Identification of Adulteration of Sesame Oils Using Least Squares Support Vector Machine Coupled with Particle Swarm Optimization and Partial Least Squares

Through particle swarm optimization (PSO), least squares support vector machine (LSSVM) and partial least squares (PLS) regression, this study was performed to the development of a new method for detection and quantification of adulteration of sesame oil with vegetable oils using gas chromatographic (GC) technique. Based on principal component analysis (PCA), the GC data of total 857 samples including 117 authentic sesame oils and 740 adulterated sesame oils were firstly analyzed for dimension reduction. Using the PCA. filtered GC data, a hierarchical approach including two steps was established for the detection and the quantification of oil samples. At the first step, a model was constructed to discriminate between authentic sesame oils and adulterated sesame oils using least squares support vector machine (LSSVM) algorithm. Then, another LSSVM. based model was developed to identify the type of adulterant in the mixed oil. At last, the PLS models were built to quantification of the adulterated oils. The prediction results showed that the classification model could achieve correct rate 100. 0% and 98. 7%, and the root-mean-square errors of PLS model were 3. 91%, meaning that this approach is a valuable tool to detect and quantify the adulteration of sesame oil compared with other methods such as BP neural network and support vector machine.