- Email: [email protected]
Neural network modelling of the fate of Salmonella enterica serovar Enteritidis PT4 in home-made mayonnaise prepared with citric acid
Food Control 13 (2002) 525–533 www.elsevier.com/locate/foodcont
Neural network modelling of the fate of Salmonella enterica serovar Enteritidis PT4 in home-made mayonnaise prepared with citric acid R. Xiong a, G. Xie b, A.S. Edmondson b, J-F. Meullenet a
a,*
Department of Food Science, University of Arkansas, 2650 N Young Avenue, Fayetteville, AR 72704, USA b Food Research Group, Leeds Metropolitan University, Calverley Street, Leeds LS1 3HE, UK Received 19 September 2001; received in revised form 12 March 2002; accepted 12 March 2002
Abstract Fifty-four mayonnaise recipes were generated by the central composite design and tested for microbiological safety at two temperatures (5 and 22 °C). The content of oil: (150–350 ml), egg yolk (10–35 g), citric acid (4.98% w/v) (10–40 g), salt (0–3 g), mustard (0–2 g), sugar (0–1 g) and white pepper (0.25 g) varied among the different recipes. The fate of Salmonella enterica serovar Enteritidis PT4 in mayonnaise products was investigated by both viable count and presence/absence tests and modelled by neural networks. This study demonstrated that feed-forward neural networks were incapable of modelling the survival/growth curves of S. Enteritidis PT4 as a one-step-procedure model, but were capable of modelling the presence/absence of the organism. Ó 2002 Published by Elsevier Science Ltd. Keywords: Neural network; Modelling; Salmonella; Mayonnaise; Citric acid
1. Introduction Mayonnaise is a widely consumed product, forming the foundation of one-half of all salad dressings and the basis of many other products such as tartare sauce, coleslaw etc. (Radford & Board, 1993; Anon, 1998). A wide range of commercial mayonnaises are available but many individuals and caterers still prefer to use homemade mayonnaise due to its lower tartness and better texture (Anon, 1989; Radford & Board, 1993). However, home-made mayonnaise has been associated with food poisoning outbreaks caused by Salmonella, Staphylococcus aureus, Bacillus, Campylobacter and other bacteria (Smittle, 1977; Irwin et al., 1993; Radford & Board, 1993). This has led to extensive studies on the fate of microorganisms in mayonnaise. The fate of microorganisms is determined by the pH of mayonnaise, type and concentration of the acidulant, the time and temperature of storage together with the antimicrobial activity of other ingredients such as salt, garlic, sugar, mustard, oil, egg white and so on (Smittle, 1977; Perales & Garcia, 1990; Radford & Board, 1993; Radford, 1994;
*
Corresponding author. Tel.: +1-501-575-4605; fax: +1-501-5756936. E-mail address: [email protected] (J.-F. Meullenet). 0956-7135/02/$ - see front matter Ó 2002 Published by Elsevier Science Ltd. PII: S 0 9 5 6 - 7 1 3 5 ( 0 2 ) 0 0 0 4 0 - 3
Zhao & Doyle, 1994). However, there is little information on the synergistic effects of mayonnaise ingredients and storage temperature on the behaviour of organisms. To investigate the single and combined contributions of ingredients and storage temperature to the safety of mayonnaise products, predictive microbiology may be applied. This is an approach to establish predictive mathematical models of growth, survival and death of food-borne microorganisms as affected by environmental factors and processing conditions. Since its successful applications offer many benefits to the practice of food microbiology, there is growing interest internationally (McMeekin, Olley, Ross, & Ratkowsky, 1993). Neural network, originally developed for mimicking human brain processes, is an artificial intelligence approach and has the ability to handle complicated information-processing problems. It has recently gained more and more attentions due to great progress in the technology of computer hardware and software and has been applied in many areas including predictive microbiology. Hajmeer, Basheer, and Najjar (1997) developed a neural network for prediction of the modified Gompertz growth model parameters. The neural network was found to yield a better agreement with experimentally measured data as compared with data predicted by polynomial models. Geeraerd and Van Impe (1999) extended neural networks to model the specific growth
526
R. Xiong et al. / Food Control 13 (2002) 525–533
rate and the lag time duration for Shigella flexneri in BHI medium as a function of temperature, pH, NaCl (%) and NaNO2 (ppm) and compared the neural networks with polynomial models. Neural network analyses of FTIR-PAS spectra of corn kernels can be used to recognize the syndrome of spectral features caused by fungal infection by A. flavus (Gordon, Wheeler, Schudy, Wicklow, & Greene, 1998). Recently, Cheroutre-Vialette and Lebert (2000) developed a recurrent neural network for the prediction of Listeria monocytogenes growth under pH and aw variable conditions. Neural networks have a great potential in predictive microbiology, but few studies have used neural networks for modelling both the growth/survival and the presence/absence of Salmonella in a food system. The purpose of this study was to model both the viable count and the presence/ absence of S. Enteritidis PT4 in mayonnaise products by neural networks.
2. Materials and methods 2.1. Materials All food materials used in this investigation were purchased from a local supermarket in Leeds, UK. The ingredients used were pure sunflower oil, large size eggs, cooking salt, sugar, white ground pepper and English mustard powder. Because eggs are perishable foods, they were purchased one day before the preparation commenced. Using citric acid (anhydrous) (Fisher Scientific UK Ltd., Leicestershire, UK), a solution of citric acid was made in the laboratory at a concentration of 4.98% (w/v) (Xiong, Xie, & Edmondson, 1999). 2.2. Basic mayonnaise recipe and preparation procedure The following basic recipe was used as it could be easily modified for this investigation: 250 ml pure sunflower oil, 20 g egg yolk (large size), 25 g citric acid solution (4.98% w/v), 1.5 g salt, 1.0 g English mustard, 0.5 g sugar, 0.25 g white pepper. This basic recipe was used to generate other recipes by the Central Composite Design method (see Experimental Design). The mayonnaise was prepared as follows: (1) all ingredients except oil were placed in a clean bowl; (2) the ingredients were mixed thoroughly by an electronic mixer and (3) oil was added drop by drop until the formation of an emulsion and then poured steadily whilst blending at high speed. After preparation, the products were subjected to pH and aw measurements. 2.3. Experimental design The Central Composite Design in MINITAB for WINDOWS (Version 11, Minitab Inc., PA, USA) was
used because the surface response method was utilized in the project. The contents of oil, egg yolk, citric acid, salt, mustard and sugar were varied while the content of white pepper was kept constant. There were two reasons to keep the pepper content unchanged: (1) the maximum number of factors used in the Central Composite Design was six and (2) pepper has been shown to have no effect on Salmonella (Radford, 1994). The fifty-four recipes (treatments) were generated by the central composite design (Table 1). However, it was found that four recipes (Recipes 1, 6, 16 and 30) could not produce mayonnaise when 10 g citric acid and 10 g egg yolk were used, but produced mayonnaise when 25 g citric acid and 15 g egg yolk were used. 2.4. Test organism and culture A strain of Salmonella enterica serovar Enteritidis PT4, isolated by the Central Public Health Laboratories in Colindale (UK) following an outbreak of food poisoning attributed to mayonnaise, was used throughout this work. The culture of S. Enteritidis PT4 was maintained on a slope of Nutrient Agar (Oxoid). The slope was inoculated with S. Enteritidis PT4 and incubated at 37 °C for 24 h and then stored at room temperature. Subcultures were made every three months. The working culture was maintained on XLD agar (Oxoid) by growing up a culture at 37 °C for 48 h and then storing at 4 °C. The working culture was subcultured weekly. 2.5. Inoculation of mayonnaise A loopful of S. Enteritidis PT4 from a XLD agar at 4 °C was inoculated into 10 ml of 0.1% peptone (Oxoid) containing 20% egg yolk emulsion (Oxoid). The culture was incubated statically at 37 °C for 24 h. Experimentation showed that this produced the stationary phase. The culture was then added to the 300 g fresh mayonnaise samples at the rate of 1 ml per 100 g to give approximately 106 organisms per gram, and blended with a Colworth stomacher (Seward, London, UK) for 2 min. No separation of mayonnaise was observed due to blending. Each contaminated mayonnaise samples were immediately transferred into a 400 ml sterile plastic container covered with a lid, and stored at 22 1 and 5 1 °C, respectively. After one week to 10 days, some low acid samples stored at 22 °C showed some level of phase separation and spoilage by molds. 2.6. Microbiological analysis For viable counts, samples were taken at time intervals after contamination. At each time interval 10 g contaminated mayonnaise was weighed into a stomacher bag (Seward), and then 90 ml of 1% Buffered
