文献类型：期刊文献

英文题名：Rapid classification of Chinese quince (Chaenomeles speciosa Nakai) fruit provenance by near-infrared spectroscopy and multivariate calibration

作者：Shao, Wenhao[1] Li, Yanjie[1,2] Diao, Songfeng[3] Jiang, Jingmin[1] Dong, Ruxiang[1]

第一作者：邵文豪

通信作者：Shao, Wenhao

机构：[1] Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73 Daqiao Road, Fuyang County, Zhejiang, 311400, China; [2] School of Forestry, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand; [3] China Paulownia Research Centre, Non-timber Forest Research and Development Centre of Chinese Academy of Forestry, 3 Weiwu Road, Zhengzhou, Henan, 450003, China

年份：2017

卷号：409

期号：1

起止页码：115-120

外文期刊名：Analytical and Bioanalytical Chemistry

收录：EI(收录号：20164502974624);Scopus(收录号：2-s2.0-84992699531)

语种：英文

外文关键词：Principal component analysis - Discriminant analysis - Infrared devices - Near infrared spectroscopy - Fruits

摘要：The quality of Chinese quince fruit is a significant factor for medicinal materials, influencing the quality of the medicine. However, it is difficult to distinguish different types of Chinese quince fruit. The main objective of this work was to use near-infrared (NIR) spectroscopy, which is a rapid and non-destructive analysis method, to classify the varieties of Chinese quince fruits. Raw spectra in the range of 1000 to 2500?nm were combined with linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machines (SVMs) for classification. The first three principal component analysis (PCA) scores were used as input variables to build LDA, QDA, and SVM discriminant models. The results indicate that all three of these methods are effective for distinguishing the different types of Chinese quince fruit. The classification accuracies for LDA, QDA, and SVM are 94, 96, and 98?%, respectively. QDA led to high-level classification accuracy of Chinese quince fruit. ? 2016, Springer-Verlag Berlin Heidelberg.