Validation of a curd-syneresis sensor over a range of milk composition and process parameters

• Published in: Journal of dairy science 2009-11, Vol.92 (11), p.5386-5395
• Main Authors: Mateo, M.J., O’Callaghan, D.J., Everard, C.D., Castillo, M., Payne, F.A., O’Donnell, C.P.
• Format: Article
• Language: English
• Subjects: Animal productions; Animals; Biological and medical sciences; Cheese; cheesemaking; curd-syneresis sensor; Dairying - instrumentation; Dairying - methods; Fats - analysis; Food Handling - instrumentation; Food Handling - methods; Food industries; Fundamental and applied biological sciences. Psychology; gel strength; least squares; Milk - chemistry; milk analysis; Milk and cheese industries. Ice creams; milk composition; milk curds; milk fat; milk proteins; Milk Proteins - analysis; Models, Theoretical; prediction; protein content; Reproducibility of Results; sensors; syneresis; Terrestrial animal productions; validation; validity; Vertebrates; whey fat; whey yield
• ISSN: 0022-0302; 1525-3198
• DOI: 10.3168/jds.2009-2363

An online visible–near-infrared sensor was used to monitor the course of syneresis during cheesemaking with the purpose of validating syneresis indices obtained using partial least squares, with cross-validation across a range of milk fat levels, gel firmness levels at cutting, curd cutting programs, stirring speeds, milk protein levels, and fat:protein ratio levels. Three series of trials were carried out in an 11-L cheese vat using recombined whole milk. Three factorial experimental designs were used, consisting of 1) 3 curd stirring speeds and 3 cutting programs; 2) 3 milk fat levels and 3 gel firmness levels at cutting; and 3) 2 milk protein levels and 3 fat:protein ratio levels, respectively. Milk was clotted under constant conditions in all experiments and the gel was cut according to the respective experimental design. Prediction models for production of whey and whey fat losses were developed in 2 of the experiments and validated in the other experiment. The best models gave standard error of prediction values of 6.6g/100g for yield of whey and 0.05g/100g for fat in whey, as compared with 4.4 and 0.013g/100g, respectively, for the calibration data sets. Robust models developed for predicting yield of whey and whey fat losses using a validation method have potential application in the cheese industry.