Prediction of Thermal Properties of Sweet Sorghum Bagasse as a Function of Moisture Content Using Artificial Neural Networks and Regression Models

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

HAYKIN, S. 1999. Neural Networks: A Comprehensive Foundation. (2nd ed.). Upper Saddle River, NJ : Prentice Hall. ISBN 0132733501.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

NeuralWorks PredictR. 2013. The Complete Solution for Neural Data Modeling User Guide. Neuralware, Carnegie, PA. USA.Search in Google Scholar

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. 782-790.10.1016/j.fm.2010.05.01421511139Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

SAS. 2010. Users Guide: Statistics. Statistical Analysis System, Inc., Cary, NC : SAS Institute.Search in Google Scholar

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/10.1186/1471-2210-10-610.1186/1471-2210-10-6289243620529254Search in Google Scholar

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.Search in Google Scholar

WHITE, H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford : Blackwell Publishers.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar