Short communication: Volatile profile of matured Tronchón cheese affected by oxytetracycline in raw goat milk.

The presence of antibiotics in milk destined for cheese production may affect the biological processes responsible for the formation of volatile compounds, leading to alterations in the characteristic cheese flavor expected by consumers. The aim of this study was to evaluate the effect of the presence of oxytetracycline in goat milk on the volatile profile of ripened cheeses. Traditional mature Tronchón cheeses were manufactured from raw goat milk spiked with different concentrations of oxytetracycline (50, 100, and 200 µg/kg). Cheese made from antibiotic-free goat milk was used as control. We analyzed the residual amounts of the antibiotic and the volatile profile of the experimental cheeses on a fortnightly basis during maturation using liquid chromatography tandem mass spectrometry and then solid-phase microextraction followed by gas chromatography-mass spectrometry. Our results suggested that oxytetracycline was widely transferred from milk to cheese: drug concentrations in the cheeses were 3.5 to 4.3 times higher than those in raw milk. Although the residual amounts of oxytetracycline significantly decreased during maturation (88.8 to 96.5%), variable amounts of residues remained in cheese matured for 60 d (<10 to 79 µg/kg). In general, the presence of oxytetracycline in goat milk did not affect the volatile profile of Tronchón cheeses; volatile profile was significantly modified by ripening time. Still, the presence of oxytetracycline residues in cheeses ripened for 60 d could be of great concern for public health.

Human Milk Oligosaccharides in Colostrum and Mature Milk of Chinese Mothers: Lewis Positive Secretor Subgroups.

To study the variability in human milk oligosaccharide (HMO) composition of Chinese human milk over a 20-wk lactation period, HMO profiles of 30 mothers were analyzed using CE-LIF. This study showed that total HMO concentrations in Chinese human milk decreased significantly over a 20-wk lactation period, independent of the mother's SeLe status, although with individual variations. In addition, total acidic and neutral HMO concentrations in Chinese human milk decreased over lactation, and levels are driven by their mother's SeLe status. Analysis showed that total neutral fucosylated HMO concentrations in Chinese human milk were higher in the two secretor groups as compared to the nonsecretor group. On the basis of the total neutral fucosylated HMO concentrations in Chinese human milk, HMO profiles within the Se+Le+ group can be divided into two subgroups. HMOs that differed in level between Se+Le+ subgroups were 2'FL, DF-L, LNFP I, and F-LNO. HMO profiles in Dutch human milk also showed Se+Le+ subgroup division, with 2'FL, LNT, and F-LNO as the driving force.

Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography-based milk fatty acid profiles.

The objective of the present study was to compare the prediction potential of milk Fourier-transform infrared spectroscopy (FTIR) for CH4 emissions of dairy cows with that of gas chromatography (GC)-based milk fatty acids (MFA). Data from 9 experiments with lactating Holstein-Friesian cows, with a total of 30 dietary treatments and 218 observations, were used. Methane emissions were measured for 3 consecutive days in climate respiration chambers and expressed as production (g/d), yield (g/kg of dry matter intake; DMI), and intensity (g/kg of fat- and protein-corrected milk; FPCM). Dry matter intake was 16.3 ± 2.18 kg/d (mean ± standard deviation), FPCM yield was 25.9 ± 5.06 kg/d, CH4 production was 366 ± 53.9 g/d, CH4 yield was 22.5 ± 2.10 g/kg of DMI, and CH4 intensity was 14.4 ± 2.58 g/kg of FPCM. Milk was sampled during the same days and analyzed by GC and by FTIR. Multivariate GC-determined MFA-based and FTIR-based CH4 prediction models were developed, and subsequently, the final CH4 prediction models were evaluated with root mean squared error of prediction and concordance correlation coefficient analysis. Further, we performed a random 10-fold cross validation to calculate the performance parameters of the models (e.g., the coefficient of determination of cross validation). The final GC-determined MFA-based CH4 prediction models estimate CH4 production, yield, and intensity with a root mean squared error of prediction of 35.7 g/d, 1.6 g/kg of DMI, and 1.6 g/kg of FPCM and with a concordance correlation coefficient of 0.72, 0.59, and 0.77, respectively. The final FTIR-based CH4 prediction models estimate CH4 production, yield, and intensity with a root mean squared error of prediction of 43.2 g/d, 1.9 g/kg of DMI, and 1.7 g/kg of FPCM and with a concordance correlation coefficient of 0.52, 0.40, and 0.72, respectively. The GC-determined MFA-based prediction models described a greater part of the observed variation in CH4 emission than did the FTIR-based models. The cross validation results indicate that all CH4 prediction models (both GC-determined MFA-based and FTIR-based models) are robust; the difference between the coefficient of determination and the coefficient of determination of cross validation ranged from 0.01 to 0.07. The results indicate that GC-determined MFA have a greater potential than FTIR spectra to estimate CH4 production, yield, and intensity. Both techniques hold potential but may not yet be ready to predict CH4 emission of dairy cows in practice. Additional CH4 measurements are needed to improve the accuracy and robustness of GC-determined MFA and FTIR spectra for CH4 prediction.

