化学计量学
偏最小二乘回归
数学
二阶导数
衍生工具(金融)
决定系数
化学
近红外光谱
统计
标准误差
预处理器
生物系统
计算机科学
人工智能
生物
色谱法
金融经济学
数学分析
经济
神经科学
作者
Quan Nguyen Minh,Đạt Quốc Lại,Nguy Minh HOANG,Tran Kieu Minh TU,Ngoc Gia Lam,Uyen Le,M Hang,Hoàng Dũng Nguyễn,Tran Diem Ai Chau,Ngoc Thuc Trinh Doan
摘要
Summary To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near‐infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS‐DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross‐validation of 0.0261. PLS‐DA model of mean centre and first‐order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post‐harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.
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