Characterization of sugar beet pectic-derived oligosaccharides obtained by enzymatic hydrolysis

Combo, Agnan Marie Michel; Aguedo, Mário; Quiévy, Nicolas; Danthine, Sabine; Goffin, Dorothée; Jacquet, Nicolas; Blecker, Christophe; Devaux, Jacques; Paquot, Michel

doi:10.1016/j.ijbiomac.2012.09.006

Cited by 88 publications

(28 citation statements)

References 35 publications

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“…(Combo, Aguedo, Goffin, Wathelet, & Paquot, 2012). POS mixtures can be formed of various types of oligomers and these structures are often characterised using chromatographic techniques such as HPAEC-PAD (Babbar, Dejonghe, Sforza, & Elst, 2017;Combo et al, 2013; n a a , and mass spectrometry (MS) (Leijdekkers, Huang, Bakx, Gruppen, & Schols, 2015;Ognyanov et al, 2016). MS allows structural patterns of complex molecules to be determined and has become the analytical method of choice in metabolomics research.…”

Section: Introductionmentioning

confidence: 99%

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

Sabater

Olano

Corzo

et al. 2019

Carbohydrate Polymers

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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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Section: Introductionmentioning

confidence: 99%

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

Sabater

Olano

Corzo

et al. 2019

Carbohydrate Polymers

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“…The review of literature has displayed a set of technologies encountered in POS' preparation through de-polymerization of pectin-containing materials via pre-treatments methods viz. enzymatic, acidic and hydrothermal methods[6–9]. In enzymatic method, pectinases and non-pectinases enzymes are employed to act synergistically for better extraction of POS [6,10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Non-Cumbersome Approach Towards Biosynthesis of Pectic-Oligosaccharides by Non-Aflatoxigenic Aspergillus sp. Section Flavi Strain EGY1 DSM 101520 through Citrus Pectin Fermentation

et al. 2016

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Pectic-Oligosaccharides (POS) have a growing potential in food and feed industries. To satisfy the demand of worldwide markets from POS and avoid the shortcomings of currently applied methodologies encountered in their preparation, the present study highlights a novel robust approach for POS biosynthesis. In the current approach, Aspergillus sp.section Flavi strain EGY1 DSM 101520 was grown on citrus pectin-based medium as a core POS production medium. POS' levels accumulated in the fungal fermentation broth were optimized through a three step sequential statistical mathematical methodology; Plackett-Burman design (PBD), Box-Behnken design (BBD) and canonical analysis. Three key determinants namely citrus pectin, peptone and NaH2PO4 were pointed out by PBD to impose significant consequences (P<0.05) on the process outcome (POS' levels). Optimal levels of these key determinants along with maximal of POS' levels were set by BBD and canonical analysis to be 2.28% (w/v) citrus pectin, 0.026% (w/v) peptone and 0.28% (w/v) NaH2PO4 to achieve a net amount of 1.3 g POS /2.28 g citrus pectin. Through this approach, a yield of 57% (w/w) POS of the total citrus pectin was obtained after 24 h of fungal growth on optimized citrus pectin–based medium. A fold enhancement of 13 times in POS' levels released in the fermentation fungal broth was realized by the end of the optimization strategy. This novel robust approach is considered a new insight towards POS biosynthesis via efficient, rapid and non-cumbersome procedure. To the best of authors' knowledge, the present work is the first article underlining detailed POS production from the fermentation broth of a fungus growing on citrus pectin-based medium.

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“…The hydrolysis of sugar beet pectin by combining endopolygalacturonase and pectinmethylesterase produced POs with a DP 1-9, with a maximum yield of trigalacturonic acid of 3.7% [140]. POs were obtained by the action of commercial enzymes on the potato rhamnogalacturonan, with a yield of 93.9 and 66.2% using Depol 670L and endo-β-1,4-galactanase, respectively.…”

Section: Pectinasesmentioning

confidence: 99%

Microbial Glycosidases for Nondigestible Oligosaccharides Production

Bezerra¹,

Montibra

Hansen

et al. 2017

Enzyme Inhibitors and Activators

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There is much interest in the study and production of nondigestible oligosaccharides (NDOs), due to their bioactivities and beneficial effects to the human health. The main approach in the production of NDOs relies on the action of glycosidases performing hydrolysis or transglycosylation of polysaccharides and sugars. In this chapter, a description of the main microbial glycosidases used for NDOs production, their sources, their principal properties, and a description of the production processes with the better results obtained are discussed.

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Characterization of sugar beet pectic-derived oligosaccharides obtained by enzymatic hydrolysis

Cited by 88 publications

References 35 publications

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

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

A Novel Non-Cumbersome Approach Towards Biosynthesis of Pectic-Oligosaccharides by Non-Aflatoxigenic Aspergillus sp. Section Flavi Strain EGY1 DSM 101520 through Citrus Pectin Fermentation

Microbial Glycosidases for Nondigestible Oligosaccharides Production

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