Detection of flaxseed oil multiple adulteration by near-infrared spectroscopy and nonlinear one class partial least squares discriminant analysis

Abstract

Near-infrared (NIR) spectroscopy is widely used to detect fraudulent food products. However, NIR spectroscopy has difficulty in detecting multiple adulterations of edible oils, which is a commonly used countermeasure against adulteration detection methods. In this study, a targeted detection approach was proposed by using NIR for multiple adulteration of flaxseed oil. A variable selection method was designed to significantly reduce the number of variables and improve the accuracy of adulterant detection. After the important variables were selected by orthogonal partial least squares discriminant analysis (OPLS-DA), a one-class partial least squares (OCPLS) was used to build a detection model from a set of Gaussian radial basis functions (GRBF) that could identify flaxseed oil adulterated at a 5% level with blends of cheaper oils. This model was validated by two independent test sets. The results indicated that this model could effectively detect single, dual, or multiple adulterants with a high accuracy of 95.8% (68 out of 71). Compared with previous studies, the model built by OPLS-OCPLS provided a rapid and effective targeted detection approach for multiple adulteration of flaxseed oil.