Hu Yating, Wei Chaojie, Wang Xiaorong, Wang Wei, Jiao Yanna
College of Engineering, China Agricultural University, Beijing 100083, China.
College of Engineering, China Agricultural University, Beijing 100083, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Mar 15;329:125524. doi: 10.1016/j.saa.2024.125524. Epub 2024 Dec 3.
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.
山茶油已广泛应用于烹饪、保健和美容领域。然而，商家为了降低成本，常将纯山茶油与低价油掺假。本研究旨在鉴定山茶油的真伪。通过采用三维荧光光谱结合平行因子分析（PARAFAC）方法，分析了不同植物油的特征，为分类建模奠定基础。在纯植物油类型的鉴定中，采用了偏最小二乘判别分析（PLS-DA）、k近邻（KNN）、支持向量机（SVM）和随机森林（RF）等方法。分类准确率达到100%，证明了PARAFAC特征提取的有效性。对于山茶油及其掺假物的鉴定，引入了传统机器学习方法和卷积神经网络（CNN）模型。结果表明，传统方法在单一和二元掺假油的分类中存在局限性。然而，优化后的CaoCNN模型在掺假油类型识别中的准确率达到了97.78%，展示了深度学习在掺假油检测中的潜力。此外，特征可视化分析验证了CaoCNN有效捕捉和区分掺假油特征的能力，为山茶油及其掺假油的鉴定提供了有效途径。
