Rapid identification of the geographic origin of Taiping Houkui green tea using near‐infrared spectroscopy combined with a variable selection method. (16th May 2022)

• Record Type: Journal Article
• Authors: Jin, Ge; Xu, Yifan; Cui, Chuanjian; Zhu, Yuanyuan; Zong, Jianfa; Cai, Huimei; Ning, Jingming; Wei, Chaoling; Hou, Ruyan
• Is Part Of: Journal of the science of food and agriculture. Volume 102:Number 13(2022)
• Page Start: 6123
• Page End: 6130
• Publication Date: 2022-05-16
• Subjects: geographical origin -- near‐infrared spectroscopy -- green tea -- variable selection -- classification; Food -- Periodicals; Agriculture -- Periodicals; 664
• DOI: 10.1002/jsfa.11964
• Languages: English
• ISSNs: 0022-5142

Abstract

Abstract: BACKGROUND: Most studies focus on the geographically larger production areas in tea traceability. However, famous high‐quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier‐transform near‐infrared (FT‐NIR) spectroscopy. RESULTS: First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over‐sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre‐processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre‐processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set. CONCLUSION: The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry.