Learning rule
4.5 APPLICATIONS IN FOOD THERMAL PROCESSING
Thermal processing is one of the major operations in the food processing and preservation system. It has generally been viewed as an energy-intensive pres-ervation technique, but persists as the most widely used method of prespres-ervation.
One major objective of thermal processing is to destroy pathogenic and spoilage microorganisms present in foods being processed so that they can be stored for extended periods and consumed with no safety concerns. Quality factors in foods are also affected by heat treatments; however, they usually show much higher heat resistance than targeted microorganisms. Therefore, an optimal thermal process procedure for a given food product means that it will result in minimal quality destruction while being sufficient to make the product safe for consump-tion. In order to solve an optimization problem, the key step is to develop a suitable model capable of describing the relationships between inputs and out-puts. To date, a variety of conventional methods have been used for both mod-eling and optimization purposes in food thermal processing areas, from which different models have been developed, including numerical, analytical, and experimental. These conventional methods applied for optimizing thermal pro-cessing operations need specific inputs. First, the models need a knowledge and understanding of relationships between the input and output variables. Second, it is necessary that information of physical and thermal properties of food products being modeled be available. Unlike other disciplines, food processing deals with biomaterials, which show much more complicated thermophysical properties and uncertainties during the processing period. This also results in more complicated relationships between input and output variables. Thus, often it is difficult to use a simple partial differential equation or model to accurately describe the phenomena occurring in food processing operations. In addition, the lower calculation speed of most conventional methods under complex situ-ations of process optimization and control limits them to be optimally applied for online application in industrial processes. Neural network models offer an attractive alternative in such instances. Hence, neural networks are being con-tinually extended to the food thermal processing area as a modeling and opti-mization technique.
Since the 1980s, neural networks have received more and more interest in food processing areas. So far, the applications of neural networks in food processing have covered various areas, such as drying,7–12 fermentation,13,14 extrusion,15–17 freezing,18 baking,19,20 postharvest,21,22 experimental design,23 etc.
The application in food thermal processing began in the mid-1990s. Sablani et al.24 published one of the early reports on the application of neural networks in food thermal processing. They developed a four-layer neural network with three inputs and three outputs to predict optimal sterilization temperatures under different processing conditions. Sablani et al.25 used artificial neural network models for the overall heat transfer coefficient and the fluid-to-particle heat transfer associated with liquid particle mixtures, in cans subjected to end-over-end rotation.
The application of the neural computing approach for prediction of the residence time distribution (RTD) under aseptic processing conditions was reported by Chen and Ramaswamy.26 In this paper, neural networks were explored for modeling two RTD functions: the time-specific (E-type distribution) and the cumulative particle concentration function (F-type distribution) of carrot cubes in starch solutions in a vertical scraped-surface heat exchanger (SSHE) of a pilot-scale aseptic processing system. Neural networks have been used as an alternative tool to the Ball and Stumbo methods, which are the most often used methods for thermal calculation to predict process time and or process lethality in cans during thermal processing.27 A more systematic and in-depth application of neural networks in food thermal processing areas was carried out by Chen and Ramaswamy.28–33 In these studies, separate ANN prediction models were developed involving main input parameters such as retort temperature profile, thermophysical properties of food products, kinetics of microorganisms, and quality factors and outputs, such as the process time, cumulative lethality value, quality retention, unit energy consumption, and transient temperature at the can center. These ANN models were able to be directly used for the process establishment and validation for a given food product, but also could be com-bined with a search technique to build optimal thermal process conditions in order to meet different optimization objectives. Some details of selected studies in thermal processing based on the ANN approach are given in the following sections.
