RESUMO
The effects of experimental repetitions and solvent extractors on the 1H NMR fingerprinting of yerba mate extracts, obtained from two genders and two light environments, were analyzed in-depth by ANOVA-simultaneous component analysis (ASCA). Different solvents were used according to a mixture design based on ethanol, dichloromethane, and hexane and their combinations. The number of experimental repetitions significantly affected the ASCA results. Increasing repetitions led to decreases in the percentage effect variance values and an increase in the percentage residual variance. However, secondary sexual dimorphism, light availability, and their interaction effects became more significant with decreasing p-values at or above the 95% confidence level. The choice of a solvent extractor significantly affects the chemical profile and can lead to distinct conclusions regarding the significance of effect values. Pure solvents yielded different conclusions about the significance of factorial design effects, with each solvent extracting unique metabolites and maximizing information for specific effects. However, the use of binary solvent mixtures, such as ethanol-dichloromethane, proved more efficient in extracting sets of compounds that simultaneously differentiate between different experimental conditions. The mixture design-fingerprint strategy provided satisfactory results expanding the range of extracted metabolites with high percentage of residual variances and low explained percentage effect variances in the ASCA models. Ternary and even higher-ordered mixtures could be good alternative extracting media for work-intensive procedures. Our study underscores the significance of experimental design and solvent selection in metabolomic analysis, improving the accuracy, robustness, and interpretability of metabolomic models, leading to a better understanding of the chemical composition and biological implications of plant extracts.
Assuntos
Ilex paraguariensis , Ilex paraguariensis/química , Espectroscopia de Prótons por Ressonância Magnética , Cloreto de Metileno , Extratos Vegetais/química , Solventes/química , Etanol , MetabolomaRESUMO
The potencial of Coffea arabica leaves as bioindicators of atmospheric carbon dioxide (CO2) was evaluated in a free-air carbon dioxide enrichment (FACE) experiment by using near-infrared reflectance (NIR) spectroscopy for direct analysis and partial least squares discriminant analysis (PLS-DA). A supervised classification model was built and validated from the spectra of coffee leaves grown under elevated and current CO2 levels. PLS-DA allowed correct test set classification of 92% of the elevated-CO2 level leaves and 100% of the current-CO2 level leaves. The spectral bands accounting for the discrimination of the elevated-CO2 leaves were at 1657 and 1698 nm, as indicated by the variable importance in the projection (VIP) score together with the regression coefficients. Seven months after suspension of enriched CO2, returning to current-CO2 levels, new spectral measurements were made and subjected to PLS-DA analysis. The predictive model correctly classified all leaves as grown under current-CO2 levels. The fingerprints suggest that after suspension of elevated-CO2, the spectral changes observed previously disappeared. The recovery could be triggered by two reasons: the relief of the stress stimulus or the perception of a return of favorable conditions. In addition, the results demonstrate that NIR spectroscopy can provide a rapid, nondestructive, and environmentally friendly method for biomonitoring leaves suffering environmental modification. Finally, C. arabica leaves associated with NIR and mathematical models have the potential to become a good biomonitoring system.