Short communication: The effect of linseed oil and DGAT1 K232A polymorphism on the methane emission prediction potential of milk fatty acids.

Several in vivo CH4 measurement techniques have been developed but are not suitable for precise and accurate large-scale measurements; hence, proxies for CH4 emissions in dairy cattle have been proposed, including the milk fatty acid (MFA) profile. The aim of the present study was to determine whether recently developed MFA-based prediction equations for CH4 emission are applicable to dairy cows with different diacylglycerol o-acyltransferase 1 (DGAT1) K232A polymorphism and fed diets with and without linseed oil. Data from a crossover design experiment were used, encompassing 2 dietary treatments (i.e., a control diet and a linseed oil diet, with a difference in dietary fat content of 22 g/kg of dry matter) and 24 lactating Holstein-Friesian cows (i.e., 12 cows with DGAT1 KK genotype and 12 cows with DGAT1 AA genotype). Enteric CH4 production was measured in climate respiration chambers and the MFA profile was analyzed using gas chromatography. Observed CH4 emissions were compared with CH4 emissions predicted by previously developed MFA-based CH4 prediction equations. The results indicate that different types of diets (i.e., with or without linseed oil), but not the DGAT1 K232A polymorphism, affect the ability of previously derived prediction equations to predict CH4 emission. However, the concordance correlation coefficient was smaller than or equal to 0.30 for both dietary treatments separately, both DGAT1 genotypes separately, and the complete data set. We therefore concluded that previously derived MFA-based CH4 prediction equations can neither accurately nor precisely predict CH4 emissions of dairy cows managed under strategies differing from those under which the original prediction equations were developed.

The relationship between milk metabolome and methane emission of Holstein Friesian dairy cows: Metabolic interpretation and prediction potential.

This study aimed to quantify the relationship between CH4 emission and fatty acids, volatile metabolites, and nonvolatile metabolites in milk of dairy cows fed forage-based diets. Data from 6 studies were used, including 27 dietary treatments and 123 individual observations from lactating Holstein-Friesian cows. These dietary treatments covered a large range of forage-based diets, with different qualities and proportions of grass silage and corn silage. Methane emission was measured in climate respiration chambers and expressed as production (g per day), yield (g per kg of dry matter intake; DMI), and intensity (g per kg of fat- and protein-corrected milk; FPCM). Milk samples were analyzed for fatty acids by gas chromatography, for volatile metabolites by gas chromatography-mass spectrometry, and for nonvolatile metabolites by nuclear magnetic resonance. Dry matter intake was 15.9 ± 1.90 kg/d (mean ± SD), FPCM yield was 25.2 ± 4.57 kg/d, CH4 production was 359 ± 51.1 g/d, CH4 yield was 22.6 ± 2.31 g/kg of DMI, and CH4 intensity was 14.5 ± 2.59 g/kg of FPCM. The results show that changes in individual milk metabolite concentrations can be related to the ruminal CH4 production pathways. Several of these relationships were diet driven, whereas some were partly dependent on FPCM yield. Next, prediction models were developed and subsequently evaluated based on root mean square error of prediction (RMSEP), concordance correlation coefficient (CCC) analysis, and random 10-fold cross-validation. The best models with milk fatty acids (in g/100 g of fatty acids; MFA) alone predicted CH4 production, yield, and intensity with a RMSEP of 34 g/d, 2.0 g/kg of DMI, and 1.7 g/kg of FPCM, and with a CCC of 0.67, 0.44, and 0.75, respectively. The CH4 prediction potential of both volatile metabolites alone and nonvolatile metabolites alone was low, regardless of the unit of CH4 emission, as evidenced by the low CCC values (<0.35). The best models combining the 3 types of metabolites as selection variables resulted in the inclusion of only MFA for CH4 production and CH4 yield. For CH4 intensity, MFA, volatile metabolites, and nonvolatile metabolites were included in the prediction model. This resulted in a small improvement in prediction potential (CCC of 0.80; RMSEP of 1.5 g/kg of FPCM) relative to MFA alone. These results indicate that volatile and nonvolatile metabolites in milk contain some information to increase our understanding of enteric CH4 production of dairy cows, but that it is not worthwhile to determine the volatile and nonvolatile metabolites in milk to estimate CH4 emission of dairy cows. We conclude that MFA have moderate potential to predict CH4 emission of dairy cattle fed forage-based diets, and that the models can aid in the effort to understand and mitigate CH4 emissions of dairy cows.

