Dolores O’Riordan

Correlation of sensory bitterness in dairy protein hydrolysates: Comparison of prediction models built using sensory, chromatographic and electronic tongue data

Sensory evaluation can be problematic for ingredients with a bitter taste during research and development phase of new food products. In this study, 19 dairy protein hydrolysates (DPH) were analysed by an electronic tongue and their physicochemical characteristics, the data obtained from these methods were correlated with their bitterness intensity as scored by a trained sensory panel and each model was also assessed by its predictive capabilities. The physiochemical characteristics of the DPHs investigated were degree of hydrolysis (DH%), and data relating to peptide size and relative hydrophobicity from size exclusion chromatography (SEC) and reverse phase (RP) HPLC. Partial least square regression (PLS) was used to construct the prediction models. All PLS regressions had good correlations (0.78 to 0.93) with the strongest being the combination of data obtained from SEC and RP HPLC. However, the PLS with the strongest predictive power was based on the e-tongue which had the PLS regression with the lowest root mean predicted residual error sum of squares (PRESS) in the study. The results show that the PLS models constructed with the e-tongue and the combination of SEC and RP-HPLC has potential to be used for prediction of bitterness and thus reducing the reliance on sensory analysis in DPHs for future food research.

Cold-set whey protein microgels as pH modulated immobilisation matrices for charged bioactives

The ability of cold-set whey protein microgels to function as pH-sensitive immobilisation matrices for bioactives was investigated. A pH dependent interaction was confirmed between the microgels and charged bioactives and this binding was impeded by the presence of competing ions in the solution, suggesting an electrostatic interaction. The use of a computer generated prediction model for the pH-dependent association of the microgels and further bioactives (including cationic and anionic peptides) was validated. The prediction model was efficient at determining the pH at which the maximum microgel-bioactive interaction occurred. This study highlights the capabilities of these food-grade whey based microgels as matrices that enable the immobilisation of a variety of bioactives by a charge interaction, and shows the potential for these matrices to function as smart delivery systems, in which uptake and release of bioactives is facilitated by environmental pH change.