Coupling Mid Infrared Spectroscopy to mathematical and statistical tools for automatic classification, qualification and quantification of Argan oil adulteration

Abstract

Adulteration detection of argan oil is one of the main objective to control its quality and to ensure customer protection. The aim of this study is to develop an automat tool based on the combination of spectroscopic method with mathematical and statistical algorithm to detect the percentages of adulteration of argan oil with commercial oil by. Data analysis is performed by three pattern recognition methods: principal component analysis (PCA), support vector machine learning regression (SVMR), partial least square regression (PLSR). The result obtained can discriminate and quantify the percentage of adulteration with commercial oil. This technologies could be successfully applied to the detection of adulteration of argan oil.