GC–MS characterisation of novel artichoke (Cynara scolymus) pectic-oligosaccharides mixtures by the application of machine learning algorithms and competitive fragmentation modelling
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.
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Elsevier
2019
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| Subjects: | Artichoke pectin, Enzymatic hydrolysis, Pectic-oligosaccharides, Neural network, In silico fragmentation, |
| Online Access: | http://hdl.handle.net/10261/174150 http://dx.doi.org/10.13039/501100003176 http://dx.doi.org/10.13039/501100004837 |
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| Summary: | 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. |
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