Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Discrimination of Polish unifloral honeys using overall PTR-MS and HPLC fingerprints combined with chemometrics
Kus, P.M. ; Ruth, S.M. van - \ 2015
Food Science and Technology = Lebensmittel-Wissenschaft und Technologie 62 (2015)1. - ISSN 0023-6438 - p. 69 - 75.
reaction-mass spectrometry - origin determination - botanical origin - electronic nose - floral markers - l. honey - volatile - classification - identification - flavonoids
A total of 62 honey samples of six floral origins (rapeseed, lime, heather, cornflower, buckwheat and black locust) were analysed by means of proton transfer reaction mass spectrometry (PTR-MS) and HPLC-DAD. The data were evaluated by principal component analysis and k-nearest neighbours classification in order to examine consistent differences in analytical fingerprints between various honeys allowing their discrimination. The study revealed, that both techniques were able to distinguish the floral origins, however the HPLC shows advantage over PTR-MS providing substantially better differentiation of all analysed honey types. Especially HPLC fingerprints recorded at 210 nm were most suitable for discrimination of botanical origin with the use of chemometric analysis. The obtained classification rates were: 100%, 93%, 100%, 83%, 100%, 100% (HPLC) and 69%, 67%, 78%, 67%, 100%, 88% (PTR-MS) for rapeseed, lime, heather, cornflower, buckwheat and black locust, respectively. Even if performance of PTR-MS in general was lower than HPLC, it might be useful for fast on-line screening of buckwheat honey.
Rapid tomato volatile profiling by using proton-transfer reaction mass spectrometry (PTS-MS)
Farneti, B. ; Cristescu, S.M. ; Costa, G. ; Harren, F.J.M. ; Woltering, E.J. - \ 2012
Journal of Food Science 77 (2012)5. - ISSN 0022-1147 - p. C551 - C559.
electronic nose - lycopersicon-esculentum - quality attributes - organic-compounds - flavor compounds - aroma volatiles - kidney beans - shelf-life - cultivars - harvest
The availability of rapid and accurate methods to assess fruit flavor is of utmost importance to support quality control especially in the breeding phase. Breeders need more information and analytical tools to facilitate selection for complex multigenic traits such as flavor quality. In this study, it is shown that proton-transfer reaction mass spectrometry (PTR-MS) is a suitable method to monitor at high sensitivity the emission of volatiles determining the tomato aromatic profile such as hexanal, hexenals, methanol, ethanol, and acetaldehyde. The volatiles emitted by 14 tomato varieties (at red stage) were analyzed by 2 solvent-free headspace methods: solid-phase microextraction/gas chromatography MS and PTR-MS. Multivariate statistics (principal component analysis and cluster analysis) of the PTR-MS results allow an unambiguous separation between varieties, especially with a clear fingerprinting separation between the different tomato types: round truss, cocktail, and cherry tomatoes. PTR-MS was also successfully used to monitor the changes in volatile profiles during postharvest ripening and storage.
Authentication of feeding fats: Classification of animal fats, fish oils and recycled cooking oils
Ruth, S.M. van; Rozijn, M. ; Koot, A.H. ; Perez-Garcia, R. ; Kamp, H.J. van der; Codony, R. - \ 2010
Animal Feed Science and Technology 155 (2010)1. - ISSN 0377-8401 - p. 65 - 73.
reaction-mass-spectrometry - partial least-squares - trace gas-analysis - electronic nose - vegetable-oils - discrimination - spectroscopy - acids
Classification of fats and oils involves the recognition of one/several markers typical of the product. The ideal marker(s) should be specific to the fat or oil. Not many chemical markers fulfill these criteria. Authenticity assessment is a difficult task, which in most cases requires the measurement of several markers and must take into account natural and technology-induced variation. The present study focuses on the identity prediction of three by-products of the fat industry (animal fats, fish oils, recycled cooking oils), which may be used for animal feeding. Their identities were predicted by their triacylglycerol fingerprints, their fatty acid fingerprints and their profiles of volatile organic compounds. Partial least square discriminant analysis allowed samples to be assigned successfully into their identity classes. Most successful were triacylglycerol and fatty acid fingerprints (both 96% correct classification). Proton transfer reaction mass spectra of the volatile compounds predicted the identity of the fats in 92% of the samples correctly.
Detection of mastitis pathogens by analysis of volatile bacterial metabolites
Hettinga, K.A. ; Valenberg, H.J.F. van; Lam, T.J.G.M. ; Hooijdonk, A.C.M. van - \ 2008
Journal of Dairy Science 91 (2008). - ISSN 0022-0302 - p. 3834 - 3839.
electronic nose - staphylococcus-aureus - bovine mastitis - milk - culture - system - network - pattern - phase
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
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