title: Detection of mastitis pathogens by analysis of volatile bacterial metabolites.
abstract: 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.
authors.affiliation: Dairy Science and Technology group, Wageningen University and Research Centre, Wageningen, the Netherlands. kasper.hettinga@wur.nl
authors.forename: K A
authors.initials: KA
authors.lastname: Hettinga
authors.forename: H J F
authors.initials: HJ
authors.lastname: van Valenberg
authors.forename: T J G M
authors.initials: TJ
authors.lastname: Lam
authors.forename: A C M
authors.initials: AC
authors.lastname: van Hooijdonk
mesh_terms: D000818:Animals; D001419:Bacteria / Q000145:classification / Q000378:metabolism*; D001424:Bacterial Infections / Q000175:diagnosis / Q000382:microbiology / Q000662:veterinary*; D002417:Cattle; D005260:Female; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D008414:Mastitis, Bovine / Q000175:diagnosis* / Q000382:microbiology; D008892:Milk / Q000737:chemistry* / Q000145:classification / Q000166:cytology / Q000382:microbiology; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D055549:Volatile Organic Compounds / Q000737:chemistry*
chemical_list: D055549:Volatile Organic Compounds