Linseed oil and DGAT1 K232A polymorphism: Effects on methane emission, energy and nitrogen metabolism, lactation performance, ruminal fermentation, and rumen microbial composition of Holstein-Friesian cows.

Complex interactions between rumen microbiota, cow genetics, and diet composition may exist. Therefore, the effect of linseed oil, DGAT1 K232A polymorphism (DGAT1), and the interaction between linseed oil and DGAT1 on CH4 and H2 emission, energy and N metabolism, lactation performance, ruminal fermentation, and rumen bacterial and archaeal composition was investigated. Twenty-four lactating Holstein-Friesian cows (i.e., 12 with DGAT1 KK genotype and 12 with DGAT1 AA genotype) were fed 2 diets in a crossover design: a control diet and a linseed oil diet (LSO) with a difference of 22 g/kg of dry matter (DM) in fat content between the 2 diets. Both diets consisted of 40% corn silage, 30% grass silage, and 30% concentrates (DM basis). Apparent digestibility, lactation performance, N and energy balance, and CH4 emission were measured in climate respiration chambers, and rumen fluid samples were collected using the oral stomach tube technique. No linseed oil by DGAT1 interactions were observed for digestibility, milk production and composition, energy and N balance, CH4 and H2 emissions, and rumen volatile fatty acid concentrations. The DGAT1 KK genotype was associated with a lower proportion of polyunsaturated fatty acids in milk fat, and with a higher milk fat and protein content, and proportion of saturated fatty acids in milk fat compared with the DGAT1 AA genotype, whereas the fat- and protein-corrected milk yield was unaffected by DGAT1. Also, DGAT1 did not affect nutrient digestibility, CH4 or H2 emission, ruminal fermentation or ruminal archaeal and bacterial concentrations. Rumen bacterial and archaeal composition was also unaffected in terms of the whole community, whereas at the genus level the relative abundances of some bacterial genera were found to be affected by DGAT1. The DGAT1 KK genotype was associated with a lower metabolizability (i.e., ratio of metabolizable to gross energy intake), and with a tendency for a lower milk N efficiency compared with the DGAT1 AA genotype. The LSO diet tended to decrease CH4 production (g/d) by 8%, and significantly decreased CH4 yield (g/kg of DM intake) by 6% and CH4 intensity (g/kg of fat- and protein-corrected milk) by 11%, but did not affect H2 emission. The LSO diet also decreased ruminal acetate molar proportion, the acetate to propionate ratio, and the archaea to bacteria ratio, whereas ruminal propionate molar proportion and milk N efficiency increased. Ruminal bacterial and archaeal composition tended to be affected by diet in terms of the whole community, with several bacterial genera found to be significantly affected by diet. These results indicate that DGAT1 does not affect enteric CH4 emission and production pathways, but that it does affect traits other than lactation characteristics, including metabolizability, N efficiency, and the relative abundance of Bifidobacterium. Additionally, linseed oil reduces CH4 emission independent of DGAT1 and affects the rumen microbiota and its fermentative activity.

Relationships between methane emission of Holstein Friesian dairy cows and fatty acids, volatile metabolites and non-volatile metabolites in milk.

