Partial least squares processing of near-infrared spectra for discrimination and quantification of adulterated olive oils

A new processing based on partial least squares (PLS) algorithm for the discrimination and determination of adulterants in pure olive oil using near-infrared (NIR) spectroscopy has been introduced. The 280 adulterations of olive oil with corn oil (n = 70), hazelnut oil (n = 70), soya oil (n = 70), and sunflower oil (n = 70) were prepared, and their NIR spectra in the region 12,000-4550 cm(-1) were collected. The 70 spectra of each adulteration of olive oil were divided into two sets, 50 spectra for a calibration set and 20 spectra for a prediction set. The spectra of a total calibration set (n = 200) were separated into individual adulterant calibration sets (n(i) = 50, i = corn, hazelnut, soya, sunflower) by using discriminant PLS (DPLS) analysis, and PLS calibration models for the quantification of adulterants with corn oil, hazelnut oil, soya oil, or sunflower oil were developed separately. A variety of wavelength ranges and data pretreatments were examined for obtaining optimal results for the discrimination and quantification objects. Four PLS models for differentiating the adulterant types were evaluated by classifying the NIR spectra of a total prediction set (n = 80) into known adulterant types. Then, these known adulterant spectra were analyzed by the PLS calibration models developed for each type to determine the content of an adulterant in pure olive oil. The results of evaluation revealed that the processing reported in this article works excellently for the discrimination and quantification of the adulterations of olive oil.