Quality Assessment of Edible Plant-Based Fixed Oils Using Different Analytical Techniques and Machine Learning Approaches

Abstract

Plant-based carrier oils are constantly gaining popularity for their beneficial health effects on human organisms, thus shifting consumers’ preferences to alternative options in the oil market. The purpose of the study was to evaluate the quality characteristics of twenty-six edible plant-based fixed oils via chromatographic, chromatic, and spectroscopic techniques, suggesting their potential use as complementary edible oil sources. Palmitic, stearic, oleic, linoleic, and a-linolenic acids were found in considerable proportions, whereas the majority of oils possessed unsaturated (UFA)/saturated (SFA) fatty acid ratio greater than 1.6, suggesting their function in lowering blood pressure and preventing cardiovascular disease. Linseed, chia seed, macadamia, and canola oils provide a balanced intake of n-6 and n-3 polyunsaturated fatty acids (PUFA) within the range of 1:1 to 5:1. Oxidative stability was inversely related to oils’ PUFA content, with linseed, chia seed, pine cone, and walnut oils being the least stable oils against oxidation. Chlorophyll content in all oils was below the limit (50 mg/kg), preventing oxidation in the presence of light, whereas the highest values of b-carotene were noticed in soybean, linseed, and canola oils (61.18, 60.42, and 60.12 ppm, respectively). The application of machine learning algorithms for analyzing ATR-FTIR band intensities and FA proportions via discriminant analysis succeeded in discriminating pulp from seed oils, with a classification accuracy of 96.0% and 88.0%, respectively.