Department of Food Science and Technology, Tokyo University of Marine Science and Technology, Tokyo , Japan

Transcription

1 Artificial neural network model flexibly applicable to retort processes under various operating conditions Yvan Llave, Tomoaki Hagiwara, and Takaharu Sakiyama Department of Food Science and Technology, Tokyo University of Marine Science and Technology, Tokyo , Japan ABSTRACT An artificial neural network (ANN) model was developed for prediction of the cold spot temperature profile during retort processing using pre-gelatinized starch dispersion (STD) as a model food. Samples of 3%, %, and 2% of STDs were prepared as follows: Corn starch powder was mixed with distilled water at 9 o C for 3 min. Each of them was filled in a retort pouch (3 23 mm), vacuum sealed, and finally processed in a retort. Sterilization was conducted under the following conditions: retort temperature (8-22 o C), holding time (2-22 min), and rotational speed (-25 rpm). Back-propagation network was chosen as the network model. The input variables for the model were retort temperature, and current and past temperatures of the cold spot (T i, T i-, and T i-2 ) at every time step. The output variable was the temperature of the cold spot at the next time step T i+. A model with 2 hidden layers, which contained and 5 neurons, respectively, was the best model among tested. It gave relative errors lower than 4% in the prediction of F value. Using the model developed, prediction of a whole profiles of the cold spot temperature were tested, starting from temperature data of the first 3 time steps with a whole course of retort temperature. The results showed very good performance of the model not only for STDs but also for several foods. Keywords: Artificial neural network; retort process; starch dispersion. I TRODUCTIO In retort processing it is essentially important to know cold spot temperature of products as accurately as possible for the evaluation of lethality attained. In recent years food engineers have developed computer models capable of simulating thermal processing based on engineering mathematics and scientific principles of heat transfer [-3]. Use of numerical solutions to mathematical heat transfer equations makes it possible to predict cold spot temperature of products even if retort temperature varies during processing. However, many parameters are required in the model: thermo-physical properties (heat capacity, density, thermal conductivity) of products and heat transfer parameters. For precise simulation, we need precise values of the parameters. Moreover, due to dynamic nature of retort process, the properties and the parameters cannot be considered uniform and constant. It is often hard to obtain the precise change in all the parameters. In some cases, such as starch-based food products processed in thermal system under intermittent agitation, brokenheating (BH) behavior is known to ocurr. BH behavior is characterized by a heat penetration curve with more than two straight portions, indicating transition of heat transfer mechanism due to gelatinization of starch. Most of the traditional models have a weak point to cope with such change in heat transfer mechanism. A more convenient technique with flexible applicability would be desired for retort process simulation. The concept of artificial neural network (ANN) has recently gained widespread popularity in many disciplines of engineering and science. Attractive features of ANN are their ability to learn from data sets to find complicated relationships in them. In this study, an ANN model was developed as an alternative tool for prediction of the cold spot temperature profile of retort products. Starch dispersion (STD) was used as a model food because they could show transition of heat transfer mechanism, so-called BH behavior. ANN model was trained with cold spot temperature profiles, including those of BH behaviors, experimentally obtained. MATERIALS & METHODS Samples of 3%, %, and 2% of STDs were prepared as follows. Corn starch powder (SIGMA-ALDRICH, USA) was mixed with distilled water thoroughly and brought to heating in a jacketed kettle mixer (Shinagawa Twin Mixer STM-5) at 9 o C for 3 min for pre-gelatinization. During the pre-gelatinization, water was added to compensate for evaporation losses. After being cooled to room temperature, the pre-

