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Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds
Published on: November 12, 2016
Sequential mixture design optimization for divergent metabolite analysis: Enriched carbon dioxide effects on Coffea
Cláudia D Tormena1, Gustavo G Marcheafave1, Miroslava Rakocevic2
1Laboratório de Quimiometria em Ciências Naturais, Departamento de Química, Universidade Estadual de Londrina, CP 6001, 86051-990 Londrina, PR, Brazil.
Abstract:
The first metabolic study of the impact of elevated CO2 (590 µL CO2 L-1) levels on the leaves and buds of Coffea arabica L. plants is reported. A novel sequential statistical mixture design strategy allowed optimization of both the extraction and mobile phase solvent systems to increase differences detected in metabolites of Coffea arabica L. plants and buds. Factor analysis showed that the 227 and 273 nm bands of the 1:1:1 ternary ethyl ether - dichloromethane - methanol mixture spectra resulted in discrimination of elevated CO2 extract samples from those obtained from leaves grown in a current level CO2 atmosphere (390 µL CO2 L-1) of leaf sample extracts. DAD-HPLC spectral peak evidence showed a 32% increase in absorbance of the 273 band for the enriched CO2 leaf extracts. This band has been assigned to caffeine-like substances and confirmed by the mass spectral signal at m/z 195 ([M + H]+). No enrichment band increases were found for kahweol, kaempferol and quercetin that had presence confirmed by mass spectral analysis. No epigenetic effect of this metabolic profile was found in new leaves after the addition of CO2 stopped. Enriched CO2 perturbation of the bud metabolite were much smaller than for the leaf samples. Absorbance increases in the 228 nm and decreases in the 235 nm bands play a prominent role in the discrimination of enriched CO2 buds from the controls in the pure dichloromethane extracting solvent. This global metabolome strategy allows the monitoring of chemical groups of plants susceptible to environmental changes as well as elucidate metabolic variations in complex matrices of biochemical responses.
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