4.5.1 NEURAL NETWORK MODELING OF HEAT TRANSFER TO LIQUID PARTICLE MIXTURES IN CANS SUBJECTED TO END-OVER-END PROCESSING
The overall heat transfer coefficient (U ) and fluid-to-particle heat transfer coefficient (hfp) are fundamental data needed to develop prediction models for the transient temperature of canned foods undergoing agitation thermal pro-cessing, which is necessary to establish and optimize the thermal process schedule for the canned liquid–particle food system. Traditionally, the dimen-sionless correlations are used for development of an experimental model of U and hfp involving other influencing parameters by use of multiple regression analysis. However, selection of appropriate dimensionless groups requires prior knowledge of the phenomena under investigation. Sablani et al.25 devel-oped an artificial neural network (ANN) model for the overall heat transfer coefficient and the fluid-to-particle heat transfer coefficient associated with liquid particle mixtures, in cans subjected to end-over-end rotation. Experi-mental data obtained for U and hfp under various test conditions (shown in Table 4.1) were used for both training and evaluation. Multilayer neural net-works with seven inputs and two output neurons (for a single particle in a can), and six inputs and two output neurons (for multiple particles in a can) were trained. The optimal network was obtained by initial trials as number of hidden layers = 2, number of neurons in each hidden layer = 10, and learning
runs = 50,000. By use of trained NN models with optimal configurations, the prediction performance of all NN models for both U and hfp was found to be higher than 0.98, meaning that the developed NN models could safely be used for prediction of U and hfp under the given experimental conditions. The comparison of NN models and dimensionless regression models using the same experimental data is summarized in Table 4.2. Prediction errors using ANN were less than 3 and 5%, respectively, for U and hfp, which were about 50% better than those associated with dimensionless number models, indicat-ing that the predictive performance of the ANN was far superior than that of dimensionless correlations.
TABLE 4.1
Range of System and Product Parameters Used in the Determination of Heat Transfer Coefficients (U and hfp)
No. Parameter Experimental Range
1 Retort temperature 110, 120, and 130°C 2 Radius of rotation 0, 0.09, 0.19, and 0.27 m 3 Rotation speed 10, 15, and 20 rpm 4 Can headspace 0.0064 and 0.01
5 Test fluid Water and oil
6 Test particle Polypropylene, nylon
7 Particle concentration Single particle, 20, 30, and 40% (v/v) 8 Particle shape and size
Cube 0.01905 m
Cylinder 0.01905 × 0.01905 m
Sphere 0.01905, 0.02225, and 0.025 m 9 Can dimension 307 × 409 (8.73 × 11.6)
TABLE 4.2
Comparison of Error Parameters for Neural Network (NN) Models and Dimensionless Correlation (DC) Models
Single Particle Multiple Particle
U hfp U hfp
Error Parameters DC NN DC NN DC NN DC NN
MAE 17.1 5.11 31.3 17.2 25.1 9.85 75.4 48.1
SDE 25.4 4.76 43.3 16.0 32.0 11.0 63.4 40.7
MRE (%) 5.00 2.46 16.9 5.82 5.70 2.57 8.26 4.52
SRE (%) 3.76 2.51 11.9 7.00 4.65 1.96 7.12 3.90
R2 0.99 0.99 0.83 0.98 0.98 0.99 0.96 0.98
4.5.2 A NEURO-COMPUTING APPROACH FOR MODELING OF RESIDENCE TIME DISTRIBUTIONOF CARROT CUBES INA VERTICAL SCRAPED-SURFACE HEAT EXCHANGER
The residence time distribution (RTD) is one of the important parameters for establishing the aseptic processing of particulate liquids. Although a lot of different models have been developed for describing RTD characteristics using conventional mathematical methods, none of them give a fully satisfactory solution for the RTD covering the wide range of processing conditions. A neuro-computing approach was used by Chen and Ramaswamy26 for modeling two residence time distribution (RTD) functions: the time-specific (E-type distribu-tion) and the cumulative particle concentration function (F-type distribudistribu-tion) of carrot cubes in starch solutions in a vertical scraped-surface heat exchanger (SSHE) of a pilot-scale aseptic processing system. In this study, 356 experi-mental data pairs obtained for E(t) and F(t) under various test conditions, including the concentration of particles, flow rate, particle dimension, and test time, were used for both training and evaluation. The optimal configurations of the neural network model were determined by adjusting the number of hidden layers, the number of neurons in each hidden layer and learning runs, and a combination of learning rule and transfer functions. The results showed that the trained ANN model can accurately map experimental results with R2 value = 0.98 and 0.99 for E and F functions, respectively. The prediction performance of the ANN model under several typical processing conditions is shown in Figure 4.5. The ANN models were also compared with conventional models developed based on multiple variable regression techniques. The comparison indicated that average modeling errors associated with the ANN model were 5.7 and 3.0%, respectively, for E and F, while those for the multiple regression models were 15.5 and 12.3%, meaning that the ANN model had higher precision for predicting E and F functions.
