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

Inequality relations for NMR-based polymer homoblock analysis and extended application: Reanalysis of historical data on alginates, chitosans, homogalacturonans, and galactomannans

There has been a long-standing bottleneck in the quantitative analysis of the frequencies of homoblock polyads beyond triads using 1H and 13C NMR for linear polysaccharides, primarily because monosaccharides within a long homoblock share similar chemical environments due to identical neighboring units, resulting in indistinct NMR peaks. In this study, through rigorous mathematical induction, inequality relations were established that enabled the calculation of frequency ranges of homoblock polyads from historically reported NMR-derived frequency values of diads and/or triads of alginates, chitosans, homogalacturonans, and galactomannans. The calculated homoblock frequency ranges were then applied to evaluate three chain growth statistical models, including the Bernoulli chain, first-order Markov chain, and second-order Markov chain, for predicting homoblock frequencies in these polysaccharides. Furthermore, based on the mathematically derived inequality relations, a novel 2D array was constructed, enabling the graphical visualization of homoblock features in polysaccharides. It was demonstrated, as a proof of concept, that the novel 2D array, along with a 1D code generated from it, could serve as an effective feature engineering tool for polymer classification using machine learning algorithms.

GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments

GCMS-ID (Gas Chromatography Mass Spectrometry compound IDentifier) is a webserver designed to enable the identification of compounds from GC-MS experiments. GC-MS instruments produce both electron impact mass spectra (EI-MS) and retention index (RI) data for as few as one, to as many as hundreds of different compounds. Matching the measured EI-MS, RI or EI-MS + RI data to experimentally collected EI-MS and/or RI reference libraries allows facile compound identification. However, the number of available experimental RI and EI-MS reference spectra, especially for metabolomics or exposomics-related studies, is disappointingly small. Using machine learning to accurately predict the EI-MS spectra and/or RIs for millions of metabolomics and/or exposomics-relevant compounds could (partially) solve this spectral matching problem. This computational approach to compound identification is called in silico metabolomics. GCMS-ID brings this concept of in silico metabolomics closer to reality by intelligently integrating two of our previously published webservers: CFM-EI and RIpred. CFM-EI is an EI-MS spectral prediction webserver, and RIpred is a Kovats RI prediction webserver. We have found that GCMS-ID can accurately identify compounds from experimental RI, EI-MS or RI + EI-MS data through matching to its own large library of >1 million predicted RI/EI-MS values generated for metabolomics/exposomics-relevant compounds. GCMS-ID can also predict the RI or EI-MS spectrum from a user-submitted structure or annotate a user-submitted EI-MS spectrum. GCMS-ID is freely available at https://gcms-id.ca/.

A method for quantitative characterization of incomplete degradation products of polygalacturonic acid

Biological activity of incomplete degradation products of polygalacturonic acid (IDPP) is closely related to its molecular weight and molecular weight distribution. Therefore, it is necessary to provide a reliable quantitative characterization method for evaluating these types of bioproducts. A novel method was established in this work for the quantitative characterization of IDPP based upon ethanol fractional precipitation. IDPP was fractionated into several fractions with high recovery (>95%), and the average molecular weights of each fraction was in descending order with the increase of ethanol concentration. Oligosaccharides (polymerization degree: 2-20) could be effectively harvested from the polygalacturonic acid enzymatic hydrolysate by ethanol precipitation. Moreover, the developed method had good repeatability and could also be applied to quantify enzymatic hydrolysis products of citrus-derived pectin polysaccharides. In conclusion, this paper provides a simple, accurate method for the quantitative characterization of IDPP and a strategy for the extraction of oligosaccharides.

Application of Artificial Intelligence in Food Industry—a Guideline

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

Artichoke pectic oligosaccharide characterisation and virtual screening of prebiotic properties using in silico colonic fermentation

The aim of this work was to develop a comprehensive workflow to elucidate molecular features of artichoke pectic oligosaccharides (POS) contributing to high potential prebiotic activity. First, obtainment of artichoke POS by Pectinex® Ultra-Olio was optimised using an artificial neural network. Under optimal conditions (pH 6.86; 1.5 h; enzyme dose 520.5 U/g pectin) POS yield was 624 mg/g pectin. Oligosaccharide structures (M_w < 1.3 kDa) were characterised by MALDI-TOF-MS. Then, conformational analysis of glycosidic bonds was performed by replica exchange molecular dynamics simulations and interaction mechanisms between POS and several microbial glycosidases were proposed by molecular modelling. Chemical information was integrated in virtual simulations of colonic fermentation. Highest hydrolysis rate was obtained for GalA-Rha-GalA trisaccharide, while the presence of partial negative charges and high radius of gyration enhance short chain fatty acid formation in distal colon. Established structure-activity relationships could help the rational design of prebiotics and clinical trials.

