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Classification of lignocellulosic matrix of spines in Cactaceae by Py-GC/MS combined with omic tools and multivariate analysis: A chemotaxonomic approach.

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Title: Classification of lignocellulosic matrix of spines in Cactaceae by Py-GC/MS combined with omic tools and multivariate analysis: A chemotaxonomic approach.
Authors: Reyes-Rivera, Jorge1 (AUTHOR) coquio83@gmail.com, Solano, Eloy1 (AUTHOR), Terrazas, Teresa1,2 (AUTHOR) tterrazas@ib.unam.mx, Soto-Hernández, Marcos3 (AUTHOR), Arias, Salvador4 (AUTHOR), Almanza-Arjona, Yara C.5 (AUTHOR), Polindara-García, Luis A.6 (AUTHOR)
Source: Journal of Analytical & Applied Pyrolysis. Jun2020, Vol. 148, pN.PAG-N.PAG. 1p.
Subjects: Lignins, Multivariate analysis, Pyrolysis gas chromatography, Cactus, Mass spectrometry, Ethylene oxide, Spine
Abstract: • Lignocellulosics characterization by analytical fast-pyrolysis is a common technique. • Pyrolysis coupled to gas chromatography-mass spectrometry produces many information. • Cheminformatics allows deconvolute spectra and comparison by multivariate analyses. • Hierarchical clustering on principal component reduced datasets minimizes noise. • Different patterns allow samples classification and have chemotaxonomic application. Pyrolysis coupled to gas chromatography and mass spectrometry (Py-GC/MS) is a fast analytical technique used to characterize different lignocellulosic materials. Additionally, cheminformatics allows processing the vast amount of information obtained from Py-GC/MS for the classification of such materials. Here we characterized the lignocellulosic matrix of the spines in species of Cactaceae by Py-GC/MS; omic tools were used for processing the mass spectra in order to classify the species and identify chemical characters with taxonomic value. Fresh spines samples were collected from mature individuals belonging to 15 species of Cactaceae with known phylogenies. All samples were cleaned by ultrasonication and sequential Soxhlet extraction. Milled samples were analysed by Py-GC/MS and mass spectra were deconvoluted, identified, aligned and annotated using omic tools before the multivariate analysis. A total of 451 compounds were identified and aligned: ketones, esters, alcohols, furans and hydrocarbons were the main classes. Furan, furfural, butane and ethylene oxide were the main derivatives produced from carbohydrates, while the p -hydroxyphenyl (H) and guaiacyl (G) derivatives, as well as catechols, were the only compounds produced from lignin. Abundance patterns shared by different clusters of species were identified, obtaining a good resolution at the genus level. Lignin derivatives were preponderant for the identification of some groups of species, such as those of Cacteae, Selenicereus - Deamia and Opuntia. An incongruent clustering for Disocactus and Echinocereus species was found; environmental effects and characters convergence are evoked as the probable causes. A good taxonomic correspondence was obtained by combining the clustering and factorization analyses. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• Lignocellulosics characterization by analytical fast-pyrolysis is a common technique. • Pyrolysis coupled to gas chromatography-mass spectrometry produces many information. • Cheminformatics allows deconvolute spectra and comparison by multivariate analyses. • Hierarchical clustering on principal component reduced datasets minimizes noise. • Different patterns allow samples classification and have chemotaxonomic application. Pyrolysis coupled to gas chromatography and mass spectrometry (Py-GC/MS) is a fast analytical technique used to characterize different lignocellulosic materials. Additionally, cheminformatics allows processing the vast amount of information obtained from Py-GC/MS for the classification of such materials. Here we characterized the lignocellulosic matrix of the spines in species of Cactaceae by Py-GC/MS; omic tools were used for processing the mass spectra in order to classify the species and identify chemical characters with taxonomic value. Fresh spines samples were collected from mature individuals belonging to 15 species of Cactaceae with known phylogenies. All samples were cleaned by ultrasonication and sequential Soxhlet extraction. Milled samples were analysed by Py-GC/MS and mass spectra were deconvoluted, identified, aligned and annotated using omic tools before the multivariate analysis. A total of 451 compounds were identified and aligned: ketones, esters, alcohols, furans and hydrocarbons were the main classes. Furan, furfural, butane and ethylene oxide were the main derivatives produced from carbohydrates, while the p -hydroxyphenyl (H) and guaiacyl (G) derivatives, as well as catechols, were the only compounds produced from lignin. Abundance patterns shared by different clusters of species were identified, obtaining a good resolution at the genus level. Lignin derivatives were preponderant for the identification of some groups of species, such as those of Cacteae, Selenicereus - Deamia and Opuntia. An incongruent clustering for Disocactus and Echinocereus species was found; environmental effects and characters convergence are evoked as the probable causes. A good taxonomic correspondence was obtained by combining the clustering and factorization analyses. [ABSTRACT FROM AUTHOR]
ISSN:01652370
DOI:10.1016/j.jaap.2020.104796