Prediction of protein composition of individual cow milk using mid-infrared spectroscopy

Challenging Sustainable and Innovative Technologies in Cheese Production: A Review

It is well known that cheese yield and quality are affected by animal genetics, milk quality (chemical, physical, and microbiological), production technology, and the type of rennet and dairy cultures used in production. Major differences in the same type of cheese (i.e., hard cheese) are caused by the rennet and dairy cultures, which affect the ripening process. This review aims to explore current technological advancements in animal genetics, methods for the isolation and production of rennet and dairy cultures, along with possible applications of microencapsulation in rennet and dairy culture production, as well as the challenge posed to current dairy technologies by the preservation of biodiversity. Based on the reviewed scientific literature, it can be concluded that innovative approaches and the described techniques can significantly improve cheese production.

Effects of breed, farm intensiveness, and cow productivity level on cheese-making ability predicted using infrared spectral data at the population level

Fourier-transform infrared (FTIR) spectra collected during milk recording schemes at population level can be used for predicting novel traits of interest for farm management, cows' genetic improvement, and milk payment systems. The aims of this study were as follows. (1) To predict cheese yield traits using FTIR spectra from routine milk recordings exploiting previously developed calibration equations. (2) To compare the predicted cheese-making abilities of different dairy and dual-purpose breeds. (3) To analyze the effects of herds' level of intensiveness (HL) and of the cow's level of productivity (CL). (4) To compare the patterns of predicted cheese yields with the patterns of milk composition in different breeds to discern the drivers of cheese-making efficiency. The major sources of variation of FTIR predictions of cheese yield ability (fresh cheese or cheese solids produced per unit milk) of individual milk samples were studied on 115,819 cows of 4 breeds (2 specialized dairy breeds, Holstein and Brown Swiss, and 2 dual-purpose breeds, Simmental and Alpine Grey) from 6,430 herds and exploiting 1,759,706 FTIR test-day spectra collected over 7 yr of milk sampling. Calibration equations used were previously developed on 1,264 individual laboratory model cheese procedures (cross-validation R² 0.85 and 0.95 for fresh and solids cheese yields, respectively). The linear model used for statistical analysis included the effects of parity, lactation stage, year of calving, month of sampling, HL, CL, breed of cow, and the interactions breed × HL and breed × CL. The HL and CL stratifications (5 classes each) were based on average daily secretion of milk net energy per cow. All effects were highly significant (P < 0.001). The major conclusions were as follows. (1) The FTIR-based prediction of cheese yield of milk goes beyond the knowledge of fat and protein content, partially explaining differences in cheese-making ability in different cows, breeds and herds. (2) Differences in cheese yields of different breeds are only partially explained by milk fat and protein composition, and less productive breeds are characterized by a higher milk nutrient content as well as a higher recovery of nutrients in the cheese. (3) High-intensive herds not only produce much more milk, but the milk has a higher nutrient content and a higher cheese yield, whereas within herds, compared with less productive cows, the more productive cows have a much greater milk yield, milk with a greater content of fat but not of protein, and a moderate improvement in cheese yield, differing little from expectations based on milk composition. Finally, (4) the effects of HL and CL on milk quality and cheese-making ability are similar but not identical in different breeds, the less productive ones having some advantage in terms of cheese-making ability. We can obtain FTIR-based prediction of cheese yield from individual milk samples retrospectively at population level, which seems to go beyond the simple knowledge of milk composition, incorporating information on nutrient retention ability in cheese, with possible advantages for management of farms, genetic improvement of dairy cows, and milk payment systems.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

Comparing the effects of whey and casein supplementation on nutritional status and immune parameters in patients with chronic liver disease: a randomised double-blind controlled trial

Protein supplementation may be beneficial for patients with chronic liver disease (CLD). This study compared the effects of whey protein isolate (WP) and casein (CA) supplementation on nutritional status and immune parameters of CLD patients who were randomly assigned to take 20 g of WP or CA twice per d as a supplement for 15 d. Body composition, muscle functionality and plasmatic immunomarkers were assessed before and after supplementation. Patients were also classified according to the model for end-stage liver disease (MELD) into less (MELD < 15) and more (MELD ≥ 15) severe disease groups. Malnutrition, determined by the Subjective Global Assessment at baseline, was observed in 57·4 % and 54·2 % of patients in the WP and CA groups, respectively (P = 0·649). Protein intake was lower at baseline in the WP group than in the CA group (P = 0·035), with no difference after supplementation (P = 0·410). Both the WP and CA MELD < 15 groups increased protein intake after supplementation according to the intragroup analysis. No differences were observed in body composition, muscle functionality, most plasma cytokines (TNF, IL-6, IL-1β and interferon-γ), immunomodulatory proteins (sTNFR1, sTNFR2, brain-derived neurotrophic factor and glial cell line-derived neurotrophic factor) or immunomodulatory hormones (adiponectin, insulin and leptin) after supplementation in the WP groups at the two assessed moments. WP supplementation increased the levels of interferon-γ-induced protein-10/CXCL10 (P = 0·022), eotaxin-1/CCL11 (P = 0·031) and monocyte chemoattractant protein-1/CCL2 (P = 0·018) and decreased IL-5 (P = 0·027), including among those in the MELD ≥ 15 group, for whom IL-10 was also increased (P = 0·008). Thus, WP consumption by patients with CLD impacted the immunomodulatory responses when compared with CA with no impact on nutritional status.