Intestinal anti-inflammatory effects of artichoke pectin and modified pectin fractions in the dextran sulfate sodium model of mice colitis. Artificial neural network modelling of inflammatory markers

Anti-inflammatory properties of artichoke pectin and modified fractions (arabinose- and galactose-free) used at two doses (40 and 80 mg kg-1) in mice with colitis induced by dextran sulfate sodium have been investigated. Expression of pro-inflammatory markers TNF-α and ICAM-I decreased in groups of mice treated with original and arabinose-free artichoke pectin while IL-1β and IL-6 liberation was reduced only in mice groups treated with original artichoke pectin. A decrease in iNOS and TLR-4 expression was observed for most treatments. Intestinal barrier gene expression was also determined. MUC-1 and Occludin increased in groups treated with original artichoke pectin while MUC-3 expression also increased in arabinose-free pectin treatment. Galactose elimination led to a loss of pectin bioactivity. Characteristic expression profiles were established for each treatment through artificial neural networks showing high accuracy rates (≥90%). These results highlight the potential amelioration of inflammatory bowel disease on mice model colitis through artichoke pectin administration.

Production of Oligosaccharides from Agrofood Wastes

The development of biorefinery processes to platform chemicals for most lignocellulosic substrates, results in side processes to intermediates such as oligosaccharides. Agrofood wastes are most amenable to produce such intermediates, in particular, cellooligo-saccharides (COS), pectooligosaccharides (POS), xylooligosaccharides (XOS) and other less abundant oligomers containing mannose, arabinose, galactose and several sugar acids. These compounds show a remarkable bioactivity as prebiotics, elicitors in plants, food complements, healthy coadyuvants in certain therapies and more. They are medium to high added-value compounds with an increasing impact in the pharmaceutical, nutraceutical, cosmetic and food industries. This review is focused on the main production processes: autohydrolysis, acid and basic catalysis and enzymatic saccharification. Autohydrolysis of food residues at 160–190 °C leads to oligomer yields in the 0.06–0.3 g/g dry solid range, while acid hydrolysis of pectin (80–120 °C) or cellulose (45–180 °C) yields up to 0.7 g/g dry polymer. Enzymatic hydrolysis at 40–50 °C of pure polysaccharides results in 0.06–0.35 g/g dry solid (DS), with values in the range 0.08–0.2 g/g DS for original food residues.

MASS SPECTROMETRIC FRAGMENTATION OF TRIMETHYLSILYL AND RELATED ALKYLSILYL DERIVATIVES

This review describes the mass spectral fragmentation of trimethylsilyl (TMS) and related alkylsilyl derivatives used for preparing samples for analysis, mainly by combined gas chromatography and mass spectrometry (GC/MS). The review is divided into three sections. The first section is concerned with the TMS derivatives themselves and describes fragmentation of derivatized alcohols, thiols, amines, ketones, carboxylic acids and bifunctional compounds such as hydroxy- and amino-acids, halo acids and hydroxy ethers. More complex compounds such as glycerides, sphingolipids, carbohydrates, organic phosphates, phosphonates, steroids, vitamin D, cannabinoids, and prostaglandins are discussed next. The second section describes intermolecular reactions of siliconium ions such as the TMS cation and the third section discusses other alkylsilyl derivatives. Among these latter compounds are di- and trialkyl-silyl derivatives, various substituted-alkyldimethylsilyl derivatives such as the tert-butyldimethylsilyl ethers, cyclic silyl derivatives, alkoxysilyl derivatives, and 3-pyridylmethyldimethylsilyl esters used for double bond location in fatty acid spectra. © 2019 Wiley Periodicals, Inc. Mass Spec Rev 0000:1-107, 2019.

Enzymatic Production and Characterization of Pectic Oligosaccharides Derived from Citrus and Apple Pectins: A GC-MS Study Using Random Forests and Association Rule Learning

Pectic oligosaccharides (POS) from citrus and apple pectin hydrolysis using ViscozymeL and Glucanex200G have been obtained. According to the results, maximum POS formation was achieved from citrus pectin after 30 min of hydrolysis with ViscozymeL, with a yield of 652 mg g^-1 and average molecular mass ( M_w) of 0.8-2.5 kDa, while with Glucanex200G, the yield was 518 mg g^-1 and M_w was 0.8-7.1 kDa. Digalacturonic and trigalacturonic acids were identified among other low M_w compounds as di- and tri-POS. In addition, differences in GC-MS spectra of all oligosaccharides found in the hydrolysates were studied by employing random forests and other algorithms to identify structural differences between the obtained POS, and high prediction rates were shown for new samples. Chemical structures were proposed for some influential m/ z ions, and 12 association rules that explain differences according to pectin and enzyme origin were built. This information could be used to establish structure-function relationships of POS.