文献类型：期刊文献

英文题名：Application of Near-Infrared Spectroscopy to Rapidly Classify the Chinese Quince Fruits from Different Habitats

作者：Diao, Songfeng[1,2] Tang, Xiaoqian[3] Huang, Lin[1] Li, Yanjie[2] Fan, Xiongfei[1] Shao, Wenhao[2]

通信作者：Shao, WH[1]

机构：[1]Res Inst Nontimber Forestry, Chinese Acad Forestry, Key Lab Nontimber Forest Germplasm Enhancement & U, Zhengzhou 450003, Henan, Peoples R China;[2]Chinese Acad Forestry, Inst Subtrop Forestry Res, Key Lab Subtrop Tree Breeding & Cultivat State For, Key Lab Tree Breeding Zhejiang Prov, Hangzhou 311400, Zhejiang, Peoples R China;[3]Chinese Acad Forestry, Sci & Technol Management Dept, Beijing 100091, Peoples R China

年份：2024

卷号：2024

外文期刊名：JOURNAL OF FOOD QUALITY

收录：;EI(收录号：20240815607792);Scopus(收录号：2-s2.0-85185400678);WOS:【SCI-EXPANDED(收录号:WOS:001163822300001)】；

基金：This work was supported by the National Science and Technology Basic Resources Survey Program of China (2019FY100803-02), the Central Finance Forest and Grass Science and Technology Demonstration Project (GTH[2024]2), and Fundamental Research Funds of CAF (CAFYBB2018SY016).

语种：英文

外文关键词：Discriminant analysis - Ecosystems - Fruits - Infrared devices - Least squares approximations

摘要：The ecological habitats of Chinese quince (Chaenomeles speciosa Nakai) fruits affect their phenotype. Currently, limited or no rapid method exists for classifying Chinese quince fruit from different ecosystems. This study developed a partial least squares discriminant analysis (PLS-DA) classification model to effectively and nondestructively classify 663 Chinese quince fruit samples from six environments in 2020. PLS-DA models and other variable selection approaches were used in this study. The near-infrared spectroscopy (NIRs) absorption spectra of raw Chinese quince fruit samples from six habitats showed a similar shape. The spectra of each environment showed little variance. The raw fruit spectra varied significantly among habitat categories after the first derivative preprocessing phase. The uninformative variable elimination (UVE) variable selection approach had greater calibration and validation set specificity of 0.93 and 0.98. This study found the best classification specificity using the UVE variable selection approach compared to other methods including the PLS-DA model without variable selection. The UVE approach improved Yunnan habitat categorization specificity from 86% to 88% when integrated with PLS-DA. Additionally, the validation set for quinces originating from Anhui, Chongqing, Hubei, Shandong, and Zhejiang achieved an ideal classification score of 100%. The findings of the study indicated that PLS-DA can serve as an alternative approach for classifying the habitats of Chinese quince fruits. When used in conjunction with other methods, this technique can assist researchers, scientists, and industry professionals in identifying the main factors responsible for significant variations in the habitats, composition, and quality of Chinese quince fruits.