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Ibrahim, U. M.
Food Process System Engineering Research Unit, Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State State, Nigeria
Ude, C. J.
Food Process System Engineering Research Unit, Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State State, Nigeria
Oke, E. O.
Food Process System Engineering Research Unit, Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State State, Nigeria
ABSTRACT
The
study aimed at modelling of soft sensor models; SVM and GPR, for predicting the
thermal property (specific heat) of dried turmeric rhizome for easy
determination of the direct technical properties of the rhizome. Proximate
composition analysis was conducted for each of the dried samples of turmeric to
determine the nutrition composition. Dried turmeric thermal properties were
derived from empirical equation for specific heat. Two hundred and ninety five
(295) data set was used in developing, training and testing the models using
fivefold cross validation method, five (5) of the remaining data was set aside used for independent validation
of predictive model results. The models developed are Support Vector Machine
(six variants) and Gaussian Process Regression models (four variants). The four
input parameters time, temperature, air velocity and relative humidity were all
simulated using both soft computing models. The result of the models indicated
that Square exponential model of the GPR models have the best model convergence
with the combination of all the input variables.
Keywords: turmeric, drying, thermal property, Support Vector Machine, Gaussian Process Regression models
https://doi.org/10.33922/j.ujet_si1_4
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Special Issue
2019 Special Issue Vol. 5 No. 3
Date Published
Monday, 06 April 2020
The contents of the articles are the sole opinion of the author(s) and not of UJET.
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