Control and Monitoring of Milk Renneting Using FT-NIR Spectroscopy as a Process Analytical Technology Tool

Abstract

Failures in milk coagulation during cheese manufacturing can lead to decreased yield, anomalous behaviour of cheese during storage, significant impact on cheese quality and process wastes. This study proposes a Process Analytical Technology approach based on FT-NIR spectroscopy for milk renneting control during cheese manufacturing. Multivariate Curve Resolution optimized by Alternating Least Squares (MCR-ALS) was used for data analysis and development of Multivariate Statistical Process Control (MSPC) charts. Fifteen renneting batches were set up varying temperature (30, 35, 40 °C), milk pH (6.3, 6.5, 6.7), and fat content (0.1, 2.55, 5 g/100 mL). Three failure batches were also considered. The MCR-ALS models well described the coagulation processes (explained variance ≥99.93%; lack of fit <0.63%; standard deviation of the residuals <0.0067). The three identified MCR-ALS profiles described the main renneting phases. Different shapes and timing of concentration profiles were related to changes in temperature, milk pH, and fat content. The innovative implementation of MSPC charts based on T2 and Q statistics allowed the detection of coagulation failures from the initial phases of the process.

Calcium: A comprehensive review on quantification, interaction with milk proteins and implications for processing of dairy products

“…At the same time, the serum phase is being physically separated from the curd material (which is often used as raw material for the production of whey-based ingredients), although caseinomacropeptide is released after renneting in the serum phase, increasing the protein profile complexity of the serum phase (Svanborg et al, 2016). However, renneting of milk does not always result in a consistent separation of the two milk phases in terms of compositions and physicochemical properties due to intrinsic factors such as temperature, rennet addition content, and innate [Ca 2+ ] (De La Fuente et al, 1996;Grassi et al, 2019). The temperature of renneting considerably influences the rate of coagulation and rheological properties of the gels formed.…”

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

“…Various groups have developed NIR spectroscopy methods for quality control of milk samples [192,[199][200][201][202]. Milk, the principal consumed dairy product, could be adulterated in several ways by addition of different substances such as urea, hydrogen peroxide, vegetable proteins, water, and others [203,204].…”

“…From this study, i-PCA model allowed Strani et al to predict how the renneting procedure will be affected with different operating conditions. A similar work published by the same group, analyzed NIR spectra and rheological data of fifteen samples at various renneting conditions with multivariate curve resolution-alternating least square (MCR-ALS) to build multivariate statistical process control (MSPC) charts [201]. The analysis performed with MCR-ALS demonstrated that three components represented the major phases of the renneting procedure under different cheese fabrication conditions.…”

Evaluation of temperature compensation methods for a near‐infrared calibration to predict the viscosity of micellar liquids

“…Near‐infrared (NIR) spectroscopy has proven to be a successful analytical tool in measuring chemical and physical properties in‐line due to its non‐destructive nature, speed of analysis and ease of implementation in manufacturing environments 1–9 . Though NIR spectroscopy has low selectivity, it can be combined with multivariate modelling techniques such as principal component analysis (PCA) or partial least squares (PLS) to extract the relevant information and develop predictive models.…”