Oil and fat classification by selected bands of near-infrared spectroscopy

One hundred and four edible oil and fat samples from 18 different sources, either vegetable (Brazil nut, coconut, corn, sunflower, walnut, virgin oliv e, peanut, palm, canola, soybean, sunflower) or animal (tallow and hydrogen ated fish), have been analyzed by highperformance gas chromatography (HPGC) and near-infrared spectroscopy (NIRS). Fatty acids were quantified by HPGC . The near-infrared spectral features of the most noteworthy bands were stu died and discussed to design a filter-type NIR instrument. An arborescent s tructure, based on stepwise linear discriminant analysis (SLDA), was built to classify the samples according to their sources. Seven discriminant func tions permitted a successive discrimination of saturated fats, corn, soybea n, sunflower, canola, peanut, high oleic sunflower, and virgin olive oils. The discriminant functions were based on the absorbance values, between thr ee and five, from the 1700-1800 and 2100-2400 nm regions. Chemical explanat ions are given in support of the selected wavelengths. The arborescent stru cture was then checked with a test set, and 90% of the samples were correct ly classified.