Universiti Teknologi Malaysia Institutional Repository

Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison

Abdul Rahim, Herlina and Chia, K. S. and Abdul Rahim, R. (2012) Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison. Journal of Zheijiang University-Science B, 13 (2). pp. 145-151. ISSN 1673-1581

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Official URL: http://dx.doi.org/10.1631/jzus.B11c0150

Abstract

Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400-1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.

Item Type:Article
Uncontrolled Keywords:artificial neural network, soluble solids, principal component regression
Subjects:Q Science
Divisions:Electrical Engineering
ID Code:47272
Deposited By: Narimah Nawil
Deposited On:22 Jun 2015 13:56
Last Modified:31 Mar 2019 16:37

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