R. Xiong et al. / Food Control 13 (2002) 525–533
527
Table 1 The experimental design table Recipe
1m 2 3 4 5 6m 7 8 9 10 11 12 13 14 15 16m 17 18 19 20 21 22 23 24 25 26 27 28 29 30m 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
Oil (ml)
Yolk (g)
Citric acid (g)
Salt (g)
Mustard (g)
Sugar (g)
Pepper (g)
350 150 350 150 250 350 150 350 250 350 150 150 150 250 150 350 350 150 150 150 350 350 350 350 250 350 250 250 350 250 150 150 350 350 150 250 150 150 150 250 250 250 250 250 250 250 250 300 250 250 200 250 250 250
25 30 30 30 20 25 10 30 20 30 10 10 30 20 10 25 30 10 30 10 10 30 30 10 20 10 20 20 30 25 30 30 10 30 30 20 10 10 30 20 20 20 20 20 20 20 25 20 20 20 20 15 20 20
15 40 10 10 25 15 40 10 25 40 10 40 10 25 40 15 10 10 10 40 40 40 40 40 25 40 25 25 10 15 10 40 40 40 40 25 10 10 40 25 25 32.5 25 25 25 25 25 25 17.5 25 25 25 25 25
3 3 0 0 1.5 0 3 3 1.5 3 3 0 3 1.5 0 3 0 0 0 3 0 0 0 3 1.5 3 1.5 1.5 3 0 3 3 0 3 0 1.5 0 3 0 1.5 1.5 1.5 1.5 0.75 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 2.25
2 2 2 2 1 2 2 2 1 2 2 0 0 1 2 0 0 2 0 0 2 2 0 0 1 2 1 1 0 0 2 0 0 0 0 1 0 0 2 1 1.5 1 0.5 1 1 1 1 1 1 1 1 1 1 1
1 0 1 0 0.5 0 1 0 0.5 1 0 1 0 0.5 0 0 0 1 1 0 1 0 1 1 0.5 0 0.5 0.5 1 1 1 1 0 0 0 0.5 0 1 1 0.5 0.5 0.5 0.5 0.5 0.75 0.5 0.5 0.5 0.5 0.25 0.5 0.5 0.5 0.5
0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
m means modified.
Peptone Water (BPW) (Oxoid) were added and the mixture was homogenized for 1 min with a Colworth stomacher (Seward). Serial tenfold dilutions in 0.1% peptone diluent were plated onto XLD agar (Oxoid)
using the surface spread method (Roberts, Hooper, & Greenwood, 1995). The XLD agar plates were incubated at 37 °C for at least 24 h and then the viable colonies were enumerated.
528
R. Xiong et al. / Food Control 13 (2002) 525–533
For presence/absence tests a 25 g mayonnaise sample was added to 225 ml 1% BPW in a sterile container at each time interval (Roberts et al., 1995). The mixture was shaken vigorously for 1–2 min and incubated at 37 °C for 24 h. After incubation, 0.1 ml of the mixture or enrichment was added to 4 ml Rappaport–Vassiliadis (RV) R10 broth (Difco) in prepared indirect RABIT tubes (Rapid Automated Bacterial Impedance Technique, Don Whitley Scientific, Shipley, UK). The RABIT tubes were placed into a RABIT incubator block and incubated at 42 °C for 18 h (Donaghy & Madden, 1993). If the RABIT detected the growth of the organisms, the result was recorded as presence of Salmonella (positive result); otherwise, absence of Salmonella (negative result). All the presence/absence experiments were performed in duplicate. 2.7. Model development To model response-time curves, two procedures are available, i.e. the one-step procedure and the two-step procedure. In the one-step procedure, there are no intermediate steps or stages involved in the modelling process, i.e. all the response-time curves involved are fitted at a time by a one-step-procedure model that uses time and all the environmental factors as independent variables. In this study, a neural network was used as a one-step-procedure model to examine the feasibility for fitting the response-time curves for S. Enteritidis PT4. The structure of the interconnected neurons in a neural network depends on the complexity of a given problem. The number of neurons in the input and the output layers are determined by the number of input and output variables in the problem. In this work, a three-layered feed-forward neural network was used. The input layer consisted of nine neurons (oil, yolk, citric acid, salt, mustard, sugar, white pepper, temperature and time) and one bias, the output layer of one neuron (LSF ¼ logðN =N0 Þ). The presence of the bias in a neural network prevents the possible trapping to a local minimum in the gradient descent search, allows the approximation of both odd and even functions and aids in the rapid convergence error function to a best point on the error surface in order to reach the minimum error value (Rumelhart & McClelland, 1986). The number of neurons in the hidden layers is related to the performance of a neural network. Too few hidden neurons limit the ability of the network to model the problem, too many can result in memorising the input/output pair patterns presented in the training process. The optimal number of neurons in the hidden layer is usually determined by trial and error. A free neural network software package (MetaNeural for Windows) from the Internet (Sankaran & Embrechts, 1997) that uses a backpropagation training algorithm was used to train the neural network. In this study, three factors (number of hidden
neurons, learning rate and momentum term) were optimised using Random Centroid Optimisation (Xie, Xiong, & Church, 1998). In the optimisation process a total of 50 sets of randomly selected values was used and the number of training cycles was 20,000. The optimum number of hidden neurons, learning rate and momentum term was obtained as 5, 0.0254 and 0.0267, respectively. Consequently, the topology of the neural network used in the present work was 9-5-1 (the biases in both the input layer and the hidden layer were not included here because they were added automatically by MetaNeural). This neural network is referred to as NN model I. The number of training cycles or iterations in neural network training is usually determined by trial and error and by training the optimised neural network on the entire database. The number of training cycles was varied and the root mean square error (RMSE) was calculated. It was found that the error stabilised at about 20,000 iterations. Any further increase in the number of iterations would not induce any significant reduction in error but might increase the computing time and result in over-fitting. 20,000 training cycles were therefore used in the model training process. The training process was repeated until the neural network’s performance was satisfactory, i.e., until the RMSE value satisfied the pre-set requirement (error tolerance ¼ 0:01 in this case) or the maximum number of iterations (20,000) was reached. 2.8. Modelling the presence/absence of Salmonella in mayonnaise A three-layered feed-forward neural network was used to model the presence/absence of S. Enteritidis PT4 in mayonnaise. The input layer consisted of nine neurons (oil, yolk, citric acid, salt, mustard, sugar, white pepper, temperature and time) and one bias, the output layer of one neuron (the presence value). The presence value was 1.0 if one of the presence tests was found to be positive (þ). Otherwise, it was 0.0. The optimal number of neurons in the hidden layers was determined as described previously and a bias was added automatically in the hidden layer. In terms of RMSE values, the structure of the optimal neural network model was 9-101, referred to in subsequent discrimination as NN model II. The satisfactory number of iterations was found to be 30,000 for the training process. 2.9. Validation experiments To verify neural networks, validation experiments are needed. Using the Random Data method in MINITAB for WINDOWS, ten recipes were randomly generated and are listed in Table 2. These recipes were not used in the original experiments but the levels of each ingredient
R. Xiong et al. / Food Control 13 (2002) 525–533
529
Table 2 The validation experiments Recipe
Oil (ml)
Yolk (g)
Citric acid (g)
Salt (g)
Mustard (g)
Sugar (g)
Pepper (g)
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10
320 240 190 270 180 260 210 330 350 160
28 24 16 22 15 16 19 29 30 29
25 16 19 23 21 31 15 18 16.7 10
2.4 1.7 1.1 0.6 2.2 0.8 1.0 0.4 3 1.9
0.6 1.9 1.7 0.7 1.3 0.2 0.0 1.4 0.0 1.5
0.0 0.7 0.0 0.5 0.2 0.8 0.4 0.9 1.0 0.6
0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
or factor were within the range tested: oil, 150–350 ml; egg yolk, 10–30 g; citric acid, 10–40 g; salt, 0–3 g; mustard, 0–2 g; sugar, 0–1 g.
