Using three-dimensional fluorescence spectroscopy and machine learning for rapid detection of adulteration in camellia oil.

Camellia oil had been widely utilized in the realms of cooking, healthcare, and beauty. Nevertheless, merchants frequently adulterated pure camellia oil with low-priced oils to cut costs. This study was aimed at identifying the authenticity of camellia oil. Through the employment of three-dimensional fluorescence spectroscopy combined with the parallel factor analysis (PARAFAC) method, the characteristics of different vegetable oils were analyzed to establish a foundation for classification modeling. In the identification of pure vegetable oil types, methods such as partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) were adopted. The classification accuracy reached 100 %, demonstrating the effectiveness of feature extraction by PARAFAC. For the identification of camellia oil and its adulterants, traditional machine learning methods and convolutional neural network (CNN) models were introduced. The results indicated that traditional methods had limitations in the classification of single and binary adulterated oils. However, the optimized CaoCNN model achieved an accuracy of 97.78 % in identifying adulterated oil types, showcasing the potential of deep learning in adulterated oil detection. Further, feature visualization analysis verified the ability of CaoCNN to effectively capture and distinguish the characteristics of adulterated oils, providing an effective approach for the identification of camellia oil and its adulterated oils.