Milk Protein Analysis: an Overview of the Methods - Development and Application

Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting

OBJECTIVE

Fat, carbohydrates (mainly lactose) and protein in breast milk all provide indispensable benefits for the growth of newborns. The only source of nutrition in early infancy is breast milk, so the energy of breast milk is also crucial to the growth of infants. Some macronutrients composition in human breast milk varies greatly, which could affect its nutritional fulfillment to preterm infant needs. Therefore, rapid analysis of macronutrients (including lactose, fat and protein) and milk energy in breast milk is of clinical importance. This study compared the macronutrients results of a mid-infrared (MIR) analyzer and an ultrasound-based breast milk analyzer and unified the results by machine learning.

METHODS

This cross-sectional study included breastfeeding mothers aged 22-40 enrolled between November 2019 and February 2021. Breast milk samples (n = 546) were collected from 244 mothers (from Day 1 to Day 1086 postpartum). A MIR milk analyzer (BETTERREN Co., HMIR-05, SH, CHINA) and an ultrasonic milk analyzer (Honɡyanɡ Co,. HMA 3000, Hebei, CHINA) were used to determine the human milk macronutrient composition. A total of 465 samples completed the tests in both analyzers. The results of the ultrasonic method were mathematically converted using machine learning, while the Bland-Altman method was used to determine the limits of agreement (LOA) between the adjusted results of the ultrasonic method and MIR results.

RESULTS

The MIR and ultrasonic milk analyzer results were significantly different. The protein, fat, and energy determined using the MIR method were higher than those determined by the ultrasonic method, while lactose determined by the MIR method were lower (all p < 0.05). The consistency between the measured MIR and the adjusted ultrasound values was evaluated using the Bland-Altman analysis and the scatter diagram was generated to calculate the 95% LOA. After adjustments, 93.96% protein points (436 out of 465), 94.41% fat points (439 out of 465), 95.91% lactose points (446 out of 465) and 94.62% energy points (440 out of 465) were within the LOA range. The 95% LOA of protein, fat, lactose and energy were - 0.6 to 0.6 g/dl, -0.92 to 0.92 g/dl, -0.88 to 0.88 g/dl and - 40.2 to 40.4 kj/dl, respectively and clinically acceptable. The adjusted ultrasonic results were consistent with the MIR results, and LOA results were high (close to 95%).

CONCLUSIONS

While the results of the breast milk rapid analyzers using the two methods varied significantly, they could still be considered comparable after data adjustments using linear regression algorithm in machine learning. Machine learning methods can play a role in data fitting using different analyzers.

On-Site Measurement of Fat and Protein Contents in Milk Using Mobile NMR Technology

Robust and easy-to-use NMR sensor technology is proposed for accurate, on-site determination of fat and protein contents in milk. The two parameters are determined using fast consecutive ¹H and ³⁵Cl low-field NMR experiments on milk samples upon the 1:1 addition of a low-cost contrast solution. Reliable and accurate measurements are obtained without tedious calibrations and the need for extensive database information and may readily be conducted by non-experts in production site environments. This enables on-site application at farms or dairies, or use in laboratories harvesting significant reductions in costs and time per analysis as compared to wet-chemistry analysis. The performance is demonstrated for calibration samples, various supermarket milk products, and raw milk samples, of which some were analyzed directly in the milking room. To illustrate the wide application range, the supermarket milk products included both conventionally/organically produced, lactose-free milk, cow's, sheep's and goat's milk, homogenized and unhomogenized milk, and a broad nutrient range (0.1-9% fat, 1-6% protein). Excellent agreement between NMR measurements and reference values, without corrections or changes in calibration for various products and during extensive periods of experiment conduction (4 months) demonstrates the robustness of the procedure and instrumentation. For the raw milk samples, correlations between NMR and IR, NMR and wet-chemistry, as well as IR and wet-chemistry results, show that NMR, in terms of accuracy, compares favorably with the other methods.

On the Applicability of Electrophoresis for Protein Quantification

Polyacrylamide gel electrophoresis is widely used for studying proteins and protein-containing objects. However, it is employed most frequently as a qualitative method rather than a quantitative one. This paper shows the feasibility of routine digital image acquisition and mathematical processing of electropherograms for protein quantification when using vertical gel electrophoresis and Chrom & Spec software. Both the well-studied model protein molecules (bovine serum albumin) and more complex real-world protein-based products (casein-containing isolate for sports nutrition), which were subjected to mechanical activation in a planetary ball mill to obtain samples characterized by different protein denaturation degrees, were used as study objects. Protein quantification in the mechanically activated samples was carried out. The degree of destruction of individual protein was shown to be higher compared to that of the protein-containing mixture after mechanical treatment for an identical amount of time. The methodological approach used in this study can serve as guidance for other researchers who would like to use electrophoresis for protein quantification both in individual form and in protein mixtures. The findings prove that photographic imaging of gels followed by mathematical data processing can be applied for analyzing the electrophoretic data as an affordable, convenient and quick tool.

Milk as a Complex Multiphase Polydisperse System: Approaches for the Quantitative and Qualitative Analysis

Milk is a product that requires quality control at all stages of production: from the dairy farm, processing at the dairy plant to finished products. Milk is a complex multiphase polydisperse system, whose components not only determine the quality and price of raw milk, but also reflect the physiological state of the herd. Today’s production volumes and rates require simple, fast, cost-effective, and accurate analytical methods, and most manufacturers want to move away from methods that use reagents that increase analysis time and move to rapid analysis methods. The review presents methods for the rapid determination of the main components of milk, examines their advantages and disadvantages. Optical spectroscopy is a fast, non-destructive, precise, and reliable tool for determination of the main constituents and common adulterants in milk. While mid-infrared spectroscopy is a well-established off-line laboratory technique for the routine quality control of milk, near-infrared technologies provide relatively low-cost and robust solutions suitable for on-site and in-line applications on milking farms and dairy production facilities. Other techniques, discussed in this review, including Raman spectroscopy, atomic spectroscopy, molecular fluorescence spectroscopy, are also used for milk analysis but much less extensively. Acoustic methods are also suitable for non-destructive on-line analysis of milk. Acoustic characterization can provide information on fat content, particle size distribution of fat and proteins, changes in the biophysical properties of milk over time, the content of specific proteins and pollutants. The basic principles of ultrasonic techniques, including transmission, pulse-echo, interferometer, and microbalance approaches, are briefly described and milk parameters measured with their help, including frequency ranges and measurement accuracy, are given.