Literature DB >> 26190596

Origin identification of dried distillers grains with solubles using attenuated total reflection Fourier transform mid-infrared spectroscopy after in situ oil extraction.

Ph Vermeulen1, J A Fernández Pierna2, O Abbas2, P Dardenne2, V Baeten2.   

Abstract

The ban on using processed animal proteins in feedstuffs led the feed sector to look for other sources of protein. Dried distillers grains with solubles (DDGS) could be considered as an important source in this regard. They are imported into Europe mainly for livestock feed. Identifying their origin is essential when labelling is missing and for feed safety, particularly in a crisis situation resulting from contamination. This study investigated applying attenuated total reflection Fourier transform mid-infrared spectroscopy (ATR-FT-MIR) to the oil fraction extracted from samples in situ in order to identify the origin of DDGS. The use of spectroscopic and chemometric tools enabled the botanical and geographical origins of DDGS, as well as the industrial process used to produce them, to be identified. The models developed during the study provided a classification higher than 95% using an external validation set.
Copyright © 2014. Published by Elsevier Ltd.

Entities:  

Keywords:  ATR-FT-MIR; Authentication; DDGS; Feed; In situ extraction

Mesh:

Substances:

Year:  2014        PMID: 26190596     DOI: 10.1016/j.foodchem.2014.09.103

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  4 in total

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Journal:  PLoS One       Date:  2017-04-03       Impact factor: 3.240

2.  Multispectral Fluorescence Imaging Technique for On-Line Inspection of Fecal Residues on Poultry Carcasses.

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Review 3.  Geographical Origin Authentication of Agri-Food Products: Α Review.

Authors:  Katerina Katerinopoulou; Achilleas Kontogeorgos; Constantinos E Salmas; Angelos Patakas; Athanasios Ladavos
Journal:  Foods       Date:  2020-04-13

4.  Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms.

Authors:  Hongyan Zhu; Jun-Li Xu
Journal:  Molecules       Date:  2020-10-28       Impact factor: 4.411

  4 in total

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