The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle

Over the last 100 years, significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs. Technological progress has enabled a shift from labour intensive, on-farm collection and processing of samples that assess yield and fat levels in milk, to large-scale processing of samples through centralised laboratories, with the scope extended to include quantification of other traits. Fourier-transform mid-infrared (FT-MIR) spectroscopy has had a significant role in the transformation of milk composition phenotyping, with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs. This includes traits related to the nutritional value of milk, the processability of milk into products such as cheese, and traits relevant to animal health and the environment. The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits, and the genetic correlations between the FT-MIR predicted and actual trait values. Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis. The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes. Additionally, there are other molecular phenotypes such as those related to the metabolome, chromatin accessibility, and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest. Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets, and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.

Reference 1D and 2D electrophoresis maps for potential disease related proteins in milk whey from lactating buffaloes and blood serum from buffalo calves (Water buffalo, Bubalus bubalis)

The aim of this study was to identify potential disease related proteins in milk whey of lactating buffaloes and blood serum of buffalo calves, in order to define a reference electrophoresis map for 1-DE and 2-DE. Additionally, changes in some protein patterns from buffalo calves during salmonellosis and lactating buffaloes during mastitis are presented. Milk samples were collected and distributed into groups: Milk samples from healthy buffaloes (SCC < 100.000 cells/ml, negative microbiology and CMT) (G1, n = 5) and buffaloes with subclinical mastitis (SCC > 500.000 cells/ml, positive microbiology and CMT) (G2, n = 5). Blood samples from buffalo calves (n = 6) were collected, and three calves were experimentally infected with Salmonella Dublin and samples analyzed before (M0) and 72 h after inoculation (M1). 1-DE was accomplished by loading 10 μg of TP into SDS-PAGE, stained with Coomassie blue. 2-DE was accomplished by loading 200 μg of TP into 11 cm, pH 3-10 non-linear IPG strips, followed by SDS-PAGE, stained with Coomassie blue. Protein bands/spots were excised, subjected to tryptic in-gel digestion and analyzed by LC/ESI-MS/MS. Protein identity was assigned using NCBI databases. After bands/spots from 1-DE and 2-DE were analyzed, a protein map with 35 and 40 different identified proteins in blood serum and milk whey, respectively, was generated. Significant changes in patterns of haptoglobin were observed in buffalo calves with salmonellosis and in patterns of IgLC, β-lactoglobulin and α-lactalbumin of lactating buffaloes during mastitis. The establishment of a protein map for 1-DE and 2-DE, identifying potential disease related proteins, can help to address alterations during diseases in buffaloes.

Application of infrared portable sensor technology for predicting perceived astringency of acidic whey protein beverages

Formulating whey protein beverages at acidic pH provides better clarity but the beverages typically develop an unpleasant and astringent flavor. Our aim was to evaluate the application of infrared spectroscopy and chemometrics in predicting astringency of acidic whey protein beverages. Whey protein isolate (WPI), whey protein concentrate (WPC), and whey protein hydrolysate (WPH) from different manufacturers were used to formulate beverages at pH ranging from 2.2 to 3.9. Trained panelists using the spectrum method of descriptive analysis tested the beverages providing astringency scores. A portable Fourier transform infrared spectroscopy attenuated total reflectance spectrometer was used for spectra collection that was analyzed by multivariate regression analysis (partial least squares regression) to build calibration models with the sensory astringency scores. Beverage astringency scores fluctuated from 1.9 to 5.2 units and were explained by pH, protein type (WPC, WPI, or WPH), source (manufacturer), and their interactions, revealing the complexity of astringency development in acidic whey protein beverages. The WPC and WPH beverages showed an increase in astringency as the pH of the solution was lowered, but no relationship was found for WPI beverages. The partial least squares regression analysis showed strong relationship between the reference astringency scores and the infrared predicted values (correlation coefficient >0.94), giving standard error of cross-validation ranging from 0.08 to 0.12 units, depending on whey protein type. Major absorption bands explaining astringency scores were associated with carboxylic groups and amide regions of proteins. The portable infrared technique allowed rapid prediction of astringency of acidic whey protein beverages, providing the industry a novel tool for monitoring sensory characteristics of whey-containing beverages.

