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Aaron Fernando González-Córdova

Publications and source records attributed to Aaron Fernando González-Córdova.

2 recordsLinked to original sources

[Bioactive peptides derived from milk proteins].

Milk proteins are known for having a wide range of nutritional, functional and biological properties that make them important ingredients in functional or health promoting foods. These properties are partly attributed to bioactive peptides coded in the different milk proteins. Bioactive peptides are inactive within the protein sequence but may be released by the action of native proteolitic enzymes from milk, enzymes from lactic acid bacteria or from exogenous sources or may be produced during gastrointestinal digestion or processing of foods. Peptides derived from caseins and whey proteins were shown to present several bioactive properties such as opioid, antihypertensive, antimicrobial, immunodulatory, mineral carrier and antithrombotic. This overview presents a perspective of the importance of dairy proteins in the production of bioactive peptides and their biological activities, as well as the main analytical tecniques that have been used for the isolation and identification of these peptides.

Milk Proteins↗

Detection and prediction of hydrolytic rancidity in milk by multiple regression analysis of short-chain free Fatty acids determined by solid phase microextraction gas chromatography and quantitative flavor intensity assessment.

The objective was to establish a method for detecting and predicting hydrolytic rancidity in milk by correlating quantitative sensory data with individual short-chain free fatty acids (FFA) (C(4)-C(12)) in milk determined by solid phase microextraction and gas chromatography (SPME-GC). A FFA-based equation for determining rancid flavor intensities in milk was derived by stepwise regression analysis. A highly significant (p < 0.001) correlation coefficient (R (2)) of 0.84 indicated that rancidity scores were dependent on FFA obtained by SPME-GC and that a good proportion of the variation in the rancidity scores was explained by the model. When rancidity scores were predicted for 19 commercial milks, one sample was found to be distinctly rancid by the statistical model and by the trained sensory panel. The rest of the samples were found to be nonrancid by either method. Thus, the predicting power of the model was shown because there was 100% correct flavor classification for the samples tested.

Animals↗