2 gelatinized STD was filled in a retort pouch (3 x 23 mm) PET2/AL9/NY5/CP6, and vacuum sealed (Yoshikawa FVCII). For validation of accuracy of ANN model, several foods were prepared and subjected to retort processing: corn soup, rice, whole corn, curry paste, and fish fillet according to traditional way of preparation for each retort foods. After pre-treatment, each sample was filled in a retort pouch of variable size according to its size. A set of 4 thermocouples (Shibaura Thermistor type 3 THR) were positioned at the geometric center of each pouch placed at the cold spot of the retort to obtain heat penetration data. Sterilization was carried out using stationary or rotary mode in a pilot scale retort (Hisaka RCS-4RTGN). After a pre-heating step (9 o C x 3 min), sterilization was conducted under the following conditions: retort temperature (8, 2, and 22 o C), holding time (2, 5, 2, and 22 min), and rotational speed (, 5,, 5, 2, and 25 rpm). Heat penetration data were recorded every 2s intervals via a data logger. A selected number of heat penetration data were processed using an ANN software (NeuralWorks Professional II/Plus). Back-propagation (BP) network was chosen as the network model. The input variables for the model were retort temperature, and current and past temperatures of the cold spot (T i, T i-, and T i-2 ) at every time step. The output variable was the temperature of the cold spot at the next time step T i+. In order to obtain the best topology of the network, parameter functions, the number of hidden layers, and the number of neurons contained in each layer were determined on a trial-and-error basis to give the smallest difference between the predicted temperature and the experimental data. In addition, the predicted time-temperature profile was compared with the experimental data in terms of accumulated lethality (F value) calculated by Eq. (). F = t ( T T) / z dt () where T = 2. o C and z = o C. Finally, the ANN model was tested for prediction of the temperature profiles of not only STDs under other retort conditions, but also of real foods. Pouches containing those foods were sterilized at 2 o C for different times to assure a final F value of 6. at least. RESULTS & DISCUSSIO Heat penetration curves for STDs Major factors affecting retort sterilization process of STDs considered in this study are the starch concentration and the rotational speed of retort. Figure shows typical heat penetration curves for 3% and 2% STDs. For 2% STD, the curve gave a single straight line after several minutes of initial lag period. On the other hand, the curve for 3% STD was approximated by two straight lines, indicating two stages of heat penetration recognized as BH. The significant change in heating rate suggested increase in heat transfer resistance at the break point (intersection of two lines), which is probably attribute to sol-gel transition caused by starch gelatinization, as known for many starch-based foods [3-5]. 3% L T RT-T ( ) 2% L 2% S TD, 5rpm 3% L2 3% STD, 25rpm T im e (m in ) Figure. Typical heating curves for 3% and 2% STDs under retort treatment

3 Construction of A model Sets of 58,997 and 5,268 data obtained through thermal processing of STDs were used for the network training and testing respectively. The steps taken for construction of ANN model are summarized as follows. The optimal configuration was selected from 24 ANN configurations (from to two hidden layers, up to fifteen neurons in each hidden layer), using training data sets and taking mean relative error as a measure of predictive performance. The coefficient of determination, R 2, between the predicted values by ANN model and the observed ones (desired output) was also used as another measure. As the result, a model BP with 2 hidden layers that contained and 5 neurons, respectively, was found the best model among tested. The final BP model, obtained by using Extended Delta-Bar-Delta learning rule and hyperbolic tangent transfer function, gave a good prediction at each time step. For example, in the case shown in Figure 2, the relative errors were within.5% for the cold spot temperature of 3% STD. This resulted in a relative error of -3.5% for F value in this case (see Table ). Temperature ( o C) Experimental temp Predicted temp Retort temp. Fo value observed Fo value predicted Time (min) F (min) Figure 2. Experimental and Predicted time-temperature profile and calculated F value of 3% STD, rotated at 25 rpm and heated at 2 o C for 5 min. Figure 3 compares the predicted and observed values of the cold spot temperature for 3% STD (during the retort processing shown in Fig. 2). It can be observed that there is a very high correlation between them (R 2 >.999), indicating a nice adjustment of ANN model to the process behavior. 4 T predicted ( ) R 2 =.9998 BP T observed ( ) Figure 3. Predicted vs. observed temperature at the cold spot using BP for 3% STD. The ANN model developed predicts the next step temperature from the last three temperatures already known. The prediction performance mentioned above was based on such step-by-step mode of prediction. However, we can put an output temperature into the input at the next step. Thus prediction of whole cold spot