4.5.3 MODELING AND OPTIMIZATION OF CONSTANT RETORT
TEMPERATURE THERMAL PROCESSING USING COUPLED
NEURAL NETWORKS AND GENETIC ALGORITHMS
Modeling and optimization of the thermal processing are of considerable interest and are widely based on conventional mathematical methods. Traditionally, solv-ing an optimization problem consists of two steps. First, different objective function models are developed using mathematical approaches that include regression methods, theoretical analysis models, and differential equations; and then the optimal conditions are sought using one of several search methods, such as direct search, grid search, gold-section method, etc., for single variables, and alternating variable search, pattern search, and Powell’s method for multivari-ables. Like traditional modeling methods, neural networks cannot provide direct answers for optimization problems. In order to be used for optimization pur-poses, neural network models have to be combined with a search technique.
Genetic algorithms (GAs) are a combinatorial optimization technique, searching for an optimal value of a complex objective function by simulation of the bio-logical evolutionary process, based on crossover and mutation, as in genetics.
Chen and Ramaswamy30 found that the combination of GA and ANN models can become an effective tool for optimization problems. This study might be the first report on application of ANN and GA for thermal processing optimization. The focuses of this study were on:
1. Developing ANN models for predicting process time (PT), average quality retention (Qv), surface cook value (Fs), equivalent unit energy consumption (En), temperature difference (g), and ratio of F value from heating to total desired F value (ρ) under constant retort temper-ature (CRT) processing conditions
2. Coupling ANN models and GA to search for the optimal quality reten-tion and the corresponding retort temperature
3. Investigating the effects of main processing parameters on both optimal quality retention and retort temperature
Processing conditions as inputs for ANN models were selected as follows:
retort temperature (RT = 110 to 140°C), thermal diffusivity (α = 1.1 to 2.14 × 10–7 m2/sec), volume of can (V = 1.64 to 6.55 × 10–4 m3), ratio of height to diameter FIGURE 4.5 Comparison of ANN model predictions and experiments for E and F functions.
F(t) F(t)
of can (Rdh = 0.2 to 1.8), total desired lethality value (Fo = 5 to 10 min) at the can center, and quality kinetic destruction parameters — decimal destruction time (Dq
= 150 to 300 min) and their temperature dependence (zq = 15 to 40°C). Six separate ANN models were developed for prediction of process time, average quality reten-tion, surface cook value, equivalent energy consumpreten-tion, final temperature differ-ence at the can center, and lethality ratio, ρ (heating/total lethality), respectively.
The data for training and testing ANN models were obtained from a finite difference computer simulation program. A second-order central composite design was used for constructing experimental data for training ANN models, while an orthogonal experimental design composed of six factors and three levels was used for the generalization of trained ANN models. The hybrid optimization method (shown in Figure 4.6) linking GAs with ANNs was employed for searching the optimal quality
FIGURE 4.6 The procedure of the hybrid optimization method using GAs and ANNs.
Initial population
Compute the fitness of all individuals using ANN
Selection Crossover Mutation
Iteration number ?
Ni
Input Output
Iteration number ?
Y
Y N
N
retention and corresponding retort temperature, and for investigating the effects of main processing parameters on optimal results. ANN-based prediction models successfully described the various outputs of CRT thermal processing (correlation coefficients, R2 > 0.98; relative errors, Er ≤ 3%). The coupled ANN-GA models, verified under several typical processing conditions, could be effectively used for optimization of CRT thermal processing. The main processing parameters and their interactions in the order of their importance with respect to the optimal quality retention and corresponding retort temperature were V > zq > Fo > Rdh and zq > Fo
> Rdh > V, respectively. The studies were later extended to variable retort temperature processes, demonstrating the excellent performance of ANN models.30
4.5.4 ANN MODEL-BASED MULTIPLE-RAMP VARIABLE RETORT
(MRV) TEMPERATURE CONTROLFOR OPTIMIZATION OF THERMAL PROCESSING
Variable retort temperature (VRT) thermal processing has been recognized as an innovative method to improve food product quality and save process times. The key to designing a VRT thermal process is to choose a reasonable (optimal) VRT profile for a given food product and package being thermally processed. The selection of optimal retort temperature profiles with a multistage ramp function involving multiple variables is complex and difficult to handle by conventional optimization methods.33 The study consisted of three parts: (1) developing asso-ciated prediction models using ANN, (2) investigating the sensitivity of VRT parameters to processing results, and (3) searching for the optimal VRT profile using a hybrid optimization technique coupling ANN with GA.
