Characterization of milk fatty acids based on genetic and herd parameters

The objective of this study was to characterize the fatty acids (FA) in milk based on genetic and herd parameters to investigate the origin of the different FA in milk. Milk samples of 1912 Dutch Holstein-Friesian cows were analysed for 39 different FA including odd and branched-chain fatty acids. The proportion of variation caused by genetic and herd effects was calculated. In addition, genetic and herd correlations among the fatty acids were estimated and a clustering technique was used to visualise these correlations. The results indicated that in Dutch milk C12:0 is not completely synthesised de novo but also partly blood derived. It was suggested that C20:0 in milk is formed from the action of elongase enzymes on C18:0 and that the odd-chain FA C5:0-C13:0 and a part of C15:0 and C17:0 are synthesised de novo while the other part of C15:0 and C17:0 is blood derived. Furthermore, this work gives an overview of the opportunities to change the concentration of individual FA both by breeding and feeding. It is clearly shown that the extent to which the individual FA can be changed varies greatly and is dependent on the origin of the different FA in milk

Metabolomics as an emerging strategy for the investigation of yogurt components

The advanced development in metabolomics allows discovery of a wide range of metabolites in complex biological systems including food matrices. This analytical approach provides opportunities to attain a global metabolite profile and discover potential biomarkers and various chemical contaminants that can be directly correlated with the quality, safety, and authenticity of the food product. This chapter provides information on the applications of metabolomics as an emerging strategy for the investigation of yogurt components. The fundamentals of metabolomics and statistical interpretation of data are described. The implementation of metabolomics as part of foodomics technologies is discussed. Some of the noteworthy studies and possibility for using an advanced high-throughput metabolomics platform in assessing and predicting the quality of yogurt are highlighted. Finally, the application of nuclear magnetic resonance-based and mass spectrometry-based technologies for determining a wide range of metabolites associated with the metabolism of starter cultures and probiotics during yogurt fermentation and storage are discussed.Keywords: Chemometrics; Dairy; Fermented milk; Flavor; Lactic acid bacteria; Metabolite profile; Metabolomics; Milk; Probiotics; Starter culture<br/

Quality control of raw cows' milk by headspace analysis

This study investigated whether headspace analysis of volatile components can be used for monitoring the quality of raw cows¿ milk. The detection of different quality defects caused by cows¿ feed, microbiological and chemical contamination, as well as enzymatic deterioration was studied. Fresh raw milk without quality defects was shown to always contain the same seven volatile components. It was also shown that treatments like heating and homogenization of raw milk may drastically change this basic pattern resulting in as much as a 10-fold increase in the number of volatile compounds. The growth of Pseudomonas could not be detected in an early stage using headspace analysis. Feed was shown to have an effect on the volatile composition if specific vegetable byproducts were fed to the cow. Chloroform contamination was quantified using the method. Also, the extent of lipolysis could be quantified by measuring the free fatty acids. For quantification of both chloroform and lipolysis, the sensitivity and reproducibility of the method were sufficient for quality control purposes. The method was thus able to detect several quality defects with a single analysis and may therefore be a useful supplementary method for raw milk quality control

Exploiting genetic variation in milk-fat composition of milk from dairy cows

Milk fat contains many nutrients necessary for humans, including fat-soluble vitamins, energy, and bioactive lipids. It is important to understand the genetic basis for milk-fat composition in cows’ milk. Knowledge of these genetic parameters can be used to predict how different traits will respond to genetic selection. The predicted response in traits, including trade-offs between different traits, plays an important role in designing breeding schemes in dairy cattle. This chapter reviews the genetic variation in milk-fat composition between cows and looks into the genetic correlations with other traits

Metabolomics as an emerging strategy for the investigation of yogurt components

The advanced development in metabolomics allows discovery of a wide range of metabolites in complex biological systems including food matrices. This analytical approach provides opportunities to attain a global metabolite profile and discover potential biomarkers and various chemical contaminants that can be directly correlated with the quality, safety, and authenticity of the food product. This chapter provides information on the applications of metabolomics as an emerging strategy for the investigation of yogurt components. The fundamentals of metabolomics and statistical interpretation of data are described. The implementation of metabolomics as part of foodomics technologies is discussed. Some of the noteworthy studies and possibility for using an advanced high-throughput metabolomics platform in assessing and predicting the quality of yogurt are highlighted. Finally, the application of nuclear magnetic resonance-based and mass spectrometry-based technologies for determining a wide range of metabolites associated with the metabolism of starter cultures and probiotics during yogurt fermentation and storage are discussed.Keywords: Chemometrics; Dairy; Fermented milk; Flavor; Lactic acid bacteria; Metabolite profile; Metabolomics; Milk; Probiotics; Starter culture<br/

Quality control of raw cows' milk by headspace analysis

This study investigated whether headspace analysis of volatile components can be used for monitoring the quality of raw cows¿ milk. The detection of different quality defects caused by cows¿ feed, microbiological and chemical contamination, as well as enzymatic deterioration was studied. Fresh raw milk without quality defects was shown to always contain the same seven volatile components. It was also shown that treatments like heating and homogenization of raw milk may drastically change this basic pattern resulting in as much as a 10-fold increase in the number of volatile compounds. The growth of Pseudomonas could not be detected in an early stage using headspace analysis. Feed was shown to have an effect on the volatile composition if specific vegetable byproducts were fed to the cow. Chloroform contamination was quantified using the method. Also, the extent of lipolysis could be quantified by measuring the free fatty acids. For quantification of both chloroform and lipolysis, the sensitivity and reproducibility of the method were sufficient for quality control purposes. The method was thus able to detect several quality defects with a single analysis and may therefore be a useful supplementary method for raw milk quality control

