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.This work has been funded by MICINN of Spain, Projects AGL2014-53445-R and AGL2017-84614-C2-1-R and by the Spanish Danone Institute. Carlos Sabater thanks his FPU Predoc contract from Spanish MECD (FPU14/03619).Peer reviewe