Emerging Parameters Justifying a Revised Quality Concept for Cow Milk

Milk has become a staple food product globally. Traditionally, milk quality assessment has been primarily focused on hygiene and composition to ensure its safety for consumption and processing. However, in recent years, the concept of milk quality has expanded to encompass a broader range of factors. Consumers now also consider animal welfare, environmental impact, and the presence of additional beneficial components in milk when assessing its quality. This shifting consumer demand has led to increased attention on the overall production and sourcing practices of milk. Reflecting on this trend, this review critically explores such novel quality parameters, offering insights into how such practices meet the modern consumer's holistic expectations. The multifaceted aspects of milk quality are examined, revealing the intertwined relationship between milk safety, compositional integrity, and the additional health benefits provided by milk's bioactive properties. By embracing sustainable farming practices, dairy farmers and processors are encouraged not only to fulfill but to anticipate consumer standards for premium milk quality. This comprehensive approach to milk quality underscores the necessity of adapting dairy production to address the evolving nutritional landscape and consumption patterns.

The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese

The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.

Influence of salt concentration on microbial growth in Kashkaval cheese

The aim of the present study was to evaluate the influence of NaCl concentration on the microflora in Kashkaval cheese produced from cow’s milk. Three cheese samples were obtained - with low (0.7%), medium (1.5%), and standard (3.1%) content of NaCl. Microbiological analyses were performed on the 1st, 15th, 30th, and 45th d of cheese ripening. It was established that the NaCl content has a significant (p < 0.05) influence on the growth and activity of the microflora in studied samples. It has been observed that the total Lactic acid bacteria (LAB) increased up to 30 d during ripening, after which their concentration decreased. A higher LABs count of samples with 0.7% NaCl and 1.5% NaCl in comparison with those containing 3.1% NaCl was found. At the same time, the variations in the salt content do not have a significant (p > 0.05) impact on the growth of Psychrotrophic bacteria, while in samples with a low salt content, the growth of Yeast and Molds was more intense. The data obtained in the present study showed that the concentration of NaCl is important for the regulation of activity of microbiological processes during the ripening of the Kashkaval cheese samples.

Prediction of fresh and ripened cheese yield using detailed milk composition and udder health indicators from individual Brown Swiss cows

The composition of raw milk is of major importance for dairy products, especially fat, protein, and casein (CN) contents, which are used worldwide in breeding programs for dairy species because of their role in human nutrition and in determining cheese yield (%CY). The aim of the study was to develop formulas based on detailed milk composition to disentangle the role of each milk component on %CY traits. To this end, 1,271 individual milk samples (1.5 L/cow) from Brown Swiss cows were processed according to a laboratory model cheese-making procedure. Fresh %CY (%CY_CURD), total solids and water retained in the fresh cheese (%CY_SOLIDS and %CY_WATER), and 60-days ripened cheese (%CY_RIPENED) were the reference traits and were used as response variables. Training-testing linear regression modeling was performed: 80% of observations were randomly assigned to the training set, 20% to the validation set, and the procedure was repeated 10 times. Four groups of predictive equations were identified, in which different combinations of predictors were tested separately to predict %CY traits: (i) basic composition, i.e., fat, protein, and CN, tested individually and in combination; (ii) udder health indicators (UHI), i.e., fat + protein or CN + lactose and/or somatic cell score (SCS); (iii) detailed protein profile, i.e., fat + protein fractions [CN fractions, whey proteins, and nonprotein nitrogen (NPN) compounds]; (iv) detailed protein profile + UHI, i.e., fat + protein fractions + NPN compounds and/or UHI. Aside from the positive effect of fat, protein, and total casein on %CY, our results allowed us to disentangle the role of each casein fraction and whey protein, confirming the central role of β-CN and κ-CN, but also showing α-lactalbumin (α-LA) to have a favorable effect, and β-lactoglobulin (β-LG) a negative effect. Replacing protein or casein with individual milk protein and NPN fractions in the statistical models appreciably increased the validation accuracy of the equations. The cheese industry would benefit from an improvement, through genetic selection, of traits related to cheese yield and this study offers new insights into the quantification of the influence of milk components in composite selection indices with the aim of directly enhancing cheese production.

