College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, Hubei, China; Key Laboratory for Deep Processing of Major Grain and Oil (Wuhan Polytechnic University), Ministry of Education, Wuhan 430023, Hubei, China; Hubei Key Laboratory for Processing and Transformation of Agricultural Products (Wuhan Polytechnic University), Wuhan 430023, China.
College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, Hubei, China.
Food Chem. 2025 Jan 15;463(Pt 2):141314. doi: 10.1016/j.foodchem.2024.141314. Epub 2024 Sep 16.
Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with R, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.
油类掺假是生产高附加值天然油的全球性挑战。拉曼光谱结合数学建模可用于检测茶油（CAO）的掺假情况。在这项研究中，从统计学的角度比较了传统化学计量学和深度学习方法在识别和定量掺假 CAO 方面的优势，在不同掺假水平的 CAO 识别中未发现显著差异。纯 CAO 和掺假 CAO 的识别率均为 100%，但不同掺假 CAO 之间存在分类错误。深度学习模型在定量预测掺假水平方面优于化学计量学方法，最佳 ConvLSTM 模型的 R、RMSEP 和 RPD 分别达到 0.999、0.9%和 31.5。本研究基于深度学习开发的分类器和模型具有广泛的适用性和可靠性，为 CAO 掺假检测提供了一种快速准确的方法。
