Classification and Detection of Adulteration in Olive Oil Using Improved Gaussian Mixture Model and Regression by Artificial Bee Colony Algorithm

Abstract

Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) can be used to detect adulteration in extra virgin olive oil. The estimate of the GMM parameters is commonly obtained from the expectation- maximization (EM) algorithm. EM algorithm has some limitations such as local optimum problems and sensitivity to the initial values. In this paper, artificial bee colony (ABC) algorithm is used to determine the optimal parameters in GMM and GMR. To improve the optimized performance and reduce computational effort of ABC algorithm, the information sharing mechanism among the global best food sources is introduced in ABC. The improved GMM and GMR by artificial bee colony algorithm (GMMRABC) were used to discriminate and quantify the adulteration of extra virgin olive oil with rapeseed oil using FT-IR spectroscopy. It has been demonstrated that the proposed method is an accurate, rapid, stable strategy for identifying and quantifying the extra virgin olive oil.