外文摘要：Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.
外文关键词：machine learning;NIR;FTIR;Black tea;Food fraud;Geographical indication
作者：Wu, Di;Li, Yicong;Logan, Natasha;Quinn, Brian;Hong, Yunhe;Birse, Nicholas;Zhu, Hao;Haughey, Simon;Elliott, Christopher T
作者单位：Wu, Di;Li, Yicong;Logan, Natasha;Quinn, Brian;Hong, Yunhe;Birse, Nicholas;Zhu, Hao;Haughey, Simon;Elliott, Christopher T
期刊名称：FOOD CHEMISTRY
期刊影响因子：0.0
出版年份：2024
出版刊次：438
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