Literature DB >> 25172720

Comprehensive metabolomics to evaluate the impact of industrial processing on the phytochemical composition of vegetable purees.

Patricia Lopez-Sanchez1, R C H de Vos2, H H Jonker2, R Mumm3, R D Hall2, L Bialek4, R Leenman4, K Strassburg5, R Vreeken5, T Hankemeier5, S Schumm4, J van Duynhoven6.   

Abstract

The effects of conventional industrial processing steps on global phytochemical composition of broccoli, tomato and carrot purees were investigated by using a range of complementary targeted and untargeted metabolomics approaches including LC-PDA for vitamins, (1)H NMR for polar metabolites, accurate mass LC-QTOF MS for semi-polar metabolites, LC-MRM for oxylipins, and headspace GC-MS for volatile compounds. An initial exploratory experiment indicated that the order of blending and thermal treatments had the highest impact on the phytochemicals in the purees. This blending-heating order effect was investigated in more depth by performing alternate blending-heating sequences in triplicate on the same batches of broccoli, tomato and carrot. For each vegetable and particularly in broccoli, a large proportion of the metabolites detected in the purees was significantly influenced by the blending-heating order, amongst which were potential health-related phytochemicals and flavour compounds like vitamins C and E, carotenoids, flavonoids, glucosinolates and oxylipins. Our metabolomics data indicates that during processing the activity of a series of endogenous plant enzymes, such as lipoxygenases, peroxidases and glycosidases, including myrosinase in broccoli, is key to the final metabolite composition and related quality of the purees.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Broccoli; Carrot; GC–MS; LC–MRM MS; LC–MS; Metabolomics; NMR; Processing; Puree; Tomato; Vitamins

Mesh:

Substances:

Year:  2014        PMID: 25172720     DOI: 10.1016/j.foodchem.2014.07.076

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


  9 in total

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7.  Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling.

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  9 in total

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