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Journal of Zhejiang University SCIENCE B 2008 Vol.9 No.7 P.552-557


Application of NIR spectroscopy for firmness evaluation of peaches

Author(s):  Xia-ping FU, Yi-bin YING, Ying ZHOU, Li-juan XIE, Hui-rong XU

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), Anisotropy, Peach, Firmness, Partial least square regression (PLSR)

Xia-ping FU, Yi-bin YING, Ying ZHOU, Li-juan XIE, Hui-rong XU. Application of NIR spectroscopy for firmness evaluation of peaches[J]. Journal of Zhejiang University Science B, 2008, 9(7): 552-557.

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author="Xia-ping FU, Yi-bin YING, Ying ZHOU, Li-juan XIE, Hui-rong XU",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Application of NIR spectroscopy for firmness evaluation of peaches
%A Xia-ping FU
%A Yi-bin YING
%A Ying ZHOU
%A Li-juan XIE
%A Hui-rong XU
%J Journal of Zhejiang University SCIENCE B
%V 9
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%P 552-557
%@ 1673-1581
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0720018

T1 - Application of NIR spectroscopy for firmness evaluation of peaches
A1 - Xia-ping FU
A1 - Yi-bin YING
A1 - Ying ZHOU
A1 - Li-juan XIE
A1 - Hui-rong XU
J0 - Journal of Zhejiang University Science B
VL - 9
IS - 7
SP - 552
EP - 557
%@ 1673-1581
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0720018

The use of near infrared (NIR) spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800~2500 nm by InGaAs detector, was used to evaluate the firmness of peaches. anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts (including three latitudes and three longitudes) were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes (i, ii, iii) and latitudes (A, B, C). For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. partial least square regression (PLSR) models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction (MSC) pretreatment, with correlation coefficient of cross-validation rcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of calibration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.

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


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