文献类型：期刊文献

英文题名：Detection of the 5-hydroxymethylfurfural content in roasted coffee using machine learning based on near-infrared spectroscopy

作者：Xie, Chuanqi[1];Wang, Changyan[2];Zhao, Mengyao[2];Zhou, Weidong[1]

机构：[1]Zhejiang Acad Agr Sci, Inst Anim Husb & Vet Sci, State Key Lab Managing Biot & Chem Threats Qual &, Hangzhou 310021, Peoples R China;[2]East China Univ Sci & Technol, Sch Biotechnol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：422

外文期刊名：FOOD CHEMISTRY

收录：;EI(收录号：20231814028162);WOS:【SCI-EXPANDED(收录号:WOS:000989162800001)】；

基金：Acknowledgements This work was sponsored by the Key R&D Program of Zhejiang (2022C02050) , the National Key Research and Development Project (2019YFD0901800) , the Shanghai Pujiang Program (18J1401900) , and the National Natural Science Foundation for Young Scientists of China (31801668) .

语种：英文

外文关键词：NIR spectroscopy; Roasted coffee; 5-hydroxymethylfurfural; Machine learning; Content prediction

摘要：Since 5-hydroxymethylfurfural (5-HMF) is carcinogenic to humans, its detection in foods is essential. This study performed near-infrared (NIR) spectroscopy (11998-4000 cm-1) to determine the 5-HMF content in roasted coffee. The random forest (RF) was used to extract important wavenumbers, after which three machine learning models (ordinary least square (OLS), support vector machine (SVM), and RF) were established for the prediction. RF obtained the best prediction results (Rc2 = 0.98 and Rp2 = 0.92) compared with OLS and SVM and effectively extracted the important wavenumbers (11667 cm-1, 11666 cm-1, 10905 cm-1, 7096 cm-1, 7095 cm-1, 7094 cm-1, 7093 cm-1, 7092 cm-1, 5054 cm-1, 5026 cm-1, 5025 cm-1, and 5024 cm-1). The results demonstrated that machine learning models based on NIR spectroscopy could provide a non-destructive approach for determining 5-HMF content in roasted coffee.