Establishment and Evaluation of Support Vector Machine Model for Adulteration Discrimination of Camellia Oil Based on Low-Field Nuclear Magnetic Resonance Relaxation Characteristics

Abstract

The high commercial value of camellia oil entails the development of a rapid and accurate method for identifying camellia oil adulteration. In this study, the feasibility of using low-field nuclear magnetic resonance (LF-NMR) relaxation characteristics and support vector machine (SVM) to detect adulteration in camellia oil was investigated. The LF-NMR relaxation characteristics of raw and oxidized oils of camellia and three other species and their binary blends were compared. Furthermore, principal component analysis was carried out and then an SVM multi-classifier with a binary tree structure was designed. After feature screening by the ReliefF algorithm, an SVM model for identifying adulteration in camellia oil was established and evaluated. The results showed that the LF-NMR relaxation characteristics of oil samples were affected by oil type, oxidation degree and blending ratio. The SVM multi-classification model with 9 features exhibited the best performance, with an accuracy of 90.77%. Additionally, the average recall, precision and F1 score for camellia oil, blending type and ratio were 90.87%, 90.83% and 0.90, respectively. This study indicated that the SVM model based on LF-NMR relaxation characteristics could be employed for identifying adulteration in camellia oil.

Keywords

• camellia oil; adulteration detection; low-field nuclear magnetic resonance; support vector machine; principal component analysis; relieff algorithm