外文摘要：Blended vegetable oils are highly prized by consumers for their comprehensive nutritional profile. Therefore, there is an urgent need for a rapid and accurate method to identify the true content of blended oils. This study combined Raman spectroscopy with three deep learning models (CNN-LSTM, improved AlexNet, and ResNet) to simultaneously quantify extra virgin olive oil (EVOO), soybean oil, and sunflower oil in olive blended oil. The results demonstrate that all three deep learning models exhibited superior predictive ability compared to traditional chemometric methods. Specifically, the CNN-LSTM model achieved a coefficient of determination (R2p) of over 0.995 for each oil in the quantitative analysis of three-component blended oils, with a mean square error of prediction (RMSEP) of less than 2%. This study presents a novel approach for the simultaneous quantitative analysis of multi-component blended oils, providing a rapid and accurate method for the identification of falsely labeled blended oils.
外文关键词：deep learning;Raman spectroscopy;Quantitative analysis;Blended vegetable oils
作者：Zhang, Xin;Wu, Xijun;Xu, Baoran;Ma, Renqi;Liu, Hailong;Zhang, Yungang;Du, Zherui;Yang, Daolin;Luo, Hao
作者单位：Yanshan Univ
期刊名称：FOOD CHEMISTRY
期刊影响因子：0.0
出版年份：2024
出版刊次：431
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