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On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy.
J Zhejiang Univ Sci B. 2009 Feb; 10(2):126-32.JZ

Abstract

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 (R(c))=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.

Authors+Show Affiliations

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

19235271

Citation

Xu, Hui-rong, et al. "On-site Variety Discrimination of Tomato Plant Using Visible-near Infrared Reflectance Spectroscopy." Journal of Zhejiang University. Science. B, vol. 10, no. 2, 2009, pp. 126-32.
Xu HR, Yu P, Fu XP, et al. On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy. J Zhejiang Univ Sci B. 2009;10(2):126-32.
Xu, H. R., Yu, P., Fu, X. P., & Ying, Y. B. (2009). On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy. Journal of Zhejiang University. Science. B, 10(2), 126-32. https://doi.org/10.1631/jzus.B0820200
Xu HR, et al. On-site Variety Discrimination of Tomato Plant Using Visible-near Infrared Reflectance Spectroscopy. J Zhejiang Univ Sci B. 2009;10(2):126-32. PubMed PMID: 19235271.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy. AU - Xu,Hui-rong, AU - Yu,Peng, AU - Fu,Xia-ping, AU - Ying,Yi-Bin, PY - 2009/2/24/entrez PY - 2009/2/24/pubmed PY - 2009/3/18/medline SP - 126 EP - 32 JF - Journal of Zhejiang University. Science. B JO - J Zhejiang Univ Sci B VL - 10 IS - 2 N2 - 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 (R(c))=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. SN - 1673-1581 UR - https://www.unboundmedicine.com/medline/citation/19235271/On_site_variety_discrimination_of_tomato_plant_using_visible_near_infrared_reflectance_spectroscopy_ L2 - http://www.jzus.zju.edu.cn/article.php?doi=10.1631/jzus.B0820200 DB - PRIME DP - Unbound Medicine ER -