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CLC number: TS207.3; S123

On-line Access: 2012-01-18

Received: 2011-05-31

Revision Accepted: 2011-08-29

Crosschecked: 2011-12-06

Cited: 18

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Journal of Zhejiang University SCIENCE B 2012 Vol.13 No.2 P.145-151


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

Author(s):  Kim-seng Chia, Herlina Abdul Rahim, Ruzairi Abdul Rahim

Affiliation(s):  Department of Control and Instrumentation, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia

Corresponding email(s):   kschia2@live.utm.my, herlina@fke.utm.my

Key Words:  Artificial neural network (ANN), Principal component regression (PCR), Visible and shortwave near infrared (VIS-SWNIR), Spectroscopy, Apple, Soluble solids content (SSC)

Kim-seng Chia, Herlina Abdul Rahim, Ruzairi Abdul Rahim. Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison[J]. Journal of Zhejiang University Science B, 2012, 13(2): 145-151.

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author="Kim-seng Chia, Herlina Abdul Rahim, Ruzairi Abdul Rahim",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison
%A Kim-seng Chia
%A Herlina Abdul Rahim
%A Ruzairi Abdul Rahim
%J Journal of Zhejiang University SCIENCE B
%V 13
%N 2
%P 145-151
%@ 1673-1581
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B11c0150

T1 - Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison
A1 - Kim-seng Chia
A1 - Herlina Abdul Rahim
A1 - Ruzairi Abdul Rahim
J0 - Journal of Zhejiang University Science B
VL - 13
IS - 2
SP - 145
EP - 151
%@ 1673-1581
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B11c0150

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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