4 temperature profile was tested, starting from temperatures at the first 3 time steps together with a whole course of retort temperature. This prediction mode is referred to as continuous prediction, while the original one is referred to as simple step prediction, hereafter. Table. Comparison of error estimation for validation processes of STDs. Simple step prediction Continuous prediction Validation Processes Time-temperature profile F Time-temperature profile F R 2 MRE RE (%) R 2 MRE RE (%) 3%STD/25 rpm/2 o Cx5min ± ± %STD/ rpm/2 o Cx2min ± ± %STD/5 rpm/2 o Cx2min ± ± %STD/5 rpm/2 o Cx22min ± ± Table shows the comparison of prediction errors of the both modes for validation data sets obtained under several retort conditions. The mean relative errors (MREs) of continuous prediction were found slightly higher than those of simple step prediction for the time-temperature profile. However the relative errors (REs) for F value (calculated with each time-temperature profile) were found opposite. This can be because continuous prediction gave large REs of temperature at beginning, where temperature was too low to affect F value. At higher temperatures, continuous prediction gave rather good prediction, which lowered RE of F value. A similar ANN model architecture, taking known temperatures at three time steps as input, was employed by Gonçalves et al. [6]. They reported that relative prediction errors of F value were lower than 2.6 % for food cans showing purely conductive heat transfer behavior. Although the experimental situations and ANN model construction were not the same, we thus obtained similar, yet better, accuracy of prediction. Moreover, the ANN model developed in this study expanded the scope of the prediction to foods with BH behaviors. Application to various types of food Several foods were selected to evaluate the prediction by BP model obtained above. Results of continuous prediction of time-temperature profile for corn soup are presented in Fig. 4. As shown in Table 2 the good accuracy of the prediction was confirmed also for other foods. Temperature ( o C) Experimental temp Predicted temp Retort temp. Fo value observed Fo value predicted Time (min) F (min) Figure 4. Experimental and Predicted time-temperature profile and calculated F value of corn soup sterilized at 2 o C at rpm.

5 Table 2. Performance of prediction of temperature profiles and Fo value for real foods. Targed food Time-temperature profile F R 2 MRE (%) RE (%) Corn soup ±.5.3% Rice ±.8.8% Fish (T. murphyi) ±.8 3.6% Whole corn ±.6 2.3% Curry paste ±.9 2.9% CO CLUSIO ANN model developed by training with heat penetration data for STDs was found successful to predict time-temperature profile and thermal lethality with high accuracy under variety of processing conditions, irrespective of different heat transfer modes. Its excellent performance for retort foods other than STDs indicated that the obtained model will be useful for prediction and control of retort systems. REFERE CES [] Tucker G.S. 99. Development and use of numerical techniques for improved thermal process calculations and control. Food Control. January, 5-9. [2] Tucker G.S. & Holdsworth S.D. 99. Mathematical modeling of sterilization and cooking processes for heat preserved foods, applications of a new heat transfer model. Trans. IchemE, 69, 5-2. [3] Noronha J., Hendrickx A., Van Loey A. & Tobback P New semi-empirical approach to handle time-variable boundary conditions during sterilization of non-conductive heating foods. Journal of Food Engineering, 24, [4] Yang W.H. & Rao M.A Numerical study of parameters affecting broken heating curve. Journal of Food Engineering, 37, [5] Tattiyakul J., Rao M.A. & Data A.K. 22. Heat transfer to a canned corn starch dispersion under intermittent agitation. Journal of Food Engineering, 54(4), [6] Gonçalves E.C., Minim L.A., Coimbra J.S.R. & Minim V.P.R. 25. Modeling sterilization process of canned foods using artificial neural networks. Chemical Engineering and Processing, 44(2),