Short communication: Selecting the most informative mid-infrared spectra wavenumbers to improve the accuracy of prediction models for detailed milk protein content

The objective of this study was to investigate the ability of mid-infrared spectroscopy (MIRS) to predict protein fraction contents of bovine milk samples by applying uninformative variable elimination (UVE) procedure to select the most informative wavenumber variables before partial least squares (PLS) analysis. Reference values (n=114) of protein fractions were measured using reversed-phase HPLC and spectra were acquired through MilkoScan FT6000 (Foss Electric A/S, Hillerød, Denmark). Prediction models were built using the full data set and tested with a leave-one-out cross-validation. Compared with MIRS models developed using standard PLS, the UVE procedure reduced the number of wavenumber variables to be analyzed through PLS regression and improved the accuracy of prediction by 6.0 to 66.7%. Good predictions were obtained for total protein, total casein (CN), and α-CN, which included αS1- and αS2-CN; moderately accurate predictions were observed for κ-CN and total whey protein; and unsatisfactory results were obtained for β-CN, α-lactalbumin, and β-lactoglobulin. Results indicated that UVE combined with PLS is a valid approach to enhance the accuracy of MIRS prediction models for milk protein fractions.

Quantification of whey proteins by reversed phase-HPLC and effectiveness of mid-infrared spectroscopy for their rapid prediction in sweet whey

In the dairy industry, membrane filtration is used to reduce the amount of whey waste and, simultaneously, to recover whey proteins (WP). The composition of WP can strongly affect the filtration treatment of whey, and rapid determination of WP fractions would be of interest for dairy producers to monitor WP recovery. This study aimed to develop mid-infrared spectroscopy (MIRS) prediction models for the rapid quantification of protein in sweet whey, using a validated rapid reversed phase (RP)-HPLC as a reference method. Quantified WP included α-lactalbumin (α-LA), β-lactoglobulin (β-LG) A and B, bovine serum albumin, caseinomacropeptides, and proteose peptone. Validation of RP-HPLC was performed by calculating the relative standard deviation (RSD) in repeatability and reproducibility tests for WP retention time and peak areas. Samples of liquid whey (n=187) were analyzed by RP-HPLC and scanned through MIRS to collect spectral information (900 to 4,000 cm(-1)); statistical analysis was carried out through partial least squares regression and random cross-validation procedure. Retention times in RP-HPLC method were stable (RSD between 0.03 and 0.80%), whereas the RSD of peak area (from 0.25 to 8.48%) was affected by WP relative abundance. Higher coefficients of determination in validation for MIRS model were obtained for protein fractions present in whey in large amounts, such as β-LG (0.58), total identified WP (0.58), and α-LA (0.56). Results of this study suggest that MIRS is an easy method for rapid quantification of detail protein in sweet whey, even if better resolution was achieved with the method based on RP-HPLC. The prediction of WP in sweet whey by MIRS might be used for screening and for classifying sweet whey according to its total and individual WP contents.

Authentication of Whey Protein Powders by Portable Mid-Infrared Spectrometers Combined with Pattern Recognition Analysis

The objective of this study was to develop a simple and rapid method to differentiate whey protein types (WPC, WPI, and WPH) used for beverage manufacturing by combining the spectral signature collected from portable mid-infrared spectrometers and pattern recognition analysis. Whey protein powders from different suppliers are produced using a large number of processing and compositional variables, resulting in variation in composition, concentration, protein structure, and thus functionality. Whey protein powders including whey protein isolates, whey protein concentrates and whey protein hydrolysates were obtained from different suppliers and their spectra collected using portable mid-infrared spectrometers (single and triple reflection) by pressing the powder onto an Attenuated Total Reflectance (ATR) diamond crystal with a pressure clamp. Spectra were analyzed by soft independent modeling of class analogy (SIMCA) generating a classification model showing the ability to differentiate whey protein types by forming tight clusters with interclass distance values of >3, considered to be significantly different from each other. The major bands centered at 1640 and 1580 cm(-1) were responsible for separation and were associated with differences in amide I and amide II vibrations of proteins, respectively. Another important band in whey protein clustering was associated with carboxylate vibrations of acidic amino acids (∼1570 cm(-1)). The use of a portable mid-IR spectrometer combined with pattern recognition analysis showed potential for discriminating whey protein ingredients that can help to streamline the analytical procedure so that it is more applicable for field-based screening of ingredients.

PRACTICAL APPLICATION

A rapid, simple and accurate method was developed to authenticate commercial whey protein products by using portable mid-infrared spectrometers combined with chemometrics, which could help ensure the functionality of whey protein ingredients in food applications.

Effectiveness of mid-infrared spectroscopy to predict fatty acid composition of Brown Swiss bovine milk

Mid-infrared spectroscopy (MIR) is used to predict fatty acid (FA) composition of individual milk samples (n=267) of Brown Swiss cows. FAs were analyzed by gas chromatography as a reference method. Samples were scanned (4000 to 900 cm-1) by MIR, and predictive models were developed using modified partial least squares regressions with full cross-validation. The methods using a first derivative or multiplicative scatter corrected plus first derivative resulted, on average, in the best predictions. Coefficients of correlation between measured and predicted C8:0, C10:0, C12:0, C14:0, anteiso-C17:0, c9-C18:1, and medium- and long-chain FA, and saturated, monounsaturated and unsaturated FA ranged from 0.71 to 0.77, suggesting that prediction models can be implemented in milk recording schemes to routinely collect information on FA composition from the whole Brown Swiss population for breeding purposes.