外文摘要：Coffee is a target for geographical origin fraud. More rapid, cost-effective, and sustainable traceability solutions are needed. The potential of hyperspectral imaging-near-infrared (HSI-NIR) and advanced machine learning models for rapid and non-destructive origin classification of coffee was explored for the first time (i) to understand the sensitivity of HSI-NIR for classification across various origin scales (continental, country, regional), and (ii) to identify discriminant wavelength regions. HSI-NIR analysis was conducted on green coffee beans from three continents, eight countries, and 22 regions. The classification performance of four different machine learning models (PLS-DA, SVM, RBF-SVM, Random Forest) was compared. Linear SVM provided near-perfect classification performance at the continental, country, and regional levels, and enabled a feature selection opportunity. This study demonstrates the feasibility of using HSI-NIR with machine learning for rapid and nondestructive screening of coffee origin, eliminating the need for sample processing.
外文关键词：hyperspectral;machine learning;Classification;Non-destructive;Green coffee bean;Origin traceability
作者：Dixit, Yash;Sim, Joy;Mcgoverin, Cushla;Oey, Indrawati;Frew, Russell;Reis, Marlon M;Kebede, Biniam
作者单位：Univ Auckland;Univ Otago;Grasslands Res Ctr;Riddet Inst;Oritain Global Ltd;Dodd Walls Ctr Photon & Quantum Technol
期刊名称：FOOD CONTROL
期刊影响因子：0.0
出版年份：2024
出版刊次：156
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台