Validation of a curd-syneresis sensor over a range of milk composition and process parameters.
ABSTRACT 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 6.6 g/100 g for yield of whey and 0.05 g/100 g for fat in whey, as compared with 4.4 and 0.013 g/100 g, 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.
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ABSTRACT: The objective of this study was to evaluate a vat wall-mounted image capture system with a range of image processing techniques (threshold, first order and second order grey level statistics and fractal dimension) to monitor curd moisture content during syneresis with a range of temperature treatments. Milk was renneted using three temperature treatments (32 °C throughout, a cooking step from 32 to 38 °C and 38 °C throughout). Prediction models were evaluated in term of fit to reference measurements on samples of curd at 10 min intervals. The best fitting model was based on the threshold technique giving a standard error of prediction (SEP) = 1.06 g/100 g and correlation coefficient (R) = 0.90. These results demonstrated that the threshold image processing technique was the most useful in this study and showed adequate potential to monitor syneresis and predict curd moisture which influences the final texture of cheese.Journal of Food Engineering. 01/2010; 99(3):257-262.