Neural Network Assisted Experimental Designs for Food Research

H.S. Ramaswamy, C. Chen, S. Sreekanth, S.S. Sablani, S.O. Prasher

Abstract


The ability of artificial neural networks (ANN) in predicting full factorial data from the fractional data corresponding to some of the commonly used experimental designs is explored in this paper. Factorial and fractional factorial designs such as L8, L9, L18, and Box and Behnken schemes were considered both in their original form and with some variations (L8+6, L15 and L9+1). Full factorial (3 factors x 5 levels) and fractional data were generated employing sixteen different mathematical equations (four in each category: linear, with and without interactions, and non-linear, with and without interactions). Different ANN models were trained and the best model was chosen for each equation based on their ability to predict the fractional data. The best experimental design was then chosen based on their ability to simulate the full- factorial data for each equation. In several cases, the mean relative errors with the L18 design (which had more input data than other models) were even higher than with other smaller fractional design. In general, the ANN assisted Lm, Box and Behnken, L15 and L18 designs were found to predict the full factorial data reasonably well with errors less than 5 %. The L8+6 model performed well with several experimental datasets reported in the literature.

 

 


Keywords


Neural network, factorial data, fractional data, food, prediction.

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DOI: http://dx.doi.org/10.24200/jams.vol5iss2pp79-84

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Copyright (c) 2017 H.S. Ramaswamy, C. Chen, S. Sreekanth, S.S. Sablani, S.O. Prasher

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

JAMS 2017-CC BY-ND

This journal and its content is licensed under a Attribution-NoDerivatives 4.0 International.

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