This study investigated the relationships between methane (CH4) emission and fatty acids, volatile metabolites (V) and non-volatile metabolites (NV) in milk of dairy cows. Data from an experiment with 32 multiparous dairy cows and four diets were used. All diets had a roughage : concentrate ratio of 80 : 20 based on dry matter (DM). Roughage consisted of either 1000 g/kg DM grass silage (GS), 1000 g/kg DM maize silage (MS), or a mixture of both silages (667 g/kg DM GS and 333 g/kg DM MS; 333 g/kg DM GS and 677 g/kg DM MS). Methane emission was measured in climate respiration chambers and expressed as production (g/day), yield (g/kg dry matter intake; DMI) and intensity (g/kg fat- and protein-corrected milk; FPCM). Milk was sampled during the same days and analysed for fatty acids by gas chromatography, for V by gas chromatography-mass spectrometry, and for NV by nuclear magnetic resonance. Several models were obtained using a stepwise selection of (1) milk fatty acids (MFA), V or NV alone, and (2) the combination of MFA, V and NV, based on the minimum Akaike's information criterion statistic. Dry matter intake was 16.8±1.23 kg/day, FPCM yield was 25.0±3.14 kg/day, CH4 production was 406±37.0 g/day, CH4 yield was 24.1±1.87 g/kg DMI and CH4 intensity was 16.4±1.91 g/kg FPCM. The observed CH4 emissions were compared with the CH4 emissions predicted by the obtained models, based on concordance correlation coefficient (CCC) analysis. The best models with MFA alone predicted CH4 production, yield and intensity with a CCC of 0.80, 0.71 and 0.69, respectively. The best models combining the three types of metabolites included MFA and NV for CH4 production and CH4 yield, whereas for CH4 intensity MFA, NV and V were all included. These models predicted CH4 production, yield and intensity better with a higher CCC of 0.92, 0.78 and 0.93, respectively, and with increased accuracy (C b ) and precision (r). The results indicate that MFA alone have moderate to good potential to estimate CH4 emission, and furthermore that including V (CH4 intensity only) and NV increases the CH4 emission prediction potential. This holds particularly for the prediction model for CH4 intensity.

A proteomics-based identification of putative biomarkers for disease in bovine milk.

The objective of this study was to identify and characterize potential biomarkers for disease resistance in bovine milk that can be used to indicate dairy cows at risk to develop future health problems. We selected high- and low-resistant cows i.e. cows that were less or more prone to develop diseases according to farmers' experience and notifications in the disease registration data. The protein composition of milk serum samples of these high- and low-resistant cows were compared using NanoLC-MS/MS. In total 78 proteins were identified and quantified of which 13 were significantly more abundant in low-resistant cows than high-resistant cows. Quantification of one of these proteins, lactoferrin (LF), by ELISA in a new and much larger set of full fat milk samples confirmed higher LF levels in low- versus high-resistant cows. These high- and low-resistant cows were selected based on comprehensive disease registration and milk recording data, and absence of disease for at least 4 weeks. Relating the experienced diseases to LF levels in milk showed that lameness was associated with higher LF levels in milk. Analysis of the prognostic value of LF showed that low-resistant cows with higher LF levels in milk had a higher risk of being culled within one year after testing than high-resistant cows. In conclusion, LF in milk are higher in low-resistant cows, are associated with lameness and may be a prognostic marker for risk of premature culling.

Milk metabolome relates enteric methane emission to milk synthesis and energy metabolism pathways.

Methane (CH4) emission of dairy cows contributes significantly to the carbon footprint of the dairy chain; therefore, a better understanding of CH4 formation is urgently needed. The present study explored the milk metabolome by gas chromatography-mass spectrometry (milk volatile metabolites) and nuclear magnetic resonance (milk nonvolatile metabolites) to better understand the biological pathways involved in CH4 emission in dairy cattle. Data were used from a randomized block design experiment with 32 multiparous Holstein-Friesian cows and 4 diets. All diets had a roughage:concentrate ratio of 80:20 (dry matter basis) and the roughage was grass silage (GS), corn silage (CS), or a mixture of both (67% GS, 33% CS; 33% GS, 67% CS). Methane emission was measured in climate respiration chambers and expressed as CH4 yield (per unit of dry matter intake) and CH4 intensity (per unit of fat- and protein-corrected milk; FPCM). No volatile or nonvolatile metabolite was positively related to CH4 yield, and acetone (measured as a volatile and as a nonvolatile metabolite) was negatively related to CH4 yield. The volatile metabolites 1-heptanol-decanol, 3-nonanone, ethanol, and tetrahydrofuran were positively related to CH4 intensity. None of the volatile metabolites was negatively related to CH4 intensity. The nonvolatile metabolites acetoacetate, creatinine, ethanol, formate, methylmalonate, and N-acetylsugar A were positively related to CH4 intensity, and uridine diphosphate (UDP)-hexose B and citrate were negatively related to CH4 intensity. Several volatile and nonvolatile metabolites that were correlated with CH4 intensity also were correlated with FPCM and not significantly related to CH4 intensity anymore when FPCM was included as covariate. This suggests that changes in these milk metabolites may be related to changes in milk yield or metabolic processes involved in milk synthesis. The UDP-hexose B was correlated with FPCM, whereas citrate was not. Both metabolites were still related to CH4 intensity when FPCM was included as covariate. The UDP-hexose B is an intermediate of lactose metabolism, and citrate is an important intermediate of Krebs cycle-related energy processes. Therefore, the negative correlation of UDP-hexose B and citrate with CH4 intensity may reflect a decrease in metabolic activity in the mammary gland. Our results suggest that an integrative approach including milk yield and composition, and dietary and animal traits will help to explain the biological metabolism of dairy cows in relation to methane CH4 emission.

