Sensory scores of chilled pork and their 95% confidence intervals for isothermal storage at 4  C (-), 7  C (C), 10  C (:) and 15  C (;), shifted isothermal storage, f1, at (12 h at 4  C and 12 h at 7  C) (A) and shifted isothermal storage, f2, at (12 h at 4  C, 6 h at 10  C and 6 h at 15  C) (+). A linear fit of the mean of the scores is shown () and the horizontal line (————) represents the spoilage level (organoleptic rejection point).

338

X. Tang et al. / Food Control 32 (2013) 334e340 6

Table 4 Sensory panel evaluation of the shelf life of chilled pork stored isothermally at temperature, T ( C), and Ns (log10 CFU/g), the corresponding level of total viable counts at the point of organoleptic rejection. Shelf lifea/h

Ns (log10 CFU/g)

4 7 10 15 Average

114.1 98.7 73.5 23.5

6.5 7.3 7.5 6.0 7.0b

a b

4

PC2 (20.61 %)

Temperature/ C

The shelf life was defined when the sensory score of chilled pork ¼ 3. Arithmetic average.

1.5 1.51.5 1.5 2

2

0

1 00

-2

parameter was added to the Gompertz equation to accommodate for aligning the equation on the y-axis) to the PC1 versus time of storage data for chilled pork at 4  C and 15  C. Limbo et al. (2010) also quantified this change, when modeling shelf-life of mince beef stored in high-oxygen modified atmosphere, using the following procedure. An empirical transition function was used to fit the PC1 vs. time of storage data and then minimized the second derivative of the empirical transition function. This minimum indicates the time of spoilage. We have used a similar procedure to quantify the shelf life of chilled pork. We set the second derivative of the shifted Gompertz function to zero and the corresponding time of spoilage was greater than 4 days at 4  C and greater than 1 day at 15  C and the level of TVC at the predicted time of spoilage was w107 CFU/g (data not shown). A TVC of 107 CFU/g was chosen as an index for evaluating the three methods for assessing shelf life in this study. A comparison between shelf life predicted by mathematical modeling, shelf life estimated by sensory panel and electronic nose, using this index to evaluate spoilage is shown in Table 5. An analysis of variance, ANOVA, of the three methods showed that all three methods could adequately predict spoilage (Table 6). The results of the present study reveal that growth of TVC on chilled pork correlated closely by sensory changes during storage, therefore, the mathematical model for TVC can be a reliable tool to predict spoilage of aerobic stored chilled pork. In our opinion, the mathematical modeling approach is more tractable and convenient when compared to the sensory approaches. If a shelf life evaluation is needed at 14  C, a new sensory experiment needs to be carried out. Since it is not always

1.5 222 2 1.5

0

00

33 2.5 2.5 2.5 32.5 2.5 2.5 33 3

1 1 1 1 1

4 444

0

-4 -8

-6

-4

-2

0

2

4

6

8

PC1 (75.79 %) Fig. 6. Scatter plot of the first and second principle component score (PC1, PC2) from the analysis of the volatile compounds produced by the natural flora of chilled pork cuts stored at 15  C. Individual symbols indicate replicate chilled pork samples taken at different storage time.

possible to anticipate future events, the sensory approach becomes expensive and time consuming in the long run. Even though 14  C was not tested during the construction of the mathematical model, it is acceptable to use interpolation to estimate shelf life since the maximum temperature tested was 15  C. The costs involved in the modeling approach, which can be steep, are in the initial construction and validation of the model (Koutsoumanis et al., 2006, Kreyenschmidt et al., 2010). This initial cost is however balanced by the tractability of the model. Several researches has already identified that temperature is the main factor affecting the growth of microorganisms (Bruckner et al., 2012; Gospavic, Kreyenschmidt, Bruckner, Popov, & Haque, 2008). Furthermore, more efforts is needed to evaluate the possible effects of several other factors, such as meat composition, changes in the gas atmosphere or processing hygiene, on model applicability and especially on the growth of microorganisms (Boonyawantang, Mahakarnchanakul, Rachtanapun, & Boonsupthip, 2012; Koutsoumanis et al., 2006; Kreyenschmidt et al., 2010). 12