3. Results and discussion 3.1. Modelling the fate of S. Enteritidis PT4 in mayonnaise In theory, a one-step-procedure model can be obtained by combining a primary model with a secondary model, but this is at the cost of making the regression process much more difficult (Saraiva, Oliveira, Oliveira, & Hendrickx, 1996). An advantage of the one-stepprocedure model is that its standard deviation is much smaller than that of the two-step-procedure model (Saraiva et al., 1996; Membre, Thurette, & Catteau, 1997). Hajmeer et al. (1997) suggested that neural networks could be utilised as a one-step-procedure model to fit the growth/survival curves. In this study, a total of 108 response-time curves were obtained from the viable
count tests. After examining these curves, it was found that most of them did not follow a linear relationship, i.e. first order kinetics. Those curves were then fitted to a three-layer feed-forward neural network model (NN model I). The RMSE value obtained was 0.0379. To reduce the RMSE value the LSF values were transformed by log, root squares and other functions. Other methods such as adding extra hidden neurons and an extra hidden layer, reducing the number of input variables, increasing/decreasing the number of iterations, and even varying learning rate and momentum term were also tested. Unfortunately, the neural network could not be converged on a desired solution. Using the trained neural network, LSF values for all the response-time curves were predicted, and then the R2 and RMSE values for each individual response-time curve were calculated (data not shown). In terms of R2 values, the overall performance of the neural network was surprisingly poor. After examining the predicted curves it was found that most showed unacceptable curve shapes. As examples, the predicted curves for the first two recipes (Recipes 1, 2) at 5 and 22 °C are presented in
Fig. 1. Comparison of observed LSF values ðÞ and the LSF values (––) predicted by neural network (NN model I) (LSF ¼ log surviving fraction of Salmonella enterica serovar Enteritidis PT4 in home-made mayonnaise).
530
R. Xiong et al. / Food Control 13 (2002) 525–533
Table 3 Observed and predicted presence (þ)/absence () of Salmonella in mayonnaise Recipe 1m 2 3 4 5 6m 7 8 9 10 11 12 13 14 15 16m 17 18 19 20 21 22 23 24 25 26 27 28 29 30m 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
Observed at 22 °C
Predicted at 22 °C
Observed at 5 °C
Predicted at 5 °C
24 h
48 h
72 h
24 h
48 h
72 h
72 h
168 h
72 h
168 h
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
m means modified.
Fig. 1. It is evident from the figure that the neural network is not capable of modelling the response-time curves obtained in this study. Another drawback is that important and widely-used kinetics such as lag time
duration, specific growth rate, maximum population, stationary time duration, death rate constant cannot be derived from the one-step-procedure neural network.
R. Xiong et al. / Food Control 13 (2002) 525–533
3.2. Modelling of the presence/absence of Salmonella in mayonnaise The presence or absence of S. Entertidis PT4 in mayonnaise was modelled by a neural network (NN model II). Using the trained NN model II, the presence value was predicted. If the calculated presence value was less than 0.5, the prediction was negative (); otherwise, it was positive (þ). The results of modelling presence/absence of Salmonella in mayonnaise are listed in Table 3. By comparing the observed presences/absences with the predicted ones, the neural network gave very good predictions (100% corrections). Genigeorgis, Martin, Franti, and Riemann (1971) were the first to use probability models in predictive microbiology. In probability modelling, the focus of attention is directed towards deciding whether a microorganism might or might not grow (Ratkowsky & Ross, 1995). Probability modelling is an alternative for modelling the presence/ absence of Salmonella in mayonnaise, on which further research is needed. 3.3. Validation analysis Competence of a model, especially a neural network, must be validated before being put to use. The trained neural network (NN model II) for the presence/absence tests was validated and the results are presented in Table 4. The data obtained previously (Xiong et al., 1999) were also used to evaluate NN model II although 0.5 g of white pepper used previously exceeded the 0.25 g used in this study. The results showed that NN model II gave a good prediction. The correction rates for predicting the presence and absence of Salmonella after 72 h storage
531
are 100% and 70%, respectively. For Recipes v2, v4 and x3 (Table 4), the organism was not detected after 72 h storage, but the NN model II predicted the presence of Salmonella. This false presence prediction was on the safe-side. As is the case with all modelling techniques, it is not recommended that any trained neural network be used for extrapolation. In other words, the user has to restrict the use of the trained neural network(s) within the limits on which they were developed. These limits for mayonnaise tested in this study are time (0–72 h at 22 °C and 0–168 h at 5 °C), temperature (5–22 °C), oil (150– 350 ml), egg yolk (10–35 g), citric acid (10–40 g), salt (0– 3 g), mustard (0–2 g), sugar (0–1 g), and white pepper (0.25 g). 3.4. Factors affecting the presence/absence of S. Enteritidis PT4 in mayonnaise Predictive microbiology applied to food products can be used to describe the effects of environmental factors and their interactive effects on the survival or presence/ absence of microorganisms. Mathematically, analysis of single and combined effects of environmental factors on the presence/absence of S. Enteritidis PT4 in home-made mayonnaise can be accomplished by altering one or two of the factors in the models while holding all others constant. After validation, the trained NN model II was used to analyse the single and combined effects of all the ingredients on the presence/absence of S. Enteritidis PT4 in mayonnaise stored at 22 °C. By using the presence value, the effects of single ingredient are presented in Fig. 2 (the combined effects of two or more ingredients are not shown). From the figure, it can be seen that egg yolk, citric acid, salt and temperature were major contributors
Table 4 Observed and predicted presence (þ)/absence () of Salmonella in mayonnaise Observed at 5 °C
Predicted at 5 °C
24 h
48 h
72 h
24 h
48 h
72 h
72 h
168 h
72 h
168 h
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ
þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
x1 x2 x3 x4 x5 x6
þ þ þ þ þ þ
þ þ
þ þ
þ þ þ þ þ þ
þ þ þ þ þ þ
þ þ þ
þ þ þ þ þ þ
þ þ þ þ þ þ
þ þ þ þ þ þ
þ þ þ þ þ þ
Recipe
Observed at 22 °C
Predicted at 22 °C
x1 to x6 data from Xiong et al. (1999).