A proteomic perspective on the changes in milk proteins due to high somatic cell count.

Although cows with subclinical mastitis have no difference in the appearance of their milk, milk composition and milk quality are altered because of the inflammation. To know the changes in milk quality with different somatic cell count (SCC) levels, 5 pooled bovine milk samples with SCC from 10(5) to 10(6) cells/mL were analyzed qualitatively and quantitatively using both one-dimension sodium dodecyl sulfate PAGE and filter-aided sample preparation coupled with dimethyl labeling, both followed by liquid chromatography tandem mass spectrometry. Minor differences were found on the qualitative level in the proteome from milk with different SCC levels, whereas the concentration of milk proteins showed remarkable changes. Not only immune-related proteins (cathelicidins, IGK protein, CD59 molecule, complement regulatory protein, lactadherin), but also proteins with other biological functions (e.g., lipid metabolism: platelet glycoprotein 4, butyrophilin subfamily 1 member A1, perilipin-2) were significantly different in milk from cows with high SCC level compared with low SCC level. The increased concentration of protease inhibitors in the milk with higher SCC levels may suggest a protective role in the mammary gland against protease activity. Prostaglandin-H2 D-isomerase showed a linear relation with SCC, which was confirmed with an ELISA. However, the correlation coefficient was lower in individual cows compared with bulk milk. These results indicate that prostaglandin-H2 D-isomerase may be used as an indicator to evaluate bulk milk quality and thereby reduce the economic loss in the dairy industry. The results from this study reflect the biological phenomena occurring during subclinical mastitis and in addition provide a potential indicator for the detection of bulk milk with high SCC.

Enteric methane production, rumen volatile fatty acid concentrations, and milk fatty acid composition in lactating Holstein-Friesian cows fed grass silage- or corn silage-based diets.

The objective of this study was to determine the effects of replacing grass silage (GS) with corn silage (CS) in dairy cow diets on enteric methane (CH4) production, rumen volatile fatty acid concentrations, and milk fatty acid (FA) composition. A completely randomized block design experiment was conducted with 32 multiparous lactating Holstein-Friesian cows. Four dietary treatments were used, all having a roughage-to-concentrate ratio of 80:20 based on dry matter (DM). The roughage consisted of either 100% GS, 67% GS and 33% CS, 33% GS and 67% CS, or 100% CS (all DM basis). Feed intake was restricted (95% of ad libitum DM intake) to avoid confounding effects of DM intake on CH4 production. Nutrient intake, apparent digestibility, milk production and composition, nitrogen (N) and energy balance, and CH4 production were measured during a 5-d period in climate respiration chambers after adaptation to the diet for 12 d. Increasing CS proportion linearly decreased neutral detergent fiber and crude protein intake and linearly increased starch intake. Milk production and milk fat content (on average 23.4 kg/d and 4.68%, respectively) were not affected by increasing CS inclusion, whereas milk protein content increased quadratically. Rumen variables were unaffected by increasing CS inclusion, except the molar proportion of butyrate, which increased linearly. Methane production (expressed as grams per day, grams per kilogram of fat- and protein-corrected milk, and as a percent of gross energy intake) decreased quadratically with increasing CS inclusion, and decreased linearly when expressed as grams of CH4 per kilogram of DM intake. In comparison with 100% GS, CH4 production was 11 and 8% reduced for the 100% CS diet when expressed per unit of DM intake and per unit fat- and protein-corrected milk, respectively. Nitrogen efficiency increased linearly with increased inclusion of CS. The concentration of trans C18:1 FA, C18:1 cis-12, and total CLA increased quadratically, and iso C16:0, C18:1 cis-13, and C18:2n-6 increased linearly, whereas the concentration of C15:0, iso C15:0, C17:0, and C18:3n-3 decreased linearly with increasing inclusion of CS. No differences were found in short- and medium-straight, even-chain FA concentrations, with the exception of C4:0 which increased linearly with increased inclusion of CS. Replacing GS with CS in a common forage-based diet for dairy cattle offers an effective strategy to decrease enteric CH4 production without negatively affecting dairy cow performance, although a critical level of starch in the diet seems to be needed.