For the first part, three separate ANN models were developed for predictions of process time, average quality retention, and surface cook value, respectively, each as a function of five input variables: ramp time, t, and four step temperatures, T1, T2, T3, and T4. ANN models were trained and tested by two data sets, respectively, which were generated by a computer simulation program of VRT thermal processing. The statistical results of the modeling performance for all ANN models had a correlation coefficient of >0.95 and an average relative error of <2.05%, indicating that these ANN models can be safely used for prediction purposes of VRT thermal processing with a multiple-ramp temperature profile.
In the second part, ANN models-based sensitivity analysis was used for inves-tigation of effects of five MRV parameters on process time (PT), average quality retention (Qv), and surface cook value (Fs). For example, Figure 4.7 shows the effects of individual variables, including four step temperatures and ramp time on process outputs: PT, Qv, and Fs for the small size. As expected normally, the increase of step temperatures resulted in the decrease of process times (Figure 4.7a), but the decrease rate was dependent on the number of steps and temperature values.
If the temperature was less than 119°C, T3 was the most sensitive to PT; if larger than 119°C, then T2 was the most sensitive factor to PT. The effects on the quality retention Qv were illustrated in Figure 4.7b. It showed that T1 and T4 had no effect on Qv, while effects of T2 and T3 were related to the temperature value.
FIGURE 4.7 Effects of MRV parameters on process outputs for small size (V = 1.64 *10–4 m3).
(a) Step temperatures vs process time
20
100 110 120 130 140
Temperature (oC)
PT(min)
T4 T3
T2 T1
(b) Step temperatures vs quality retention
65 70 75 80
100 110 120 130 140
Temperature (oC)
Qv(%)
T4 T3
T2 T1
(c) Step temperatures vs surface cook value
30 35 40 45
100 110 120 130 140
Temperature (oC)
Fs(min)
T4 T3
T2 T1
(d) Ramp time vs process time
20
(e) Ramp time vs quality retention
70
(f) Ramp time vs surface cook
30
Qv increased with the temperature value of T2 or T3 until the temperature reached around 127°C, and then decreased with the temperatures. The effects of step temperatures on the surface cook value Fs are presented in Figure 4.7c. It can be found that increasing temperatures T1 or T4 caused Fs to increase, especially for T4; and for T2 or T3, the best temperature was about 119°C, which had the minimum surface cook value. Effects of the ramp time on process outputs are shown in Figure 4.7d to f. Basically, the increase of ramp time made the process time and the quality retention increase and the surface cook value decrease. However, the sensitivity of ramp time was dependent on the base temperature.
Coupled ANN models and GA were then used for searching the optimal retort temperature profiles to meet the requirements of optimization objectives and constraint conditions. The typical optimal MRV profiles for the middle size (4.92 * 10–4 m3) achieved by the GA-ANN protocol are illustrated in Figure 4.8.
It could be found that there were different specific MRV values for different optimization objectives and constraint conditions. By comparison of optimization objectives, it was indicated that the minimum PT used as the optimization objec-tive needed much more ramp time than the minimum Fs as the optimization
FIGURE 4.8 Optimal MRV profiles obtained by the GA-ANN method.
V30_minimum PT_Fs <= 66
objective. For example, the ramp time was 70 min for the minimum PT with Qv 62%, while it was only 30 min for the minimum Fs with PT 74 or Qv 62%. From the step temperature point of view, the order of step temperatures was T4 > T3 >
T2 > T1 if the PT or Fs was used as the constraint condition, while T4 was less than T3 if Qv was used as the constraint condition.
4.5.5 ANALYSISOF CRITICAL CONTROL POINTS
IN DEVIANT THERMAL PROCESSES