532
R. Xiong et al. / Food Control 13 (2002) 525–533
Fig. 2. Effect of oil, egg yolk, citric acid, salt, mustard, sugar, pepper and temperature on the presence value for Salmonella enterica serovar Enteritidis PT4 for the basic recipe mayonnaise stored at 22 °C for 72 h (predicted by NN model II).
to the presence value, while others had negligible effects. As the amount of egg yolk increased, the presence value (approximately probability of presence) increased, whereas it decreased as the amount of citric acid and/or salt and storage temperature increased. It was interestingly found that there were ‘thresholds’ for salt and temperature after which the presence value began to drop rapidly, which indicates that the minimum amount or proportion of salt and the minimum temperature were required to inactivate the organism within 72 h. It is evident from Fig. 2 that if the basic recipes contained 20 g or less egg yolk, at least 20 g citric acid and at least 1.5 g salt and the product stored at 22 °C or above, the presence value would be near zero. 20 g egg yolk, 20 g citric acid, 1.5 g salt and 22 °C temperature were called the boundary conditions (maximum or minimum requirements) for the absence of Salmonella in citric acid mayonnaise after 72 h storage. This result is of particular importance to consumers. For low infectious dose pathogens (such as E. coli O157:H7), the growth rate and limits of the organisms are irrelevant because any number of the organisms re-
sults in a high risk of illness (Presser, Ratkowsky, & Ross, 1997). Although it is not possible to guarantee the absence of pathogens in all units of all foods, it is possible to minimise the risk of food poisoning and to quantify the environmental factors that affect it. In this context, neural network is a very useful tool for food safety management. As far as is known, this study is the first attempt to use a neural network for modelling the presence/absence of Salmonella in mayonnaise and for quantifying the factors that affect the risk of food poisoning.
4. Conclusions Although neural network has failed to model the viable count of Salmonella in mayonnaise, it has been successful in modelling its presence/absence in the products. The results have shown that the bacterial presence and absence interface for citric acid mayonnaise stored for 72 h was determined by the following factors: 20 g egg yolk, 20 g citric acid, 1.5 g salt and a storage tem-
R. Xiong et al. / Food Control 13 (2002) 525–533
perature of 22 °C. It is recommended that to maintain S. Enteritidis PT4-free mayonnaise prepared with pure lemon juice (citric acid concentration 5% w/v), recipes containing at least 20 ml pure lemon juice per egg yolk (assuming maximum weight of egg yolk to be 20 g (Xiong et al., 1999)) and at least 2 g salt per egg yolk should be used and the product should be held for at least 72 h at 22 °C or above before consumption or refrigeration. When using ingredients other than those employed in this study, their effect on the fate of S. Enteritidis should be taken into account and the use of extra lemon juice and salt per egg yolk is advised. This recommendation is made with regard to the advice of the Chief Medical Office (Department of Health, 1998) that eating raw eggs should be avoided and pasteurised eggs should be used.
References Anon. (1998). Salad dressing. Available from: http://www.prostar.com/ web/gngoat/gn09046.htm (accessed on 10 November, 1998). Anon. (1989). House of Commons Session 1988–1989. Agricultural committee. First report––Salmonella in eggs, Vol. 2. Minutes of evidence and appendices. London, Her Majesty’s Stationary Office. Cheroutre-Vialette, M., & Lebert, A. (2000). Modelling the growth of Listeria monocytogenes in dynamic conditions. Int. J. Food Microbiol., 55, 201–207. Department of Health. (1998). Expert advice repeated on Salmonella and raw eggs. Available from: http://www.coi.gov.uk/coi/deptGDH/coi0316e.ok (accessed October 5, 1998). Donaghy, J. A., & Madden, R. H. (1993). Detection of Salmonella in animal protein by Rappaport–Vassiliadis broth using indirect impediometry. Int. J. Food Microbiol., 17, 281–288. Geeraerd, A. H., & Van Impe, J. F. (1999). Recent developments in describing the combined effect of temperature, pH, sodium chloride and sodium nitrite concentration on the growth of microorganisms. In Third European Symposium on Sous Vide Proceedings (pp. 71– 97). Leuven, Belgium: Katholieke Universiteit Leuven. Genigeorgis, C., Martin, S., Franti, C. E., & Riemann, H. (1971). Initiation of staphylococcal growth in laboratory media. Appl. Microbiol., 21, 934–939. Gordon, S. H., Wheeler, B. C., Schudy, R. B., Wicklow, D. T., & Greene, R. V. (1998). Neural network pattern recognition of photoacustic FTIR Spectra and knowledge-based techniques for detection of mycotoxigenic fungi in food grains. J. Food Prot., 61, 221–230.
533
Hajmeer, M. N., Basheer, I. A., & Najjar, Y. M. (1997). Computational neural networks for predictive microbiology II. Application to microbial growth. Int. J. Food Microbiol., 34, 51–66. Irwin, D. J., Rao, M., Barham, D. W., Pencheon, D. C., Lofts, P., Jones, P. H., O’Manony, M., Soltanpoor, N., Ward, L. R., Threlfall, E. J. (1993). An outbreak of infection with Salmonella Enteritidis phage type 4 associated with the use of raw shell eggs, CDR Review, Vol. 3(13), pp. R179–R183. McMeekin, T. A., Olley, J., Ross, T., & Ratkowsky, D. A. (1993). Predictive microbiology: theory and application. New York: John Wiley & Sons. Membre, J. M., Thurette, J., & Catteau, M. (1997). Modelling the growth, survival and death of Listeria monocytogenes. J. Appl. Microbiol., 82, 345–350. Perales, I., & Garcia, M. I. (1990). The influence of pH and temperature on the behaviour of Salmonella enteritidis phage type 4 in home-made mayonnaise. Lett. App. Microbiol., 10, 19–22. Presser, K. A., Ratkowsky, D. A., & Ross, T. (1997). Modelling the growth rate of Escherichia coli as a function of pH and lactic acid concentration. Appl. Environ. Microbiol., 63, 2355–2360. Radford, S. A. (1994). Factors affecting the fate of Salmonella Enteritidis PT4 in mayonnaise. Ph.D. Thesis. Bath University, UK. Radford, S. A., & Board, R. G. (1993). Review: fate of pathogens in home-made mayonnaise and related products. Food Microbiol., 10, 269–278. Ratkowsky, D. A., & Ross, T. (1995). Modelling the bacterial growth/ no growth interface. Lett. Appl. Microbiol., 20, 29–33. Roberts, D., Hooper, W., & Greenwood, M. (1995). Practical food microbiology (second ed.). London: Public Health Laboratory Service. Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing, explorations in the microstructure of cognition. Cambridge, MA: MIT Press. Sankaran, V., Embrechts, M.J. (1997). MetaNeural for Windows. Available from: http://www.eng.rpi.edu/dept/env-energy-eng/WWW/ NEURAL/metaneural.html (accessed on 15th May, 1997). Saraiva, J., Oliveira, J. C., Oliveira, S., & Hendrickx, M. (1996). Inactivation kinetics of horseradish peroxidase in organic solvents of different hydrophobicity at different water contents. Int. J. Food Sci. Technol., 31, 233–240. Smittle, R. B. (1977). Microbiology of mayonnaise and salad dressing: a review. J. Food Prot., 40, 415–422. Xie, G., Xiong, R., & Church, I. (1998). Comparison of kinetics neural network and fuzzy logic in modelling texture changes of dry peas in long time cooking. Food Sci. Technol. (Lebensm.-Wiss. u.-Technol.), 31, 639–647. Xiong, R., Xie, G., & Edmondson, A. S. (1999). The fate of Salmonella Enteritidis PT4 in home-made mayonnaise prepared with citric acid. Lett. Appl. Microbiol., 28, 36–40. Zhao, T., & Doyle, M. P. (1994). Fate of Enterohemorrhagic Escherichia coli O157:H7 in commercial mayonnaise. J. Food Prot., 57, 780–783.