Concentrations of n-3 and n-6 fatty acids in Dutch bovine milk fat and their contribution to human dietary intake.

Weekly samples representative of Dutch milk were analyzed for concentrations of n-3 and n-6 fatty acids (FA). Concentrations of the n-3 FA α-linolenic acid (ALA), eicosatetraenoic acid, eicosapentaenoic acid, and docosapentaenoic acid were 0.495±0.027, 0.041±0.004, 0.067±0.005, and 0.086±0.008g per 100g of fat, respectively, whereas docosahexaenoic acid was absent or present in concentrations lower than 0.020g per 100g of fat. Concentrations of the n-6 FA linoleic acid (LeA), γ-linoleic acid, dihomo-γ-linoleic acid, and arachidonic acid were 1.428±0.068, 0.070±0.007, 0.066±0.004, and 0.089±0.004g per 100g of fat, respectively; adrenic acid was present in concentrations lower than 0.020g per 100g of fat, whereas docosapentaenoic acid was absent in all samples. The concentrations of ALA and LeA were significantly higher in spring and summer, compared with autumn and winter. The concentrations of all other ALA- and LeA-derived n-3 and n-6 FA were not significantly different between seasons. The contribution of milk fat to the daily intake of eicosapentaenoic acid, docosapentaenoic acid and docosahexaenoic acid was calculated for human consumption levels in different countries. Milk fat contributed between 10.7 and 14.1% to the daily intake of eicosapentaenoic acid and between 23.5 and 34.2% to the intake of docosapentaenoic acid; whereas docosahexaenoic acid contribution was marginal. Arachidonic acid from milk fat contributed between 10.5 and 18.8% to the human intake of n-6 FA.

Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer.

It has recently been shown that Fourier transform infrared spectroscopy has potential for the prediction of detailed milk fat composition, even based on a limited number of observations. Therefore, there seems to be an opportunity for improvement by means of using more observations. The objective of this study was to verify whether the use of more data would add to the accuracy of predicting milk fat composition. In addition, the effect of season on modeling was quantified because large differences in milk fat composition between winter and summer samples exist. We concluded that the use of 3,622 observations does increase predictability of milk fat composition based on infrared spectroscopy. However, for fatty acids with low concentrations, the use of many observations does not increase predictability to a level at which application of the model becomes obvious. Furthermore, the effect of season on validation r-square was limited but was occasionally large on prediction bias. For fatty acids that show large differences in level and standard deviation between winter and summer, a representative sample that includes observations collected in various seasons is critical for unbiased prediction. This research shows that all major fatty acids, combined groups of fatty acids, and the ratio of saturated to unsaturated fatty acids can be predicted accurately.

The influence of incubation on the formation of volatile bacterial metabolites in mastitis milk.

The possibility to detect mastitis-causing pathogens based on their volatile metabolites was previously studied. In that study, the mastitis samples were incubated overnight. To minimize the total analysis time, no incubation, or a short incubation, would be preferred. We therefore investigated the effect of the incubation time on the formation of volatile metabolites in mastitis samples. A selection of 6 volatile metabolites with the highest impact on the prediction model for identifying the mastitis-causing pathogen, was compared at different incubation times between 0 and 24 h. Identification of the pathogens was not possible without incubation. The minimum incubation time for detection of most of the 6 metabolites was 4 to 8 h. Although a longer incubation time increased the differences between pathogens, after 8 h all metabolites could be detected and the pathogens could be differentiated. Eight hours was therefore selected as the optimal incubation time. This optimal incubation time was evaluated with a set of 25 mastitis samples, of which 88% were correctly classified after 8 h of incubation. The total analysis time for this method is therefore considerably shorter than current microbiological culturing.

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 mastitis.

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 quarters.