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

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Abstract

Artificial neural networks (ANN) and traditional regression models were developed for prediction of thermal properties of sweet sorghum bagasse as a function of moisture content and room temperature. Predictions were made for three thermal properties: 1) thermal conductivity, 2) volumetric specific heat, and 3) thermal diffusivity. Each thermal property had five levels of moisture content (8.52%, 12.93%, 18.94%, 24.63%, and 28.62%, w. b.) and room temperature as inputs. Data were sub-partitioned for training, testing, and validation of models. Backpropagation (BP) and Kalman Filter (KF) learning algorithms were employed to develop nonparametric models between input and output data sets. Statistical indices including correlation coefficient (R) between actual and predicted outputs were produced for selecting the suitable models. Prediction plots for thermal properties indicated that the ANN models had better accuracy from unseen patterns as compared to regression models. In general, ANN models were able to strongly generalize and interpolate unseen patterns within the domain of training.

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  • 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.

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