A rapid method to quantify casein in fluid milk by front-face fluorescence spectroscopy combined with chemometrics

Casein in fluid milk determines cheese yield and affects cheese quality. Traditional methods of measuring casein in milk involve lengthy sample preparations with labor-intensive nitrogen-based protein quantifications. The objective of this study was to quantify casein in fluid milk with different casein-to-crude-protein ratios using front-face fluorescence spectroscopy (FFFS) and chemometrics. We constructed calibration samples by mixing microfiltration and ultrafiltration retentate and permeate in different ratios to obtain different casein concentrations and casein-to-crude-protein ratios. We developed partial least squares regression and elastic net regression models for casein prediction in fluid milk using FFFS tryptophan emission spectra and reference casein contents. We used a set of 20 validation samples (including raw, skim, and ultrafiltered milk) to optimize and validate model performance. We externally tested another independent set of 20 test samples (including raw, skim, and ultrafiltered milk) by root mean square error of prediction (RMSEP), residual prediction deviation (RPD), and relative prediction error (RPE). The RMSEP for casein content quantification in raw, skim, and ultrafiltered milk ranged from 0.12 to 0.13%, and the RPD ranged from 3.2 to 3.4. The externally validated error of prediction was comparable to the existing rapid method and showed practical model performance for quality-control purposes. This FFFS-based method can be implemented as a routine quality-control tool in the dairy industry, providing rapid quantification of casein content in fluid milk intended for cheese manufacturing.

Milk Products from Minor Dairy Species: A Review

Milk processing is one of the most ancient food technologies, dating back around 6000 BC. The majority of dairy products are manufactured from cows, buffaloes, goats, and sheep; their production technologies are mostly standardized and have been widely investigated. Milk and dairy products from minor species are less important under the economic point of view, but they play a fundamental social role in many marginal and poor areas. Due to scarce interest of the dairy industry, their technological characteristics and related issues have been investigated less. Recently, the increasing interest toward ethnic foods and food biodiversity is helping these minor products to emerge from the "darkness" in which they have remained for long time. Some of them are increasingly seen as useful for the valorization of marginal areas, while others are recognized as innovative or healthy foods. The present review aims to resume the most recent knowledge about these less-known dairy products. The first part summarizes the main technological properties of equine, camel, and yak milk with a view to processing. The second is a survey on the related dairy products, both the traditional ones that have been manufactured for a long time and those that have been newly developed by food researchers.

Coagulation Traits of Sheep and Goat Milk

Milk production from sheep and goat species is continuously growing worldwide, and its main use is for cheesemaking. Given that the final quality of cheese is linked to the traits of raw milk cheese yield at dairy plants, it is often calculated by using predictive formulas based on fat and protein content. Predictive formulas have been studied for bovine milk and are very effective but not appropriate for sheep and goat milk. Several methods, which simulate the actual coagulation processes, are available at the laboratories. This article reviews the available literature about rennet coagulation and cheese yield traits from sheep and goat milk and the methods used at the laboratory level. In general, if compared to cow milk, sheep and goat milk are characterized by shorter rennet coagulation times and a very limited amount of non-coagulating samples. Curd firmness of sheep milk is almost independent from the rennet coagulation time, and some coagulation traits can be predicted by infrared spectra. In addition, coagulation traits are characterized by appropriate values of heritability to be considered in selective breeding plans. With regard to goat milk, rennet coagulation time and cheese yield are strongly influenced by the breed effect.

