References
- AJASA, A. A. - AKINYEMI, L. A. - SHOEWU, O. O. - ADENOWO, A. A. 2014. Exploitation of Artificial Neural Networks Approach to Predict the Thermal Conductivity of Food Products in Nigeria. In American Journal of Engineering Research, vol. 3, no. 3, pp. 22-29.
- AVRAMIDIS, S. - ILIADIS, L. 2005. Predicting Wood Thermal Conductivity Using Artificial Neural Networks. In Wood and Fiber Science, vol. 37, no. 4, pp. 682-690.
- BAIKA, O. - MITTAL, G. S. 2003. Determination and Modeling of Thermal Properties of Tofu. In International Journal of Food Properties, vol. 6, no. 1, pp. 9-24.
- BOŽIKOVA, M. - HLAVAČ, P. 2013. Thermal conductivity and thermal diffusivity of biodiesel and bioethanol samples. In Acta Technologica Agriculturae, vol. 16, no. 4, pp. 90-94.
- BOŽIKOVA, M. - HLAVAČ, P. - VOZAROVA, V. - BELAŇ, L. 2015. Experimental determination of soft wheat flour thermal parameters. In Acta Technologica Agriculturae, vol. 18, no. 1, pp. 6-9.
- FAHLMAN, S. E. - LEBIERE, C. 1990. The Cascade-Correlation Learning Architecture. In TOURETZKY, D. S. Advances in Neural Information Processing Systems 2. Los Altos, CA : Morgan-Kaufmann.
- FARINUA, A. - BAIKA, O. 2007. Thermal Properties of Sweet Potato with Its Moisture Content and Temperature. In International Journal of Food Properties, vol. 10, no. 4, pp. 703-719.
- GOSUKONDA, R. - MAHAPATRA, A. K. - LIU, X. - KANNAN, G. 2015. Application of Artificial Neural Network to Predict Escherichia coli O157:H7 Inactivation on Beef Surfaces. In Food Control, vol. 47, no. 1, pp. 606-614.
- HAJMEER, M. - BASHEER, I. A. 2002. A Probabilistic Neural Network Approach for Modeling and Classification of Bacterial Growth/No-Growth Data. In Journal of Microbiological Methods, vol. 51, no. 2, pp. 217-226.
- HAYKIN, S. 1999. Neural Networks: A Comprehensive Foundation. (2nd ed.). Upper Saddle River, NJ : Prentice Hall. ISBN 0132733501.
- JEYAMKONDAN, S. - JAYAS, D. S. - HOLLEY, R. A. 2001. Microbial Modeling with Artificial Neural Networks. In International Journal of Food Microbiology, vol. 64, no. 3, pp. 343-354.
- KHALIL, S. R. A. - ABDELHAFEZ, A. A. - AMER, E. A. M. 2015. Evaluation of Bioethanol Production from Juice and Bagasse of Some Sweet Sorghum Varieties. In Annals of Agricultural Science, vol. 60, no. 2, pp. 317-324.
- KURTA, H. - KAYFECIB, M. 2009. Prediction of Thermal Conductivity of Ethylene Glycol-Water Solutions by Using Artificial Neural Networks. In Applied Energy, vol. 86, no. 10, pp. 2244-2248.
- NAJJAR, Y. - BASHEER, I. - HAJMEER, M. 1997. Computational Neural Networks for Predictive Microbiology: I Methodology. In International Journal of Food Microbiology, vol. 34, no. 1, pp. 27-49.
- NeuralWorks PredictR. 2013. The Complete Solution for Neural Data Modeling User Guide. Neuralware, Carnegie, PA. USA.
- PANAGOU, E. Z. - MOHAREB, F. R. - ARGYRI, A. A. - BESSANT, C. M. - NYCHAS, G. E. 2011. A Comparison of Artificial Neural Networks and Partial Least Squares Modelling for the Rapid Detection of the Microbial Spoilage of Beef Fillets Based on Fourier Transform Infrared Spectral Fingerprints. In Food Microbiology, vol. 28, no. 4, pp. 782-790.
- PLUTOWSKI, M. - WHITE, H. 1993. Selecting Concise Training Sets from Clean Data. In EEE Transactions on Neural Networks, vol. 4, no. 2, pp. 305-318.
- RUCK, D. W. - ROGERS, S. K. - KABRISKY, M. - MAYBECK, P. S. - MILLS, J. P. 1992. Comparative Analysis of Back-propagation and the Extended Kalman Filter for Training Multilayer Perceptrons. In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 14, no. 6, pp. 686-691.
- SABLANI, S. S. - BAIK, O. D. - MARCOTTE, M. 2002. Neural Networks for Predicting Thermal Conductivity of Bakery Products. In Journal of Food Engineering, vol. 52, no. 3, pp. 299-304.
- SABLANI, S. S. - RAHMAN, M. S. 2003. Using Neural Networks to Predict Thermal Conductivity of Food as a Function of Moisture Content, Temperature, and Apparent Porosity. In Food Research International, vol. 36, no. 6, pp. 617-623.
- SARGENT, D. J. 2001. Comparison of Artificial Neural Networks with Other Statistical Approaches: Results from Medical Data Sets. In Cancer, vol. 91, no. 8 (Supplementary), pp. 1636-42.
- SARLE, W. S. 1994. Neural Networks and Statistical Models. In Proceedings of the Nineteenth Annual SAS Users Group International Conference, Cary, NC, pp. 1538-1550.
- SAS. 2010. Users Guide: Statistics. Statistical Analysis System, Inc., Cary, NC : SAS Institute.
- SPIESS, A. N. - NEUMEYER, N. 2010. An Evaluation of R2 as an Inadequate Measure for Nonlinear Models in Pharmacological and Biochemical Research: a Monte Carlo Approach. In BMC Pharmacology, vol. 10, no. 6. Available at: http://doi.org/10.1186/1471-2210-10-6
- WANG, N. - BRENNAN, J. G. 1993. The Influence of Moisture Content and Temperature on the Specific Heat of Potato Measured by Differential Scanning Calorimetry. In Journal of Food Engineering, vol. 19, no. 3, pp. 303-310.
- WHITE, H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford : Blackwell Publishers.
- YANG, W. - SOKHANSANG, S. - TANG, J. - WINTER, P. 2002. Determination of Thermal Conductivity, Specific Heat and Thermal Diffusivity of Borage Seeds. In Biosystems Engineering, vol. 82, no. 2, pp. 169-176.
- ZHANG, Y. H. P. - DIN, S. Y. - MIELENZ, J. R. - CUI, J. B. - ELANDER, R. T, - LASER, M. 2007. Fractionating Recalcitrant Lignocellulose at Modest Reaction Conditions. In Biotechnology and Bioengineering, vol. 97, no. 1, pp. 214-33.
- ZOU, J. - HAN, Y. - SO, S. S. 2008. Chapter 1. Overview of Artificial Neural Networks. In LIVINGSTONE, D. Artificial Neural Networks: Methods and Applications. New York: Humana Press, pp. 15-23. ISBN (soft cover) 9781617377389.