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Journal of Zhejiang University SCIENCE B 2008 Vol.9 No.12 P.982~989

10.1631/jzus.B0820057


Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy


Author(s):  Li-juan XIE, Xing-qian YE, Dong-hong LIU, Yi-bin YING

Affiliation(s):  College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China

Corresponding email(s):   ybying@zju.edu.cn

Key Words:  Near-infrared (NIR) spectroscopy, Principal component-radial basis function neural networks (PC-RBFNN), Bayberry juice, Adulteration, Chemometrics technique


Li-juan XIE, Xing-qian YE, Dong-hong LIU, Yi-bin YING. Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy[J]. Journal of Zhejiang University Science B, 2008, 9(12): 982~989.

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author="Li-juan XIE, Xing-qian YE, Dong-hong LIU, Yi-bin YING",
journal="Journal of Zhejiang University Science B",
volume="9",
number="12",
pages="982~989",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B0820057"
}

%0 Journal Article
%T Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy
%A Li-juan XIE
%A Xing-qian YE
%A Dong-hong LIU
%A Yi-bin YING
%J Journal of Zhejiang University SCIENCE B
%V 9
%N 12
%P 982~989
%@ 1673-1581
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0820057

TY - JOUR
T1 - Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy
A1 - Li-juan XIE
A1 - Xing-qian YE
A1 - Dong-hong LIU
A1 - Yi-bin YING
J0 - Journal of Zhejiang University Science B
VL - 9
IS - 12
SP - 982
EP - 989
%@ 1673-1581
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0820057


Abstract: 
near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.

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

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