Neural network modelling of the fate of Salmonella enterica serovar Enteritidis PT4 in home-made mayonnaise prepared with citric acid R. Xiong a, G. Xie b, A.S. Edmondson b, J-F. Meullenet a
a,*
Department of Food Science, University of Arkansas, 2650 N Young Avenue, Fayetteville, AR 72704, USA b Food Research Group, Leeds Metropolitan University, Calverley Street, Leeds LS1 3HE, UK Received 19 September 2001; received in revised form 12 March 2002; accepted 12 March 2002
Abstract Fifty-four mayonnaise recipes were generated by the central composite design and tested for microbiological safety at two temperatures (5 and 22 °C). The content of oil: (150–350 ml), egg yolk (10–35 g), citric acid (4.98% w/v) (10–40 g), salt (0–3 g), mustard (0–2 g), sugar (0–1 g) and white pepper (0.25 g) varied among the different recipes. The fate of Salmonella enterica serovar Enteritidis PT4 in mayonnaise products was investigated by both viable count and presence/absence tests and modelled by neural networks. This study demonstrated that feed-forward neural networks were incapable of modelling the survival/growth curves of S. Enteritidis PT4 as a one-step-procedure model, but were capable of modelling the presence/absence of the organism. Ó 2002 Published by Elsevier Science Ltd. Keywords: Neural network; Modelling; Salmonella; Mayonnaise; Citric acid
1. Introduction Mayonnaise is a widely consumed product, forming the foundation of one-half of all salad dressings and the basis of many other products such as tartare sauce, coleslaw etc. (Radford & Board, 1993; Anon, 1998). A wide range of commercial mayonnaises are available but many individuals and caterers still prefer to use homemade mayonnaise due to its lower tartness and better texture (Anon, 1989; Radford & Board, 1993). However, home-made mayonnaise has been associated with food poisoning outbreaks caused by Salmonella, Staphylococcus aureus, Bacillus, Campylobacter and other bacteria (Smittle, 1977; Irwin et al., 1993; Radford & Board, 1993). This has led to extensive studies on the fate of microorganisms in mayonnaise. The fate of microorganisms is determined by the pH of mayonnaise, type and concentration of the acidulant, the time and temperature of storage together with the antimicrobial activity of other ingredients such as salt, garlic, sugar, mustard, oil, egg white and so on (Smittle, 1977; Perales & Garcia, 1990; Radford & Board, 1993; Radford, 1994;
*
Corresponding author. Tel.: +1-501-575-4605; fax: +1-501-5756936. E-mail address: [email protected] (J.-F. Meullenet). 0956-7135/02/$ - see front matter Ó 2002 Published by Elsevier Science Ltd. PII: S 0 9 5 6 - 7 1 3 5 ( 0 2 ) 0 0 0 4 0 - 3
Zhao & Doyle, 1994). However, there is little information on the synergistic effects of mayonnaise ingredients and storage temperature on the behaviour of organisms. To investigate the single and combined contributions of ingredients and storage temperature to the safety of mayonnaise products, predictive microbiology may be applied. This is an approach to establish predictive mathematical models of growth, survival and death of food-borne microorganisms as affected by environmental factors and processing conditions. Since its successful applications offer many benefits to the practice of food microbiology, there is growing interest internationally (McMeekin, Olley, Ross, & Ratkowsky, 1993). Neural network, originally developed for mimicking human brain processes, is an artificial intelligence approach and has the ability to handle complicated information-processing problems. It has recently gained more and more attentions due to great progress in the technology of computer hardware and software and has been applied in many areas including predictive microbiology. Hajmeer, Basheer, and Najjar (1997) developed a neural network for prediction of the modified Gompertz growth model parameters. The neural network was found to yield a better agreement with experimentally measured data as compared with data predicted by polynomial models. Geeraerd and Van Impe (1999) extended neural networks to model the specific growth
526
R. Xiong et al. / Food Control 13 (2002) 525–533
rate and the lag time duration for Shigella flexneri in BHI medium as a function of temperature, pH, NaCl (%) and NaNO2 (ppm) and compared the neural networks with polynomial models. Neural network analyses of FTIR-PAS spectra of corn kernels can be used to recognize the syndrome of spectral features caused by fungal infection by A. flavus (Gordon, Wheeler, Schudy, Wicklow, & Greene, 1998). Recently, Cheroutre-Vialette and Lebert (2000) developed a recurrent neural network for the prediction of Listeria monocytogenes growth under pH and aw variable conditions. Neural networks have a great potential in predictive microbiology, but few studies have used neural networks for modelling both the growth/survival and the presence/absence of Salmonella in a food system. The purpose of this study was to model both the viable count and the presence/ absence of S. Enteritidis PT4 in mayonnaise products by neural networks.
2. Materials and methods 2.1. Materials All food materials used in this investigation were purchased from a local supermarket in Leeds, UK. The ingredients used were pure sunflower oil, large size eggs, cooking salt, sugar, white ground pepper and English mustard powder. Because eggs are perishable foods, they were purchased one day before the preparation commenced. Using citric acid (anhydrous) (Fisher Scientific UK Ltd., Leicestershire, UK), a solution of citric acid was made in the laboratory at a concentration of 4.98% (w/v) (Xiong, Xie, & Edmondson, 1999). 2.2. Basic mayonnaise recipe and preparation procedure The following basic recipe was used as it could be easily modified for this investigation: 250 ml pure sunflower oil, 20 g egg yolk (large size), 25 g citric acid solution (4.98% w/v), 1.5 g salt, 1.0 g English mustard, 0.5 g sugar, 0.25 g white pepper. This basic recipe was used to generate other recipes by the Central Composite Design method (see Experimental Design). The mayonnaise was prepared as follows: (1) all ingredients except oil were placed in a clean bowl; (2) the ingredients were mixed thoroughly by an electronic mixer and (3) oil was added drop by drop until the formation of an emulsion and then poured steadily whilst blending at high speed. After preparation, the products were subjected to pH and aw measurements. 2.3. Experimental design The Central Composite Design in MINITAB for WINDOWS (Version 11, Minitab Inc., PA, USA) was
used because the surface response method was utilized in the project. The contents of oil, egg yolk, citric acid, salt, mustard and sugar were varied while the content of white pepper was kept constant. There were two reasons to keep the pepper content unchanged: (1) the maximum number of factors used in the Central Composite Design was six and (2) pepper has been shown to have no effect on Salmonella (Radford, 1994). The fifty-four recipes (treatments) were generated by the central composite design (Table 1). However, it was found that four recipes (Recipes 1, 6, 16 and 30) could not produce mayonnaise when 10 g citric acid and 10 g egg yolk were used, but produced mayonnaise when 25 g citric acid and 15 g egg yolk were used. 2.4. Test organism and culture A strain of Salmonella enterica serovar Enteritidis PT4, isolated by the Central Public Health Laboratories in Colindale (UK) following an outbreak of food poisoning attributed to mayonnaise, was used throughout this work. The culture of S. Enteritidis PT4 was maintained on a slope of Nutrient Agar (Oxoid). The slope was inoculated with S. Enteritidis PT4 and incubated at 37 °C for 24 h and then stored at room temperature. Subcultures were made every three months. The working culture was maintained on XLD agar (Oxoid) by growing up a culture at 37 °C for 48 h and then storing at 4 °C. The working culture was subcultured weekly. 2.5. Inoculation of mayonnaise A loopful of S. Enteritidis PT4 from a XLD agar at 4 °C was inoculated into 10 ml of 0.1% peptone (Oxoid) containing 20% egg yolk emulsion (Oxoid). The culture was incubated statically at 37 °C for 24 h. Experimentation showed that this produced the stationary phase. The culture was then added to the 300 g fresh mayonnaise samples at the rate of 1 ml per 100 g to give approximately 106 organisms per gram, and blended with a Colworth stomacher (Seward, London, UK) for 2 min. No separation of mayonnaise was observed due to blending. Each contaminated mayonnaise samples were immediately transferred into a 400 ml sterile plastic container covered with a lid, and stored at 22 1 and 5 1 °C, respectively. After one week to 10 days, some low acid samples stored at 22 °C showed some level of phase separation and spoilage by molds. 2.6. Microbiological analysis For viable counts, samples were taken at time intervals after contamination. At each time interval 10 g contaminated mayonnaise was weighed into a stomacher bag (Seward), and then 90 ml of 1% Buffered
