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

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...

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Main Authors: Abdul Rahim, Herlina, Chia, K. S., Abdul Rahim, R.
Format: Article
Published: 2012
Subjects:
Q Science
Online Access:http://eprints.utm.my/id/eprint/47272/
http://dx.doi.org/10.1631/jzus.B11c0150
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spelling my.utm.472722019-03-31T08:37:48Z http://eprints.utm.my/id/eprint/47272/ Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison Abdul Rahim, Herlina Chia, K. S. Abdul Rahim, R. Q Science 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. 2012 Article PeerReviewed 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 http://dx.doi.org/10.1631/jzus.B11c0150 DOI:10.1631/jzus.B11c0150
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science
spellingShingle Q Science
Abdul Rahim, Herlina
Chia, K. S.
Abdul Rahim, R.
Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison
description 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.
format Article
author Abdul Rahim, Herlina
Chia, K. S.
Abdul Rahim, R.
author_facet Abdul Rahim, Herlina
Chia, K. S.
Abdul Rahim, R.
author_sort Abdul Rahim, Herlina
title Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison
title_short Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison
title_full Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison
title_fullStr Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison
title_full_unstemmed Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison
title_sort neural network and principal component regression in non-destructive soluble solids content assessment: a comparison
publishDate 2012
url http://eprints.utm.my/id/eprint/47272/
http://dx.doi.org/10.1631/jzus.B11c0150
_version_ 1643652274697797632
score 13.211869

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