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ABSTRACT Milk protein concentrate 85 (MPC85) is a high‐protein dairy ingredient widely used in a variety of foods and prone to performance challenges with certain applications. To address the performance limitations, this study explored pulsed electric field (PEF) processing, a nonthermal method known to alter protein behavior and functional properties. The impact of PEF processing on liquid MPC85 was assessed for solubility, foaming capacity and stability, emulsion stability, gel strength, and water‐holding capacity. Key parameters examined were temperature (25°C–50°C), electric field strength (4–20 kV/cm), and frequency (30–300 Hz). Predicted individual optimized conditions were as follows: For foaming capacity, the minimum (92.35 mL/g; 29.4% lower than the control) was predicted at 25°C, 12.45 kV/cm, and 32.21 Hz and maximum (153.86 mL/g; 17% higher than the control) was predicted at 49.35°C, 19.58 kV/cm, and 119.06 Hz. Maximum emulsion stability (55.12 min; 70.2% higher than the control) was at 50°C, 5.9 kV/cm, and 30 Hz, and the maximum gel strength (4751.33 N; 131% greater than the control) was at 50°C, 4 kV/cm, and 300 Hz. All models showed a good fit to the experimental data. Results demonstrated that foaming stability, water‐holding capacity, and solubility did not show significant improvement under the tested conditions. In conclusion, PEF could be potentially used as a tool to modify the structure of the MPC85 to further promote the application of high‐protein ingredients in the dairy industry. 

Glycomacropeptide (GMP) is isolated from whey and used as an ingredient in phenylketonuria-safe foods because it does not contain phenylalanine. GMP is highly glycosylated and has several sites where N-acetylneuraminic acid (NANA) is bound. In the dairy industry, quantification of NANA from dairy proteins is accomplished by colorimetric, fluorometric, enzymatic, and chromatographic procedures; there is no uniformly accepted industry-wide standard method. In this investigation, NANA quantification methods were evaluated using GMP, and a comparison was made based on the length of time to complete the assay, protein-specificity, linearity, precision, and accuracy. From the methods evaluated, the chromatography protocol was determined to have the greatest benefit for use as a dairy industry standard to measure NANA on GMP. The average mass percent of NANA in 10 statistically independent replicates from a commercial GMP product was measured to be 6.18% ± 0.12%, with a relative standard deviation of 1.94%, which was the lowest of all the methods tested. The accuracy of the chromatographic approach was validated using spike and recovery experiments that provided an average recovery of 90.25%. 

The preprocessing of infrared spectra can significantly improve predictive accuracy for protein, carbohydrate, lipid, or other nutrition components, yet optimal preprocessing selection is typically empirical, tedious, and dataset specific. This study introduces a Bayesian optimization-based framework designed for the automated selection of optimal spectral preprocessing pipelines within a chemometric modeling context. The framework was applied to mid-infrared spectra of milk to predict compositional parameters for fat, protein, lactose, and total solids. A total of 385 averaged spectra corresponding to 198 unique samples was split into a 70/30 ratio (training/test) using a group-aware Kennard-Stone algorithm, resulting in 269 averaged spectra (135 unique samples) for training and 116 spectra (58 unique samples) for testing. Six regression models: Elastic Net, Gradient Boosting Machines (GBM), Partial Least Squares (PLS), RidgeCV Regression, LassoLarsCV, and Support Vector Regression (SVR) were evaluated across three preprocessing conditions: (1) no preprocessing, (2) literature-derived custom preprocessing (e.g., MSC, SNV, and first and second derivatives), and (3) optimized preprocessing via the proposed Bayesian framework. Optimized preprocessing consistently outperformed other methods, with RidgeCV achieving the best performance for all components except lactose, where PLS slightly outperformed it. Improvements in predictive accuracy, particularly in terms of RMSEP were observed across all milk components. The best RMSEP results were achieved for protein (RMSEP = 0.054, R2=0.981) and lactose (RMSEP = 0.026, R2=0.917), followed by fat (RMSEP = 0.139, R2=0.926) and total solids (RMSEP = 0.154, R2=0.960). Literature-based pipelines demonstrated inconsistent effectiveness, highlighting the limitations of transferring preprocessing methods between datasets. The Bayesian optimization approach identified relatively simple yet highly effective preprocessing pipelines, typically involving few steps. By eliminating manual trial and error, this data-driven strategy offers a robust and generalizable solution that streamlines spectral modeling in dairy analysis and can be readily applied to other types of spectroscopic data across various domains. 

Due to its high casein content, micellar casein concentrate (MCC) is a stable protein currently used for various product applications. Our objective was to reduce the viscosity of MCC using a pulsed electric field (PEF) processing which is one of the non-thermal technologies researched in the market. In this study, the effect of processing conditions for PEF treatment, such as temperature (15–45 °C), electric field strength (EFS) (4–20 kV/cm), and frequency on the viscosity (30–300 Hz) of MCC was investigated and optimized using response surface methodology (RSM). The analysis resulted in a quadratic prediction model with R2 = 0.91. The optimized conditions were 35 °C, EFS at 4 kV/cm and frequency at 63 Hz. The optimized consistency coefficient was predicted to be 1440.57 Pa sn which was 46% less than control at 30 °C. Temperature and EFS were found to be the most critical parameters that affect the functionality. Industrial relevance This study provides the optimized process conditions for reducing the viscosity of MCC using PEF, which would benefit the application of MCC in various end-product applications. The results indicate the relevance of using PEF as a treatment through an inline process during the manufacturing of MCC which will in turn allow the dairy industry to fine tune the ingredients and lead to the production of novel ingredients with enhanced functionality. Keywords: Pulsed electric field; Micellar casein concentrate; Viscosity; Response surface methodology; Optimization 

Protein content variation in milk can impact the quality and consistency of dairy products, necessitating access to in-line real time monitoring. Here, we present a chemometric approach for the qualitative and quantitative monitoring of β-lactoglobulin and α-lactalbumin, using mid-infrared spectroscopy (MIR). In this study, we employed Hotelling T2 and Q-residual for outlier detection, automated preprocessing using nippy, conducted wavenumber selection with genetic algorithms, and evaluated four chemometric models, including partial least squares, support vector regression (SVR), ridge, and logistic regression to accurately predict the concentrations of β-lactoglobulin and α-lactalbumin in milk. For the quantitative analysis of these two whey proteins, SVR performed the best to interpret protein concentration from 197 MIR spectra originating from 42 Cornell University samples of preserved pasteurized modified milk. The R2 values obtained for β-lactoglobulin and α-lactalbumin using leave one out cross-validation (LOOCV) are 92.8% and 92.7%, respectively, which is the highest correlation reported to date. Our approach introduced a combination of preprocessing automation, genetic algorithm-based wavenumber selection, and used Optuna to optimize the framework for tuning hyperparameters of the chemometric models, resulting in the best chemometric analysis of MIR data to quantitate β-lactoglobulin and α-lactalbumin to date.