Geographical origin identification of Hainan camellia oil based on fatty acid composition and near infrared spectroscopy combined with chemometrics

Abstract

Ensuring the authenticity of the original production region is of utmost importance in safeguarding the reputation and ensuring the quality and safety of Hainan camellia oil, which possesses unique quality and commands a higher price than camellia oil from other main producing areas in China. This study explored the potential of fatty acid composition and near infrared (NIR) spectra for geographical traceability of Hainan camellia oil. The relative content of 16 fatty acids in camellia oil samples was determined using gas chromatography (GC), and the spectral information of the samples was obtained using NIR spectroscopy. The data were then analyzed using chemometrics methods, comparing the classification abilities of partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms. The results demonstrated that the SVM model based on the fatty acid composition, the CNN model based on the NIR spectra, and the CNN model based on data fusion achieved prediction accuracies of 97.08%, 97.92%, and 98.75%, respectively, enabling high-precision identification of the geographical origin of Hainan camellia oil. This study reveals that the fatty acid composition and NIR spectra can serve as accurate tools for identifying the geographical origin of camellia oil.