R. Xiong et al. / Food Control 13 (2002) 525–533
527
Table 1 The experimental design table Recipe
1m 2 3 4 5 6m 7 8 9 10 11 12 13 14 15 16m 17 18 19 20 21 22 23 24 25 26 27 28 29 30m 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
Oil (ml)
Yolk (g)
Citric acid (g)
Salt (g)
Mustard (g)
Sugar (g)
Pepper (g)
350 150 350 150 250 350 150 350 250 350 150 150 150 250 150 350 350 150 150 150 350 350 350 350 250 350 250 250 350 250 150 150 350 350 150 250 150 150 150 250 250 250 250 250 250 250 250 300 250 250 200 250 250 250
25 30 30 30 20 25 10 30 20 30 10 10 30 20 10 25 30 10 30 10 10 30 30 10 20 10 20 20 30 25 30 30 10 30 30 20 10 10 30 20 20 20 20 20 20 20 25 20 20 20 20 15 20 20
15 40 10 10 25 15 40 10 25 40 10 40 10 25 40 15 10 10 10 40 40 40 40 40 25 40 25 25 10 15 10 40 40 40 40 25 10 10 40 25 25 32.5 25 25 25 25 25 25 17.5 25 25 25 25 25
3 3 0 0 1.5 0 3 3 1.5 3 3 0 3 1.5 0 3 0 0 0 3 0 0 0 3 1.5 3 1.5 1.5 3 0 3 3 0 3 0 1.5 0 3 0 1.5 1.5 1.5 1.5 0.75 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 2.25
2 2 2 2 1 2 2 2 1 2 2 0 0 1 2 0 0 2 0 0 2 2 0 0 1 2 1 1 0 0 2 0 0 0 0 1 0 0 2 1 1.5 1 0.5 1 1 1 1 1 1 1 1 1 1 1
1 0 1 0 0.5 0 1 0 0.5 1 0 1 0 0.5 0 0 0 1 1 0 1 0 1 1 0.5 0 0.5 0.5 1 1 1 1 0 0 0 0.5 0 1 1 0.5 0.5 0.5 0.5 0.5 0.75 0.5 0.5 0.5 0.5 0.25 0.5 0.5 0.5 0.5
0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
m means modified.
Peptone Water (BPW) (Oxoid) were added and the mixture was homogenized for 1 min with a Colworth stomacher (Seward). Serial tenfold dilutions in 0.1% peptone diluent were plated onto XLD agar (Oxoid)
using the surface spread method (Roberts, Hooper, & Greenwood, 1995). The XLD agar plates were incubated at 37 °C for at least 24 h and then the viable colonies were enumerated.
528
R. Xiong et al. / Food Control 13 (2002) 525–533
For presence/absence tests a 25 g mayonnaise sample was added to 225 ml 1% BPW in a sterile container at each time interval (Roberts et al., 1995). The mixture was shaken vigorously for 1–2 min and incubated at 37 °C for 24 h. After incubation, 0.1 ml of the mixture or enrichment was added to 4 ml Rappaport–Vassiliadis (RV) R10 broth (Difco) in prepared indirect RABIT tubes (Rapid Automated Bacterial Impedance Technique, Don Whitley Scientific, Shipley, UK). The RABIT tubes were placed into a RABIT incubator block and incubated at 42 °C for 18 h (Donaghy & Madden, 1993). If the RABIT detected the growth of the organisms, the result was recorded as presence of Salmonella (positive result); otherwise, absence of Salmonella (negative result). All the presence/absence experiments were performed in duplicate. 2.7. Model development To model response-time curves, two procedures are available, i.e. the one-step procedure and the two-step procedure. In the one-step procedure, there are no intermediate steps or stages involved in the modelling process, i.e. all the response-time curves involved are fitted at a time by a one-step-procedure model that uses time and all the environmental factors as independent variables. In this study, a neural network was used as a one-step-procedure model to examine the feasibility for fitting the response-time curves for S. Enteritidis PT4. The structure of the interconnected neurons in a neural network depends on the complexity of a given problem. The number of neurons in the input and the output layers are determined by the number of input and output variables in the problem. In this work, a three-layered feed-forward neural network was used. The input layer consisted of nine neurons (oil, yolk, citric acid, salt, mustard, sugar, white pepper, temperature and time) and one bias, the output layer of one neuron (LSF ¼ logðN =N0 Þ). The presence of the bias in a neural network prevents the possible trapping to a local minimum in the gradient descent search, allows the approximation of both odd and even functions and aids in the rapid convergence error function to a best point on the error surface in order to reach the minimum error value (Rumelhart & McClelland, 1986). The number of neurons in the hidden layers is related to the performance of a neural network. Too few hidden neurons limit the ability of the network to model the problem, too many can result in memorising the input/output pair patterns presented in the training process. The optimal number of neurons in the hidden layer is usually determined by trial and error. A free neural network software package (MetaNeural for Windows) from the Internet (Sankaran & Embrechts, 1997) that uses a backpropagation training algorithm was used to train the neural network. In this study, three factors (number of hidden
neurons, learning rate and momentum term) were optimised using Random Centroid Optimisation (Xie, Xiong, & Church, 1998). In the optimisation process a total of 50 sets of randomly selected values was used and the number of training cycles was 20,000. The optimum number of hidden neurons, learning rate and momentum term was obtained as 5, 0.0254 and 0.0267, respectively. Consequently, the topology of the neural network used in the present work was 9-5-1 (the biases in both the input layer and the hidden layer were not included here because they were added automatically by MetaNeural). This neural network is referred to as NN model I. The number of training cycles or iterations in neural network training is usually determined by trial and error and by training the optimised neural network on the entire database. The number of training cycles was varied and the root mean square error (RMSE) was calculated. It was found that the error stabilised at about 20,000 iterations. Any further increase in the number of iterations would not induce any significant reduction in error but might increase the computing time and result in over-fitting. 20,000 training cycles were therefore used in the model training process. The training process was repeated until the neural network’s performance was satisfactory, i.e., until the RMSE value satisfied the pre-set requirement (error tolerance ¼ 0:01 in this case) or the maximum number of iterations (20,000) was reached. 2.8. Modelling the presence/absence of Salmonella in mayonnaise A three-layered feed-forward neural network was used to model the presence/absence of S. Enteritidis PT4 in mayonnaise. The input layer consisted of nine neurons (oil, yolk, citric acid, salt, mustard, sugar, white pepper, temperature and time) and one bias, the output layer of one neuron (the presence value). The presence value was 1.0 if one of the presence tests was found to be positive (þ). Otherwise, it was 0.0. The optimal number of neurons in the hidden layers was determined as described previously and a bias was added automatically in the hidden layer. In terms of RMSE values, the structure of the optimal neural network model was 9-101, referred to in subsequent discrimination as NN model II. The satisfactory number of iterations was found to be 30,000 for the training process. 2.9. Validation experiments To verify neural networks, validation experiments are needed. Using the Random Data method in MINITAB for WINDOWS, ten recipes were randomly generated and are listed in Table 2. These recipes were not used in the original experiments but the levels of each ingredient
R. Xiong et al. / Food Control 13 (2002) 525–533
529
Table 2 The validation experiments Recipe
Oil (ml)
Yolk (g)
Citric acid (g)
Salt (g)
Mustard (g)
Sugar (g)
Pepper (g)
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10
320 240 190 270 180 260 210 330 350 160
28 24 16 22 15 16 19 29 30 29
25 16 19 23 21 31 15 18 16.7 10
2.4 1.7 1.1 0.6 2.2 0.8 1.0 0.4 3 1.9
0.6 1.9 1.7 0.7 1.3 0.2 0.0 1.4 0.0 1.5
0.0 0.7 0.0 0.5 0.2 0.8 0.4 0.9 1.0 0.6
0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
or factor were within the range tested: oil, 150–350 ml; egg yolk, 10–30 g; citric acid, 10–40 g; salt, 0–3 g; mustard, 0–2 g; sugar, 0–1 g.
