Performance Evaluation of Fiber Near-Infrared (NIR) Optic Probes for Quality Control of Curd Hardness in Cheese Produced by Spray-Dried Milk

Abstract

This study aims to provide the dairy industry with a direct control model focused on milk coagulation by using multifiber probes to determine parameters in the curding process, such as cutting time, at a lower cost. The main objective of the research is to confirm that a multifiber NIR light scattering probe can be used to predict the elastic modulus of curd during milk coagulation in cheese production. Two randomized complete block designs were used with a 3 × 3 factorial arrangement of three protein levels (3%, 3.5% and 4%) and three wavelengths (870 nm, 880 nm and 890 nm). Using a multifiber probe at a wavelength of 880 nm allowed obtaining a better optical response of the sensor during enzymatic milk coagulation than the 870 nm. It showed greater sensitivity to variations in the protein content of the milk and lower variation in the response. The multifiber probe at a wavelength of 880 nm generated a NIR light backscatter profile like those obtained with other systems. The results showed that the prediction model parameters had a variation as a function of the protein content, which opens the possibility of improving the prediction model’s performance substantially. Furthermore, the initial voltage obtained with the probe responded linearly to the different protein levels in milk. This fact would make it possible, at least theoretically, to estimate protein concentration with the same inline probe for G’ determination, facilitating the incorporation of a corrective protein factor in the prediction models using a single instrument.

Integration of Sensor Fusion to Enhance Quality Assessment of White Brine Cheeses

“…Complementing the results of Meza et al [22], the results obtained in the preliminary prediction of the main characteristics of cheese by chemometric techniques presented significantly higher values of the coefficient of determination and lower errors when using the PLSR method. This can be explained by the fact that PLSR is an approach that effectively deals with the multicollinearity found in the data characterizing cheese quality indicators.…”