Sweet corn cob as a functional ingredient in bakery products.

Gluten free (GF) products are often inferior in quality attributes, nutritional content and consumer acceptability. The use of GF by-products is a novel strategy to improve the structure and nutritional profile of these products. Sweet corn cob (SCC) is a by-product of sweet corn processing containing a considerable amount of fibre and ferulic acid. The effect of baking on ferulic acid content, colour, texture and physical characteristics on muffins incorporated with SCC flour (SCCF) as a value-added food ingredient was investigated using a GF model system. The freeze-dried SCCF, containing ferulic acid (6.02 mg g-1) was used to replace the rice flour at varying levels of 10, 20, and 30%. In general, SCCF increased dietary fibre and free ferulic acid content of muffins. Inclusion of 20% SCCF showed an increase in terms of the height of the muffin and number of air cells in the crumb, along with a decrease in the hardness of muffins. Muffins with SCCF showed higher mean overall liking scores than rice flour muffin.

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.