Objective
The heat denaturation of whey proteins impacts the functional properties of milk. After denaturing, β-lactoglobulin (β-LG) associates with κ-casein (κ-CN), a protein located in the surface of the casein micelle. This association significantly impairs milk coagulation by creating a steric obstacle for κ-CN hydrolysis −a critical phenomenon initiating coagulation− and by increasing the gel moisture retention. Correlations of the degree of β-LG denaturation to gelation time, gel firmness, and gel moisture content have been widely documented. The resulting high moisture and soft gels are undesirable for cheese manufacture but advisable in yogurt processing, as it aids in preventing one of its most frequent defects, wheying-off. Early prediction of the potential gelling strength of milk will allow milk batches to be used for their most suitable purposes. The economical impact this would have on the dairy industry, in addition to the non-existence of a simple method of protein denaturation measurement, motivates this research. The goal of this project is the development of an optical sensor technology for inline determination of β-LG denaturation and subsequent association with κ-CN during milk heat treatment. Given the scattering properties of the micelles, light backscatter was chosen for optical measurement. Preliminary experiments indicate that light backscatter response is proportional to heat treatment intensity, which suggests that light scatter could be used to determine the whey protein denaturation degree. The development of the proposed sensor technology will require: a) finding the adequate wavelength/s, b) developing a robust prediction algorithm, and c) obtaining chemical data supporting the observed optical responses. Successful development of this optic sensor technology will aid in the decision-making process of dairy plants for the efficient use of raw milk and the assurance of a high quality product.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensors
- agricultural sciencesanimal and dairy sciencedairy
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
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Call for proposal
FP7-PEOPLE-2010-RG
See other projects for this call
Funding Scheme
MC-IRG - International Re-integration Grants (IRG)Coordinator
08193 Cerdanyola Del Valles
Spain