可追溯性
特征选择
模式识别(心理学)
极限学习机
近红外光谱
选择(遗传算法)
计算机科学
集合(抽象数据类型)
人工智能
生物系统
数学
统计
物理
人工神经网络
生物
光学
程序设计语言
作者
Ge Jin,Yifan Xu,Chuanjian Cui,Yuanyuan Zhu,Jian-Fa Zong,Huimei Cai,Jingming Ning,Chaoling Wei,Ruyan Hou
摘要
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.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.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.
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