Bioactive profiles of edible vegetable oils determined using 10D hyphenated comprehensive high-performance thin-layer chromatography (HPTLC×HPTLC) with on-surface metabolism (nanoGIT) and planar bioassays

Abstract

IntroductionVegetable oils rich in unsaturated fatty acids are assumed to be safe and even healthy for consumers though lipid compositions of foods vary naturally and are complex considering the wealth of minor compounds down to the trace level.MethodsThe developed comprehensive high-performance thin-layer chromatography (HPTLC×HPTLC) method including the on-surface metabolization (nanoGIT) and bioassay detection combined all steps on the same planar surface. The pancreatic lipolysis (intestinal phase) experiment and the subsequent analysis of the fatty acid composition including its effect-directed detection using a planar bioassay was performed without elaborate sample preparation or fractionation to ensure sample integrity. Thus, no sample part was lost, and the whole sample was studied on a single surface regarding all aspects. This made the methodology as well as technology miniaturized, lean, all-in-one, and very sustainable.Results and discussionTo prioritize important active compounds including their metabolism products in the complex oil samples, the nanoGIT method was used to examine the pancreatic lipolysis of nine different vegetable oils commonly used in the kitchen and food industry, e.g., canola oil, flaxseed oil, hemp oil, walnut oil, soybean oil, sunflower oil, olive oil, coconut oil, and palm oil. The digested oils revealed antibacterial and genotoxic effects, which were assigned to fatty acids and oxidized species via high-resolution tandem mass spectrometry (HRMS/MS). This finding reinforces the importance of adding powerful techniques to current analytical tools. The 10D hyphenated nanoGIT-HPTLC×HPTLC-Vis/FLD-bioassay-heart cut-RP-HPLC-DAD-HESI-HRMS/MS has the potential to detect any potential hazard due to digestion/metabolism, improving food safety and understanding on the impact of complex samples.