Oil and Fat Classification by Selected Bands of Near-Infrared Spectroscopy

Abstract

One hundred and four edible oil and fat samples from 18 different sources, either vegetable (Brazil nut, coconut, corn, sunflower, walnut, virgin olive, peanut, palm, canola, soybean, sunflower) or animal (tallow and hydrogenated fish), have been analyzed by high-performance 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 studied and discussed to design a filter-type NIR instrument. An arborescent structure, based on stepwise linear discriminant analysis (SLDA), was built to classify the samples according to their sources. Seven discriminant functions permitted a successive discrimination of saturated fats, corn, soybean, sunflower, canola, peanut, high oleic sunflower, and virgin olive oils. The discriminant functions were based on the absorbance values, between three and five, from the 1700–1800 and 2100–2400 nm regions. Chemical explanations are given in support of the selected wavelengths. The arborescent structure was then checked with a test set, and 90% of the samples were correctly classified.