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Carbohydrate Polymers
Article . 2019 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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GC–MS characterisation of novel artichoke (Cynara scolymus) pectic-oligosaccharides mixtures by the application of machine learning algorithms and competitive fragmentation modelling

Authors: Sabater, Carlos; Olano, Agustín; Corzo, Nieves; Montilla, Antonia;

GC–MS characterisation of novel artichoke (Cynara scolymus) pectic-oligosaccharides mixtures by the application of machine learning algorithms and competitive fragmentation modelling

Abstract

Novel artichoke pectic-oligosaccharides (POS) mixtures have been obtained by enzymatic hydrolysis using four commercial enzyme preparations: Glucanex®200G, Pentopan®Mono-BG, Pectinex®Ultra-Olio and Cellulase from Aspergillus niger. Analysis by HPAEC-PAD showed that Cellulase from A. niger produced the greatest amount of POS (310.6 mg g-1 pectin), while the lowest amount was produced by Pentopan®Mono-BG (45.7 mg g-1 pectin). To determine structural differences depending on the origin of the enzyme, GC-MS spectra of di- and trisaccharides have been studied employing three machine learning algorithms: multilayer perceptron, random forest and boosted logistic regression. Machine learning models allowed characteristic m/z ions patterns to be established for each enzyme based on their GC-MS spectra with high prediction rates (above 95% on the test set). Possible chemical structures were given for some m/z ions having a decisive influence on these classifications. Finally, it was observed that several ions could be formed from specific POS structures.

Keywords

Enzymatic hydrolysis, Pectic-oligosaccharides, In silico fragmentation, Artichoke pectin, Neural networks, Neural network

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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