6

10

4

8 1.5 1.5 1.5 000 1.5 0 1.5 1.5

5

1 1 11 3 123

0

6

5 5

PC1

PC2 (17.96 %)

2

23 3

-2

2 3

5

2 1 22 44 44 4 4

4 2

5

0

-4

5 3

-2

-6 -6

-4

-2

0

2

4

6

8

10

12

PC1 (71.69 %)

-4 0

2

4

6

Time (days) Fig. 5. Scatter plot of the first and second principle component score (PC1, PC2) from the analysis of the volatile compounds produced by the natural flora of chilled pork cuts stored at 4  C. Individual symbols indicate replicate chilled pork samples taken at different storage time.

Fig. 7. A plot of the first principle component score, PC1, versus the time of storage for chilled pork stored at 4  C (-) and the corresponding fit using a modified Gompertz model ().

X. Tang et al. / Food Control 32 (2013) 334e340

6 4

mathematical modeling can accommodate these factors and therefore allow for consistent management of shelf life in the meat industry.

2

Acknowledgments

0

PC1

339

This research was supported by the National “Twelfth Five-Year” Plan for Science & Technology Support (Project No: 2011BAD24B02), the project of Science and Technology Commission of Shanghai Municipality (Project No: 11310501100,12391901300) and Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation (Project No: 11DZ2280300). This work was also supported by the Maine Agricultural and Forest Experiment Station at the University of Maine with external publication number 3700 and the program granted by Shanghai Ocean University for training outstanding graduate student. The authors are thankful to Dr. Pradeep K. Malakar for his help with this manuscript preparation.

-2 -4 -6 -8 0

2

4

6

Time (days) References Fig. 8. A plot of the first principle component score, PC1, versus the time of storage for chilled pork stored at 15  C (-) and the corresponding fit using a modified Gompertz model ().

Table 5 A comparison of predicted shelf-life of chilled pork derived from a mathematical growth model, electronic nose analysis and sensory panel evaluation at isothermal storage temperatures of 4  C, 7  C, 10  C, 15  C and two shifted isothermal storage temperature regimes. Storage temperature/ C

SLsensorya/day

SL

4 C 7 C 10  C 15  C 4  C/12 h w7  C/12 h 4  C/12 hw10  C/6 hw15  C/6 h

4.8 4.1 3.1 1.0 4.6 3.4

5.5 3.6 2.5 1.4 4.8 3.6

model

b

/day

SLe-nosec/day 4.3 4.3 2.7 1.3 3.4 3.5

Note: The shelf life of chilled pork predicted by sensory analysis, mathematical model and electronic nose, corresponded to a TVC of w107 CFU/g, when the sensory score of chilled pork ¼ 3. a Shelf-life of chilled pork derived from sensory panel evaluation. b Shelf-life of chilled pork derived from a mathematical growth model. c Shelf-life of chilled pork derived from electronic nose analysis.

Table 6 Analysis of variance (ANOVAa) of the predicted shelf life by sensory panel analysis, by mathematical modeling and by electronic nose measurements. The data for this analysis is given in Table 5. Source

df

SS

MS ¼ SS/df

F value

P value

Between models Within a model

2 15

0.3 26.8

0.2 1.8

0.1

0.9

a At the 0.05 significance level, the population means are not ‘significantly different’ df ¼ degrees of freedom, SS ¼ Sum of Squares (Draper & Smith, 1998).

4. Conclusion A mathematical modeling approach for determining shelf life of chilled pork in China is a viable option. In this study we have shown that a mathematical modeling approach is comparable to sensory approaches which are the dominant method for determining shelf life. Reliable estimation of the quality and the remaining shelf life of the products can obtained from a mathematical model when coupled to inexpensive timeetemperature integrators (Bruckner et al., 2013; Kreyenschmidt et al., 2010). Whilst there are several other factors which influence growth of microorganisms,

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