Utilization of gallic acid-crosslinked soy proteins as wall material for chia oil microencapsulation.

BACKGROUND: Chia oil represents the vegetable source with the highest content of omega-3 fatty acids. However, the incorporation of polyunsaturated fatty acids into food is limited due to their susceptibility toward oxidation. This investigation aimed to study the microencapsulation of chia oil (CO), using gallic acid (GA) crosslinked-soy protein isolate (SPI) as a wall material and its effect on its oxidative stability. RESULTS: Microcapsules presented a moisture content, water activity, and encapsulation efficiency of around 2.95-4.51% (wet basis); 0.17 and 59.76-71.65%, respectively. Rancimat tests showed that with higher GA content, the induction period increased up to 27.9 h. The storage test demonstrated that the microencapsulated oil with crosslinked wall material has lower values of hydroperoxides and higher induction times concerning the non-crosslinked oil. Finally, the fatty acid profile at this storage time indicated that microcapsules with GA did not have significant changes. In vitro digestion exhibited a reduction in the percentage of bioavailable oil for crosslinked microcapsules, but with no variations in its chemical quality, and an increase in the total polyphenols amount and antioxidant activity. CONCLUSION: The results obtained demonstrated that the microencapsulation of CO using SPI crosslinked with GA as wall material exerted a very important protective effect since a synergistic effect could be described between the microencapsulation effect and the antioxidant power of GA. © 2023 Society of Chemical Industry.

Discovery of food identity markers by metabolomics and machine learning technology.

Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chemicals are unique identifiers of food components. Non-targeted metabolomics is ideally suited to screen food markers when coupled to efficient data analysis. This study explored feasibility of random forest (RF) machine learning, specifically its inherent feature extraction for non-targeted metabolic marker discovery. The distinction of chia, linseed, and sesame that have gained attention as "superfoods" served as test case. Chemical fractions of non-processed seeds and of wheat cookies with seed ingredients were profiled. RF technology classified original seeds unambiguously but appeared overdesigned for material with unique secondary metabolites, like sesamol or rosmarinic acid in the Lamiaceae, chia. Most unique metabolites were diluted or lost during cookie production but RF technology classified the presence of the seed ingredients in cookies with 6.7% overall error and revealed food processing markers, like 4-hydroxybenzaldehyde for chia and succinic acid monomethylester for linseed additions. RF based feature extraction was adequate for difficult classifications but marker selection should not be without human supervision. Combination with alternative data analysis technologies is advised and further testing of a wide range of seeds and food processing methods.