Prediction of temperature and moisture content of frankfurters during thermal processing using neural network
Introduction
Analysis of the fundamental physical processes and the determination of those physical properties important for the water removal processes, provide valuable information as how to prevent undesirable processes leading to impairment of quality. Carefully controlled thermal processes are necessary to develop initial colour, characteristic skin and internal texture, and eliminate microbial population without emulsion destabilization, excessive moisture loss or texture changes (Mittal & Blaisdell, 1982). Little work has been conducted on the prediction of temperature and moisture content using artificial neural network (ANN) during thermal processing of meat emulsion products.
Mittal et al., 1983, Mittal & Blaisdell, 1982 reported prediction of moisture and temperature distribution during thermal processing of meat emulsion products of slab and cylindrical geometries as a function of composition, relative humidity and process time. Prediction of moisture and temperature profiles using computer simulation, based on mathematical models, is a time consuming work. Developing ANN based on simulation data from validated models will be a useful tool to use results in practice and for process control in smokehouses. An ANN is a data processing system based on the structure of the biological neural system. Prediction with ANN is not like modelling and simulation, but by learning from the data generated experimentally or using validated models. ANN has the ability for relearning according to new data (Hertz, Krough & Palmer, 1991).
Unlike other modelling techniques such as simultaneous heat and mass transfer, kinetic models, and regression analysis, an ANN can accommodate more than two variables to predict two or more parameters. ANN differ from conventional programs in their ability to learn about the system to be modelled without prior knowledge of the process variables relationships (Ramesh, Kumar & Rao, 1996). ANN is now used for food process related problems. Park, Chen, Whittaker and Miller (1993) developed a feed-forward back-propagation neural network model for predicting and classifying beef palatability attributes using ultrasonic spectral features as descriptive parameters. Arteaga and Nakai (1993) developed ANN for predicting foam capacity and stability, and emulsion activity index using physicochemical properties of 11 food-related proteins. Greeraerd, Cenens, Herremans and Van Impe (1997) used a low-complexity, black box ANN model to predict microbial growth considering the effects of temperature, pH and water activity. Ruan, Almaer, Zou and Chen (1997) used two spectrum analysis techniques, fast Fourier transformation and power spectrum density for data preprocessing as part of ANN development for predicting rheological properties of a cookie dough.
Therefore, the objective of this study was to develop an ANN for predicting temperature and moisture content of frankfurters during thermal processing using simulation data from validated mathematical models.
Section snippets
Mathematical modelling and data generation
For data generation, mathematical models developed, simulated and validated against experimental data for frankfurters cooking by Mittal and Blaisdell (1982), were used. Brief descriptions on modelling and simulation are given here.
Mathematical models in non-dimensional form characterizing simultaneous heat and mass transfer are represented as:With the following initial and boundary conditions:
Network training and testing
From the generated data (13500 sets), 1543 and 1416 data sets were randomly selected as testing and production sets, respectively (approximately 10% each of total). The rest, 10541 data sets were used for ANN training. After every data set training, ANN weights were adjusted. Testing data were fed to test trained ANN after training 400 epochs. Testing errors were recorded. In the beginning of training, testing error decreased with the training process. Training was continued until testing error
Conclusion
- 1.
Predicting temperature and moisture content of frankfurters using ANN with simulated data is a simple, convenient and accurate method. A few high relative errors in prediction were caused by boundary patterns in production sets.
- 2.
Selection of hidden nodes was important to ANN predictions.
- 3.
Appropriate combination of learning rate and momentum improved the predictions.
- 4.
The prediction accuracy was improved by shrinking range of input variables.
- 5.
The fat protein ratio (FP) was not an important factor in
Acknowledgements
The research was supported by the Natural Sciences and Engineering Research Council of Canada.
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