Discrimination of Phytosterol and Tocopherol Profiles in Soybean Cultivars Using Independent Component Analysis

Soybean (Glycine max (L.) Merrill) is a major oilseed crop rich in phytosterols and tocopherols, compounds associated with functional and nutritional properties of vegetable oils. This study aimed to apply, for the first time, Independent Component Analysis (ICA) to discriminate the composition of phytosterols (β-sitosterol, campesterol, stigmasterol) and tocopherols (α, β, γ, δ) in 20 soybean genotypes—14 non-transgenic and six transgenic—cultivated in two major producing regions of Paraná state, Brazil (Londrina and Ponta Grossa). Lipophilic compounds were extracted from soybean seeds, quantified via gas chromatography and HPLC, and statistically analyzed using ICA with the JADE algorithm. The extracted independent components successfully differentiated soybean varieties based on phytochemical profiles. Notably, transgenic cultivars from Ponta Grossa exhibited higher levels of total tocopherols, including α- and β-tocopherol, while conventional cultivars from both regions showed elevated phytosterol content, particularly campesterol and stigmasterol. ICA proved to be a powerful unsupervised method for visualizing patterns in complex compositional data. These findings highlight the significant influence of genotype and growing region on the nutraceutical potential of soybean, and support the use of multivariate analysis as a strategic tool for cultivar selection aimed at enhancing functional quality in food applications.