Detection of mastitis pathogens by analysis of volatile bacterial metabolites

The ability to detect mastitis pathogens based on their volatile metabolites was studied. Milk samples from cows with clinical mastitis, caused by Staphylococcus aureus, coagulase-negative staphylococci, Streptococcus uberis, Streptococcus dysgalactiae, and Escherichia coli were collected. In addition, samples from cows without clinical mastitis and with low somatic cell count (SCC) were collected for comparison. All mastitis samples were examined by using classical microbiological methods, followed by headspace analysis for volatile metabolites. Milk from culture-negative samples contained a lower number and amount of volatile components compared with cows with clinical mastitis. Because of variability between samples within a group, comparisons between pathogens were not sufficient for classification of the samples by univariate statistics. Therefore, an artificial neural network was trained to classify the pathogen in the milk samples based on the bacterial metabolites. The trained network differentiated milk from uninfected and infected quarters very well. When comparing pathogens, Staph. aureus produced a very different pattern of volatile metabolites compared with the other samples. Samples with coagulase-negative staphylococci and E. coli had enough dissimilarity with the other pathogens, making it possible to separate these 2 pathogens from each other and from the other samples. The 2 streptococcus species did not show significant differences between each other but could be identified as a different group from the other pathogens. Five groups can thus be identified based on the volatile bacterial metabolites: Staph. aureus, coagulase-negative staphylococci, streptococci (Strep. uberis and Strep. dysgalactiae as one group), E. coli, and uninfected quarter

The origin of the volatile metabolites found in mastitis milk

The possibility to detect mastitis pathogens based on their volatile metabolites was previously studied. Because the origin of the metabolites is unknown, the formation of volatile metabolites by five mastitis pathogens inoculated in milk of healthy cows was studied. The volatile metabolites from inoculated samples were compared to those of mastitis milk samples from which the inoculated pathogens were isolated. Most metabolites formed in the inoculated samples were similar to the metabolites formed in mastitis samples, both in presence and in amount. Prediction by a neural network showed that the similarity between the inoculated samples and mastitis samples was sufficient for correct prediction of the pathogen in the inoculated sample. The main difference between the inoculated samples and the mastitis samples was the absence of ethyl esters of free fatty acids in inoculated samples. This could be explained by disturbance of the milk¿blood barrier, allowing the transfer of esterase from the cows¿ blood to the milk in cows with mastiti

Protein, casein and micellar salts in milk: Current content and historical perspectives

The protein and fat content of Dutch bulk milk has been monitored since the 1950s and has increased considerably, by 11 and 20%, respectively, whereas milk yield has more than doubled. The change in protein and fat content of milk is advantageous for the dairy industry, as these are the 2 most economically valuable constituents of milk. Increases in protein and fat content of milk have allowed increases in the yield of various products such as cheese and butter. However, for cheese and other applications where casein micelles play a crucial role in structure and stability, it is not only casein content, but also the properties of the casein micelles that determine processability. Of particular importance herein is the salt partition in milk, but it is unknown whether increased protein content has affected the milk salts and their distribution between casein micelles and milk serum. It was, therefore, the objective of this research to determine the salt composition and protein content for individual cow milk and bulk milk over a period of 1 yr and to compare these data to results obtained during the 1930s, 1950s, and 1960s in the last century. Calcium, magnesium, sodium, potassium, and phosphorus content were determined by inductively coupled plasma atomic emission spectrometry and inorganic phosphate, citrate, chloride, and sulfate content by anion-exchange chromatography in bulk milk and milk ultracentrifugate. In addition, ionic calcium and ionic magnesium concentration were determined by the Donnan membrane technique. We concluded that historical increase in milk yield and protein content in milk have resulted in correlated changes in casein content and the micellar salt fraction of milk. In addition, the essential nutrients, calcium, magnesium, and phosphorus in milk have increased the past 75 yr; therefore, the nutritional value of milk has improved. Key words: milk protein , casein , calcium phosphate , magnesiu

Protein, casein and micellar salts in milk: Current content and historical perspectives

The protein and fat content of Dutch bulk milk has been monitored since the 1950s and has increased considerably, by 11 and 20%, respectively, whereas milk yield has more than doubled. The change in protein and fat content of milk is advantageous for the dairy industry, as these are the 2 most economically valuable constituents of milk. Increases in protein and fat content of milk have allowed increases in the yield of various products such as cheese and butter. However, for cheese and other applications where casein micelles play a crucial role in structure and stability, it is not only casein content, but also the properties of the casein micelles that determine processability. Of particular importance herein is the salt partition in milk, but it is unknown whether increased protein content has affected the milk salts and their distribution between casein micelles and milk serum. It was, therefore, the objective of this research to determine the salt composition and protein content for individual cow milk and bulk milk over a period of 1 yr and to compare these data to results obtained during the 1930s, 1950s, and 1960s in the last century. Calcium, magnesium, sodium, potassium, and phosphorus content were determined by inductively coupled plasma atomic emission spectrometry and inorganic phosphate, citrate, chloride, and sulfate content by anion-exchange chromatography in bulk milk and milk ultracentrifugate. In addition, ionic calcium and ionic magnesium concentration were determined by the Donnan membrane technique. We concluded that historical increase in milk yield and protein content in milk have resulted in correlated changes in casein content and the micellar salt fraction of milk. In addition, the essential nutrients, calcium, magnesium, and phosphorus in milk have increased the past 75 yr; therefore, the nutritional value of milk has improved. Key words: milk protein , casein , calcium phosphate , magnesiu