Near-infrared-based Identification of Walnut Oil Authenticity

Abstract

Near-infrared spectroscopy (NIR) was employed to determine the quality of walnut oil. Multivariate scattering correction (MSC) and combination of standard normal variate and de-trend (SNV-DT) were separately used for preprocessing. Successive projection algorithm (SPA) and combination of competitive adaptive reweighted sampling algorithm and partial least squares method (CARS-PLS) were separately used for extraction of characteristic wavelengths. Particle swarm optimization (PSO) and genetic algorithm (GA) methods were separately used for parameter optimization. Eventually, quantitative prediction models for impurities contents in walnut oil were established by support vector machine (SVM). After analysis of the prediction results derived from these models, an optimal candidate was obtained.