3. Results and discussion 3.1. Modelling the fate of S. Enteritidis PT4 in mayonnaise In theory, a one-step-procedure model can be obtained by combining a primary model with a secondary model, but this is at the cost of making the regression process much more difficult (Saraiva, Oliveira, Oliveira, & Hendrickx, 1996). An advantage of the one-stepprocedure model is that its standard deviation is much smaller than that of the two-step-procedure model (Saraiva et al., 1996; Membre, Thurette, & Catteau, 1997). Hajmeer et al. (1997) suggested that neural networks could be utilised as a one-step-procedure model to fit the growth/survival curves. In this study, a total of 108 response-time curves were obtained from the viable
count tests. After examining these curves, it was found that most of them did not follow a linear relationship, i.e. first order kinetics. Those curves were then fitted to a three-layer feed-forward neural network model (NN model I). The RMSE value obtained was 0.0379. To reduce the RMSE value the LSF values were transformed by log, root squares and other functions. Other methods such as adding extra hidden neurons and an extra hidden layer, reducing the number of input variables, increasing/decreasing the number of iterations, and even varying learning rate and momentum term were also tested. Unfortunately, the neural network could not be converged on a desired solution. Using the trained neural network, LSF values for all the response-time curves were predicted, and then the R2 and RMSE values for each individual response-time curve were calculated (data not shown). In terms of R2 values, the overall performance of the neural network was surprisingly poor. After examining the predicted curves it was found that most showed unacceptable curve shapes. As examples, the predicted curves for the first two recipes (Recipes 1, 2) at 5 and 22 °C are presented in
Fig. 1. Comparison of observed LSF values ðÞ and the LSF values (––) predicted by neural network (NN model I) (LSF ¼ log surviving fraction of Salmonella enterica serovar Enteritidis PT4 in home-made mayonnaise).
530
R. Xiong et al. / Food Control 13 (2002) 525–533
Table 3 Observed and predicted presence (þ)/absence () of Salmonella in mayonnaise Recipe 1m 2 3 4 5 6m 7 8 9 10 11 12 13 14 15 16m 17 18 19 20 21 22 23 24 25 26 27 28 29 30m 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
Observed at 22 °C
Predicted at 22 °C
Observed at 5 °C
Predicted at 5 °C
24 h
48 h
72 h
24 h
48 h
72 h
72 h
168 h
72 h
168 h
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ þ
m means modified.
Fig. 1. It is evident from the figure that the neural network is not capable of modelling the response-time curves obtained in this study. Another drawback is that important and widely-used kinetics such as lag time
duration, specific growth rate, maximum population, stationary time duration, death rate constant cannot be derived from the one-step-procedure neural network.
R. Xiong et al. / Food Control 13 (2002) 525–533
3.2. Modelling of the presence/absence of Salmonella in mayonnaise The presence or absence of S. Entertidis PT4 in mayonnaise was modelled by a neural network (NN model II). Using the trained NN model II, the presence value was predicted. If the calculated presence value was less than 0.5, the prediction was negative (); otherwise, it was positive (þ). The results of modelling presence/absence of Salmonella in mayonnaise are listed in Table 3. By comparing the observed presences/absences with the predicted ones, the neural network gave very good predictions (100% corrections). Genigeorgis, Martin, Franti, and Riemann (1971) were the first to use probability models in predictive microbiology. In probability modelling, the focus of attention is directed towards deciding whether a microorganism might or might not grow (Ratkowsky & Ross, 1995). Probability modelling is an alternative for modelling the presence/ absence of Salmonella in mayonnaise, on which further research is needed. 3.3. Validation analysis Competence of a model, especially a neural network, must be validated before being put to use. The trained neural network (NN model II) for the presence/absence tests was validated and the results are presented in Table 4. The data obtained previously (Xiong et al., 1999) were also used to evaluate NN model II although 0.5 g of white pepper used previously exceeded the 0.25 g used in this study. The results showed that NN model II gave a good prediction. The correction rates for predicting the presence and absence of Salmonella after 72 h storage
531
are 100% and 70%, respectively. For Recipes v2, v4 and x3 (Table 4), the organism was not detected after 72 h storage, but the NN model II predicted the presence of Salmonella. This false presence prediction was on the safe-side. As is the case with all modelling techniques, it is not recommended that any trained neural network be used for extrapolation. In other words, the user has to restrict the use of the trained neural network(s) within the limits on which they were developed. These limits for mayonnaise tested in this study are time (0–72 h at 22 °C and 0–168 h at 5 °C), temperature (5–22 °C), oil (150– 350 ml), egg yolk (10–35 g), citric acid (10–40 g), salt (0– 3 g), mustard (0–2 g), sugar (0–1 g), and white pepper (0.25 g). 3.4. Factors affecting the presence/absence of S. Enteritidis PT4 in mayonnaise Predictive microbiology applied to food products can be used to describe the effects of environmental factors and their interactive effects on the survival or presence/ absence of microorganisms. Mathematically, analysis of single and combined effects of environmental factors on the presence/absence of S. Enteritidis PT4 in home-made mayonnaise can be accomplished by altering one or two of the factors in the models while holding all others constant. After validation, the trained NN model II was used to analyse the single and combined effects of all the ingredients on the presence/absence of S. Enteritidis PT4 in mayonnaise stored at 22 °C. By using the presence value, the effects of single ingredient are presented in Fig. 2 (the combined effects of two or more ingredients are not shown). From the figure, it can be seen that egg yolk, citric acid, salt and temperature were major contributors
Table 4 Observed and predicted presence (þ)/absence () of Salmonella in mayonnaise Observed at 5 °C
Predicted at 5 °C
24 h
48 h
72 h
24 h
48 h
72 h
72 h
168 h
72 h
168 h
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ
þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ
x1 x2 x3 x4 x5 x6
þ þ þ þ þ þ
þ þ
þ þ
þ þ þ þ þ þ
þ þ þ þ þ þ
þ þ þ
þ þ þ þ þ þ
þ þ þ þ þ þ
þ þ þ þ þ þ
þ þ þ þ þ þ
Recipe
Observed at 22 °C
Predicted at 22 °C
x1 to x6 data from Xiong et al. (1999).
