Preliminary study on the application of near infrared spectroscopy and pattern recognition methods to classify different types of apple samples

被引：87

作者：

Luo, Weiqi ^{[1
]}

Huan, Shuangyan ^{[1
]}

Fu, Haiyan ^{[1
]}

Wen, Guoli ^{[1
]}

Cheng, Hanwen ^{[1
]}

Zhou, Jingliang ^{[1
]}

Wu, Hailong ^{[1
]}

Shen, Guoli ^{[1
]}

Yu, Ruqin ^{[1
]}

机构：

[1] Hunan Univ, Coll Chem & Chem Engn, State Key Lab Chemo Biosensing & Chemometr, Changsha 410082, Hunan, Peoples R China

来源：

FOOD CHEMISTRY | 2011年 / 128卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Near-infrared fibre-optic technology; Apple; Pattern recognition; MWPLSDA; SQUARES DISCRIMINANT-ANALYSIS; NIR SPECTROSCOPY; VIS/NIR SPECTROSCOPY; REFLECTANCE SPECTROSCOPY; VARIETIES; CLASSIFICATION; IDENTIFICATION; CHEMOMETRICS; REGRESSION; MODEL;

D O I：

10.1016/j.foodchem.2011.03.065

中图分类号：

O69 [应用化学];

学科分类号：

081704 ;

摘要：

In this paper, near infrared (NIR) spectroscopy combined with pattern recognition methods was used in an attempt to classify different types of apple samples. Three pattern recognition methods such as K-nearest neighbour (KNN), partial least-squares discriminant analysis (PLSDA) and moving window partial least-squares discriminant analysis (MWPLSDA) were used to classify apple samples of different geographical origins, grades and varieties. The result indicates that MWPLSDA is superior to these two conventional pattern recognition methods. Because MWPLSDA method can select narrow but informative wavelength intervals to reconstruct an efficacious classification model with high predicting accuracy. In conclusion, MWPLSDA coupled with near-infrared fibre-optic technology is proved to be an effective method for fruit classification. (C) 2011 Elsevier Ltd. All rights reserved.

引用

收藏

页码：555 / 561

页数：7

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