Effects of technological settings on yield, curd, whey, and cheese composition during the cheese-making process from raw sheep milk in small rural dairies: Emphasis on cutting and cooking conditions

The technological conditions of cheese-making affect cheese yield and compound losses in the whey, especially the processes of cutting and cooking. Although significant compositional and functional differences have been reported among animal species, there is a lack of studies on the effects of cheese-making technology on cheese yield and losses for sheep milk. Thus, we examined the cheese-making settings in 8 small rural dairies working with raw sheep milk and their effects on cheese yield and compound losses in whey during the cheese-production season. Actual cheese yield varied in 2 to 3 kg of cheese/100 kg of milk among dairies due to the cheese-making conditions, particularly the duration of cutting and cooking and the final cooking temperature. The combination of the conditions used during cutting and cooking, especially, determined fat losses in the whey. Fat losses were increased with high-speed and short cutting time settings together with high stirring speed and long duration of cooking. Additionally, cheese-makers should adapt the cutting and cooking conditions to the seasonal variations of milk composition, especially during early summer, when fat losses in the whey are higher. Our results suggest that it could be useful to use approximately 10 to 15 min of cutting time and moderate cooking speed and duration. The data reported in this study may assist the improvement of the cheese-making process in small rural dairies using sheep milk, where facilities are limited and the role of the cheese-maker is crucial.

Effect of goat milk composition on cheesemaking traits and daily cheese production

Cheese yield is strongly influenced by the composition of milk, especially fat and protein contents, and by the efficiency of the recovery of each milk component in the curd. The real effect of milk composition on cheesemaking ability of goat milk is still unknown. The aims of this study were to quantify the effects of milk composition; namely, fat, protein, and casein contents, on milk nutrient recovery in the curd, cheese yield, and average daily yield. Individual milk samples were collected from 560 goats of 6 different breeds. Each sample was analyzed in duplicate using the 9-laboratory milk cheesemaking assessment, a laboratory method that mimicked cheesemaking procedures, with milk heating, rennet addition, coagulation, curd cutting, and draining. Data were submitted to statistical analysis; results showed that the increase of milk fat content was associated with a large improvement of cheese yield because of the higher recovery of all milk nutrients in the curd, and thus a higher individual daily cheese yield. The increase of milk protein content affected the recovery of fat, total solids, and energy in the curd. Casein number, calculated as casein-to-protein ratio, did not affect protein recovery but strongly influenced the recovery of fat, showing a curvilinear pattern and the most favorable data for the intermediate values of casein number. In conclusion, increased fat and protein contents in the milk had an effect on cheese yield not only for the greater quantity of nutrients available but also for the improved efficiency of the recovery in the curd of all nutrients. These results are useful to improve knowledge on cheesemaking processes in the caprine dairy industry.

A 100-Year Review: Cheese production and quality

In the beginning, cheese making in the United States was all art, but embracing science and technology was necessary to make progress in producing a higher quality cheese. Traditional cheese making could not keep up with the demand for cheese, and the development of the factory system was necessary. Cheese quality suffered because of poor-quality milk, but 3 major innovations changed that: refrigeration, commercial starters, and the use of pasteurized milk for cheese making. Although by all accounts cold storage improved cheese quality, it was the improvement of milk quality, pasteurization of milk, and the use of reliable cultures for fermentation that had the biggest effect. Together with use of purified commercial cultures, pasteurization enabled cheese production to be conducted on a fixed time schedule. Fundamental research on the genetics of starter bacteria greatly increased the reliability of fermentation, which in turn made automation feasible. Demand for functionality, machinability, application in baking, and more emphasis on nutritional aspects (low fat and low sodium) of cheese took us back to the fundamental principles of cheese making and resulted in renewed vigor for scientific investigations into the chemical, microbiological, and enzymatic changes that occur during cheese making and ripening. As milk production increased, cheese factories needed to become more efficient. Membrane concentration and separation of milk offered a solution and greatly enhanced plant capacity. Full implementation of membrane processing and use of its full potential have yet to be achieved. Implementation of new technologies, the science of cheese making, and the development of further advances will require highly trained personnel at both the academic and industrial levels. This will be a great challenge to address and overcome.

