Abstract 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.

中文翻译：

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通过 Py-GC/MS 结合组学工具和多变量分析对仙人掌科刺的木质纤维素基质进行分类：一种化学分类学方法

摘要 热解与气相色谱和质谱联用 (Py-GC/MS) 是一种用于表征不同木质纤维素材料的快速分析技术。此外，化学信息学允许处理从 Py-GC/MS 获得的大量信息，用于对此类材料进行分类。在这里，我们通过 Py-GC/MS 表征了仙人掌科植物刺的木质纤维素基质；组学工具用于处理质谱以对物种进行分类并确定具有分类学价值的化学特征。从属于 15 种已知系统发育的仙人掌科的成熟个体收集新鲜刺样本。所有样品均通过超声波处理和顺序索氏提取进行清洁。通过 Py-GC/MS 分析研磨的样品，并对质谱图进行解卷积、鉴定、在多变量分析之前使用组学工具对齐和注释。共鉴定和排列了 451 种化合物：酮类、酯类、醇类、呋喃类和烃类是主要类别。呋喃、糠醛、丁烷和环氧乙烷是由碳水化合物产生的主要衍生物，而对羟基苯基 (H) 和愈创木酚 (G) 衍生物以及儿茶酚是仅有的由木质素产生的化合物。确定了不同物种集群共享的丰度模式，在属水平上获得了良好的分辨率。木质素衍生物在鉴定某些物种方面占优势，例如仙人掌科、Selenicereus-Deamia 和仙人掌属的物种。发现了 Disocactus 和 Echinocereus 物种的不一致聚类；环境影响和性格趋同被认为是可能的原因。