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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Record number 386775
Title Authentication of feeding fats: Classification of animal fats, fish oils and recycled cooking oils
Author(s) Ruth, S.M. van; Rozijn, M.; Koot, A.H.; Perez-Garcia, R.; Kamp, H.J. van der; Codony, R.
Source Animal Feed Science and Technology 155 (2010)1. - ISSN 0377-8401 - p. 65 - 73.
DOI https://doi.org/10.1016/j.anifeedsci.2009.09.016
Department(s) BU Microbiological & Chemical Food Analysis
Publication type Refereed Article in a scientific journal
Publication year 2010
Keyword(s) reaction-mass-spectrometry - partial least-squares - trace gas-analysis - electronic nose - vegetable-oils - discrimination - spectroscopy - acids
Abstract 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.
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