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Received: 2008-06-24

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Crosschecked: 2009-01-07

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Journal of Zhejiang University SCIENCE B 2009 Vol.10 No.2 P.126-132


On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy

Author(s):  Hui-rong XU, Peng YU, Xia-ping FU, 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:  Visible-NIR spectroscopy, Tomato plant variety, Discrimination, Principal component analysis (PCA), Discriminant analysis (DA), Discriminant partial least squares (DPLS)

Hui-rong XU, Peng YU, Xia-ping FU, Yi-bin YING. On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy[J]. Journal of Zhejiang University Science B, 2009, 10(2): 126-132.

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author="Hui-rong XU, Peng YU, Xia-ping FU, Yi-bin YING",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy
%A Hui-rong XU
%A Peng YU
%A Xia-ping FU
%A Yi-bin YING
%J Journal of Zhejiang University SCIENCE B
%V 10
%N 2
%P 126-132
%@ 1673-1581
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0820200

T1 - On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy
A1 - Hui-rong XU
A1 - Peng YU
A1 - Xia-ping FU
A1 - Yi-bin YING
J0 - Journal of Zhejiang University Science B
VL - 10
IS - 2
SP - 126
EP - 132
%@ 1673-1581
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0820200

The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (Rc)=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction=0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.

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


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