| Literature DB >> 28750167 |
Natthaporn Phonsatta1,2, Pawinee Deetae3, Pairoj Luangpituksa2, Claudia Grajeda-Iglesias4, Maria Cruz Figueroa-Espinoza4, Jérôme Le Comte5, Pierre Villeneuve5, Eric A Decker6,7, Wonnop Visessanguan1, Atikorn Panya1.
Abstract
The addition of antioxidants is one of the strategies to inhibit lipid oxidation, a major cause of lipid deterioration in foods leading to rancidity development and nutritional losses. However, several studies have been reported that conventional antioxidant assays, e.g., TPC, ABTS, FRAP, and ORAC could not predict antioxidant performance in several foods. This study aimed to investigate the performance of two recently developed assays, e.g., the conjugated autoxidizable triene (CAT) and the apolar radical-initiated conjugated autoxidizable triene (ApoCAT) assays to predict the antioxidant effectiveness of gallic acid and its esters in selected food models in comparison with the conventional antioxidant assays. The results indicated that the polarities of the antioxidants have a strong impact on antioxidant activities. In addition, different oxidant locations demonstrated by the CAT and ApoCAT assays influenced the overall antioxidant performances of the antioxidants with different polarities. To validate the predictability of the assays, the antioxidative performance of gallic acid and its alkyl esters was investigated in oil-in-water (O/W) emulsions, bulk soybean oils, and roasted peanuts as the lipid food models. The results showed that only the ApoCAT assay could be able to predict the antioxidative performances in O/W emulsions regardless of the antioxidant polarities. This study demonstrated that the relevance of antioxidant assays to food models was strongly dependent on physical similarities between the tested assays and the food structure matrices.Entities:
Keywords: ApoCAT; CAT; antioxidant; food matrix; gallates; gallic acid; lipid oxidation
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Year: 2017 PMID: 28750167 DOI: 10.1021/acs.jafc.7b02503
Source DB: PubMed Journal: J Agric Food Chem ISSN: 0021-8561 Impact factor: 5.279