532
R. Xiong et al. / Food Control 13 (2002) 525–533
Fig. 2. Effect of oil, egg yolk, citric acid, salt, mustard, sugar, pepper and temperature on the presence value for Salmonella enterica serovar Enteritidis PT4 for the basic recipe mayonnaise stored at 22 °C for 72 h (predicted by NN model II).
to the presence value, while others had negligible effects. As the amount of egg yolk increased, the presence value (approximately probability of presence) increased, whereas it decreased as the amount of citric acid and/or salt and storage temperature increased. It was interestingly found that there were ‘thresholds’ for salt and temperature after which the presence value began to drop rapidly, which indicates that the minimum amount or proportion of salt and the minimum temperature were required to inactivate the organism within 72 h. It is evident from Fig. 2 that if the basic recipes contained 20 g or less egg yolk, at least 20 g citric acid and at least 1.5 g salt and the product stored at 22 °C or above, the presence value would be near zero. 20 g egg yolk, 20 g citric acid, 1.5 g salt and 22 °C temperature were called the boundary conditions (maximum or minimum requirements) for the absence of Salmonella in citric acid mayonnaise after 72 h storage. This result is of particular importance to consumers. For low infectious dose pathogens (such as E. coli O157:H7), the growth rate and limits of the organisms are irrelevant because any number of the organisms re-
sults in a high risk of illness (Presser, Ratkowsky, & Ross, 1997). Although it is not possible to guarantee the absence of pathogens in all units of all foods, it is possible to minimise the risk of food poisoning and to quantify the environmental factors that affect it. In this context, neural network is a very useful tool for food safety management. As far as is known, this study is the first attempt to use a neural network for modelling the presence/absence of Salmonella in mayonnaise and for quantifying the factors that affect the risk of food poisoning.
4. Conclusions Although neural network has failed to model the viable count of Salmonella in mayonnaise, it has been successful in modelling its presence/absence in the products. The results have shown that the bacterial presence and absence interface for citric acid mayonnaise stored for 72 h was determined by the following factors: 20 g egg yolk, 20 g citric acid, 1.5 g salt and a storage tem-
R. Xiong et al. / Food Control 13 (2002) 525–533
perature of 22 °C. It is recommended that to maintain S. Enteritidis PT4-free mayonnaise prepared with pure lemon juice (citric acid concentration 5% w/v), recipes containing at least 20 ml pure lemon juice per egg yolk (assuming maximum weight of egg yolk to be 20 g (Xiong et al., 1999)) and at least 2 g salt per egg yolk should be used and the product should be held for at least 72 h at 22 °C or above before consumption or refrigeration. When using ingredients other than those employed in this study, their effect on the fate of S. Enteritidis should be taken into account and the use of extra lemon juice and salt per egg yolk is advised. This recommendation is made with regard to the advice of the Chief Medical Office (Department of Health, 1998) that eating raw eggs should be avoided and pasteurised eggs should be used.
References Anon. (1998). Salad dressing. Available from: http://www.prostar.com/ web/gngoat/gn09046.htm (accessed on 10 November, 1998). Anon. (1989). House of Commons Session 1988–1989. Agricultural committee. First report––Salmonella in eggs, Vol. 2. Minutes of evidence and appendices. London, Her Majesty’s Stationary Office. Cheroutre-Vialette, M., & Lebert, A. (2000). Modelling the growth of Listeria monocytogenes in dynamic conditions. Int. J. Food Microbiol., 55, 201–207. Department of Health. (1998). Expert advice repeated on Salmonella and raw eggs. Available from: http://www.coi.gov.uk/coi/deptGDH/coi0316e.ok (accessed October 5, 1998). Donaghy, J. A., & Madden, R. H. (1993). Detection of Salmonella in animal protein by Rappaport–Vassiliadis broth using indirect impediometry. Int. J. Food Microbiol., 17, 281–288. Geeraerd, A. H., & Van Impe, J. F. (1999). Recent developments in describing the combined effect of temperature, pH, sodium chloride and sodium nitrite concentration on the growth of microorganisms. In Third European Symposium on Sous Vide Proceedings (pp. 71– 97). Leuven, Belgium: Katholieke Universiteit Leuven. Genigeorgis, C., Martin, S., Franti, C. E., & Riemann, H. (1971). Initiation of staphylococcal growth in laboratory media. Appl. Microbiol., 21, 934–939. Gordon, S. H., Wheeler, B. C., Schudy, R. B., Wicklow, D. T., & Greene, R. V. (1998). Neural network pattern recognition of photoacustic FTIR Spectra and knowledge-based techniques for detection of mycotoxigenic fungi in food grains. J. Food Prot., 61, 221–230.
533
Hajmeer, M. N., Basheer, I. A., & Najjar, Y. M. (1997). Computational neural networks for predictive microbiology II. Application to microbial growth. Int. J. Food Microbiol., 34, 51–66. Irwin, D. J., Rao, M., Barham, D. W., Pencheon, D. C., Lofts, P., Jones, P. H., O’Manony, M., Soltanpoor, N., Ward, L. R., Threlfall, E. J. (1993). An outbreak of infection with Salmonella Enteritidis phage type 4 associated with the use of raw shell eggs, CDR Review, Vol. 3(13), pp. R179–R183. McMeekin, T. A., Olley, J., Ross, T., & Ratkowsky, D. A. (1993). Predictive microbiology: theory and application. New York: John Wiley & Sons. Membre, J. M., Thurette, J., & Catteau, M. (1997). Modelling the growth, survival and death of Listeria monocytogenes. J. Appl. Microbiol., 82, 345–350. Perales, I., & Garcia, M. I. (1990). The influence of pH and temperature on the behaviour of Salmonella enteritidis phage type 4 in home-made mayonnaise. Lett. App. Microbiol., 10, 19–22. Presser, K. A., Ratkowsky, D. A., & Ross, T. (1997). Modelling the growth rate of Escherichia coli as a function of pH and lactic acid concentration. Appl. Environ. Microbiol., 63, 2355–2360. Radford, S. A. (1994). Factors affecting the fate of Salmonella Enteritidis PT4 in mayonnaise. Ph.D. Thesis. Bath University, UK. Radford, S. A., & Board, R. G. (1993). Review: fate of pathogens in home-made mayonnaise and related products. Food Microbiol., 10, 269–278. Ratkowsky, D. A., & Ross, T. (1995). Modelling the bacterial growth/ no growth interface. Lett. Appl. Microbiol., 20, 29–33. Roberts, D., Hooper, W., & Greenwood, M. (1995). Practical food microbiology (second ed.). London: Public Health Laboratory Service. Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing, explorations in the microstructure of cognition. Cambridge, MA: MIT Press. Sankaran, V., Embrechts, M.J. (1997). MetaNeural for Windows. Available from: http://www.eng.rpi.edu/dept/env-energy-eng/WWW/ NEURAL/metaneural.html (accessed on 15th May, 1997). Saraiva, J., Oliveira, J. C., Oliveira, S., & Hendrickx, M. (1996). Inactivation kinetics of horseradish peroxidase in organic solvents of different hydrophobicity at different water contents. Int. J. Food Sci. Technol., 31, 233–240. Smittle, R. B. (1977). Microbiology of mayonnaise and salad dressing: a review. J. Food Prot., 40, 415–422. Xie, G., Xiong, R., & Church, I. (1998). Comparison of kinetics neural network and fuzzy logic in modelling texture changes of dry peas in long time cooking. Food Sci. Technol. (Lebensm.-Wiss. u.-Technol.), 31, 639–647. Xiong, R., Xie, G., & Edmondson, A. S. (1999). The fate of Salmonella Enteritidis PT4 in home-made mayonnaise prepared with citric acid. Lett. Appl. Microbiol., 28, 36–40. Zhao, T., & Doyle, M. P. (1994). Fate of Enterohemorrhagic Escherichia coli O157:H7 in commercial mayonnaise. J. Food Prot., 57, 780–783.