苏贝林
化学
蜡
角质
色谱法
气相色谱法
内胚层
气相色谱-质谱法
植物
有机化学
质谱法
生物化学
细胞壁
生物
作者
Dolors Company-Arumí,Mercè Figueras,Victòria Salvadó,Marisa Molinas,Olga Serra,Enriqueta Anticó
摘要
Introduction Protective plant lipophilic barriers such as suberin and cutin, with their associated waxes, are complex fatty acyl derived polyesters. Their precise chemical composition is valuable to understand the specific role of each compound to the physiological function of the barrier. Objectives To develop a method for the compositional analysis of suberin and associated waxes by gas chromatography (GC) coupled to ion trap-mass spectrometry (IT-MS) using N-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide (MTBSTFA) as sylilating reagent, and apply it to compare the suberin of the root and tuber periderm of potato (Solanum tuberosum). Methodology Waxes and suberin monomers from root and periderm were extracted subsequently using organic solvents and by methanolysis, and subjected to MTBSTFA derivatisation. GC analyses of periderm extracts were used to optimise the chromatographic method and the compound identification. Quantitative data was obtained using external calibration curves. The method was fully validated and applied for suberin composition analyses of roots and periderm. Results Wax and suberin compounds were successfully separated and compound identification was based on the specific (M-57) and non-specific ions in mass spectra. The use of calibration curves built with different external standards provided quantitative accurate data and showed that suberin from root contains shorter chained fatty acyl derivatives and a relative predominance of α,ω-alkanedioic acids compared to that of the periderm. Conclusion We present a method for the analysis of suberin and their associated waxes based on MTBSTFA derivatisation. Moreover, the characteristic root suberin composition may be the adaptive response to its specific regulation of permeability to water and gases. Copyright © 2016 John Wiley & Sons, Ltd.
科研通智能强力驱动
Strongly Powered by AbleSci AI