Real-time evaluation of individual cow milk for higher cheese-milk quality with increased cheese yield

Cheese was produced in a series of experiments from milk separated in real time during milking by using the Afilab MCS milk classification service (Afikim, Israel), which is installed on the milk line in every stall and sorts milk in real time into 2 target tanks: the A tank for cheese production (CM) and the B tank for fluid milk products (FM). The cheese milk was prepared in varying ratios ranging from ~10:90 to ~90:10 CM:FM by using this system. Cheese was made with corrected protein-to-fat ratio and without it, as well as from milk stored at 4°C for 1, 2, 3, 4, and 8d before production. Cheese weight at 24h increased along the separation cutoff level with no difference in moisture, and dry matter increased. The data compiled allowed a theoretical calculation of cheese yield and comparing it to the original van Slyke equation. Whenever the value of Afi-Cf, which is the optical measure of curd firmness obtained by the Afilab instrument, was used, a better predicted level of cheese yield was obtained. In addition, 27 bulk milk tanks with milk separated at a 50:50 CM:FM ratio resulted in cheese with a significantly higher fat and protein, dry matter, and weight at 24h. Moreover, solids incorporated from the milk into the cheese were significantly higher in cheeses made of milk from A tanks. The influence of storage of milk up to 8d before cheese making was tested. Gross milk composition did not change and no differences were found in cheese moisture, but dry matter and protein incorporated in the cheese dropped significantly along the storage time. These findings confirm that milk stored for several days before processing is prone to physico-chemical deterioration processes, which result in loss of milk constituents to the whey and therefore reduced product yield. The study demonstrates that introducing the unknown parameters for calculating the predicted cheese yield, such as the empiric measured Afi-Cf properties, are more accurate and the increase in cheese yield is more than increasing just the protein level, the value that is being tested by the dairies, or even casein.

Real-time evaluation of milk quality as reflected by clotting parameters of individual cow's milk during the milking session, between day-to-day and during lactation

Real-time analysis of milk coagulation properties as performed by the AfiLab™ milk spectrometer introduces new opportunities for the dairy industry. The study evaluated the performance of the AfiLab™ in a milking parlor of a commercial farm to provide real-time analysis of milk-clotting parameters -Afi-CF for cheese manufacture and determine its repeatability in time for individual cows. The AfiLab™ in a parlor, equipped with two parallel milk lines, enables to divert the milk on-line into two bulk milk tanks (A and B). Three commercial dairy herds of 220 to 320 Israeli Holstein cows producing ∼11 500 l during 305 days were selected for the study. The Afi-CF repeatability during time was found significant (P < 0.001) for cows. The statistic model succeeded in explaining 83.5% of the variance between Afi-CF and cows, and no significant variance was found between the mean weekly repeated recordings. Days in milk and log somatic cell count (SCC) had no significant effect. Fat, protein and lactose significantly affected Afi-CF and the empirical van Slyke equation. Real-time simulations were performed for different cutoff levels of coagulation properties where the milk of high Afi-CF cutoff value was channeled to tank A and the lower into tank B. The simulations showed that milk coagulation properties of an individual cow are not uniform, as most cows contributed milk to both tanks. Proportions of the individual cow's milk in each tank depended on the selected Afi-CF cutoff. The assessment of the major causative factors of a cow producing low-quality milk for cheese production was evaluated for the group that produced the low 10% quality milk. The largest number of cows in those groups at the three farms was found to be cows with post-intramammary infection with Escherichia coli and subclinical infections with streptococci or coagulase-negative staphylococci (∼30%), although the SCC of these cows was not significantly different. Early time in lactation together with high milk yield >50 l/day, and late in lactation together with low milk yield<15 l/day and estrous (0 to 5 days) were also important influencing factors for low-quality milk. However, ∼50% of the tested variables did not explain any of the factors responsible for the cow producing milk in the low - 10% Afi-CF.