1H NMR Mixture Design-Fingerprints and ASCA Analysis in Ilex paraguariensis: Model Stability in Search of a Global Metabolome.

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.

Authentication of carioca common bean cultivars (Phaseolus vulgaris L.) using digital image processing and chemometric tools.

Bean authentication can result in higher quality products for commerce. Partial least squares discriminant analysis (PLS-DA) was applied to digital images in order to develop a methodology that allows the non-destructive discrimination of three Phaseolus vulgaris L. cultivars (Agro ANfc9, IPR-Andorinha, and IPR-Sabiá) having different technological characteristics. Principal component analysis resulted in a separation of these cultivars, but with a certain amount of overlap. Supervised analysis showed that three PLS1-DA models, each for two cultivars, was moderately better than the simultaneous treatment of all three cultivars (PLS2-DA). Permutation test evaluated statistical significance of PLS-DA models. The classification models were more accurate for Agro ANfc9 and IPR-Sabiá cultivars than for IPR-Andorinha. The Agro ANfc9-IPR-Sabiá model correctly classified 100% of the two bean classes in both training and test sets. This analytical strategy is fast, inexpensive, environmentally friendly, and can be applied for bean quality control helping cultivar authenticity for commerce.

Ecometabolic mixture design-fingerprints from exploratory multi-block data analysis in Coffea arabica beans from climate changes: Elevated carbon dioxide and reduced soil water availability.

Ecometabolic mixture design-fingerprinting in coffee cultivated under climate change was chemically explored using ComDim. Multi-blocks were formed using UV, NIRS, 1H NMR, SWV, and FT-IR data. ComDim investigated all these different fingerprints according to the extractor solvent and in virtue of atmospheric CO2 increase. Ethanol and ethanol-dichloromethane showed the best separations due to CO2 environment. 1H NMR loading indicate increases of fatty acids, caffeine, trigonelline, and glucose in beans under current CO2 levels, whereas quinic acid/chlorogenic acids, malic acid, and kahweol/cafestol increased in beans under elevated CO2 conditions. SWV indicated quercetin and chlorogenic acid as important compounds in coffee beans cultivated under current and elevated CO2, respectively. Based on the ethanol and ethanol-dichloromethane fingerprints, k-NN correctly classified the beans cultivated under different carbon dioxide environments and water availabilities, confirming the existence of metabolic changes due to climate changes. SWV proved to be promising compared with widely used spectrometric methods.

The main effects of elevated CO2 and soil-water deficiency on 1H NMR-based metabolic fingerprints of Coffea arabica beans by factorial and mixture design.

The metabolic response of Coffea arabica trees in the face of the rising atmospheric concentration of carbon dioxide (CO2) combined with the reduction in soil-water availability is complex due to the various (bio)chemical feedbacks. Modern analytical tools and the experimental advance of agronomic science tend to advance in the understanding of the metabolic complexity of plants. In this work, Coffea arabica trees were grown in a Free-Air Carbon Dioxide Enrichment dispositive under factorial design (22) conditions considering two CO2 levels and two soil-water availabilities. The 1H NMR mixture design-fingerprinting effects of CO2 and soil-water levels on beans were strategically investigated using the principal component analysis (PCA), analysis of variance (ANOVA) - simultaneous component analysis (ASCA) and partial least squares-discriminant analysis (PLS-DA). From the ASCA, the CO2 factor had a significant effect on changing the 1H NMR profile of fingerprints. The soil-water factor and interaction (CO2 × soil-water) were not significant. 1H NMR fingerprints with PCA, ASCA and PLS-DA analysis determined spectral profiles for fatty acids, caffeine, trigonelline and glucose increases in beans from current CO2, while quinic acid/chlorogenic acids, malic acid and kahweol/cafestol increased in coffee beans from elevated CO2. PLS-DA results revealed a good classification performance between the significant effect of the atmospheric CO2 levels on the fingerprints, regardless of the soil-water availabilities. Finally, the PLS-DA model showed good prediction ability, successfully classifying validation data-set of coffee beans collected over the vertical profile of the plants and included several fingerprints of different extracting solvents. The results of this investigation suggest that the association of experimental design, mixture design, PCA, ASCA and PLS-DA can provide accurate information on a series of metabolic changes provoked by climate changes in products of commercial importance, in addition to minimizing the extra work necessary in classic analytical approaches, encouraging the development of similar strategies.

Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis.

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.

PARAFAC HPLC-DAD metabolomic fingerprint investigation of reference and crossed coffees.

In this study two cultivars of Coffea arabica L., Bourbon (reference) and IPR101 (crossing) were analyzed. The extracts were prepared according to a simplex centroid design with four components, ethanol, ethyl acetate, dichloromethane, and hexane. Multiway data were obtained by HPLC-DAD analysis of the fifteen different mixtures for each cultivar. The PARAFAC methodology was used to investigate the chromatographic fingerprint. For both cultivars, Factor 1 was able to discriminate mixtures containing ethyl acetate as solvent. Factor 2 indicated that mixtures in pure ethanol and binary mixtures containing ethanol were the most efficient in extracting chlorogenic acids and factor 3 identified methylxanthines through spectrophotometric profile in all mixtures. Higher concentrations were obtained by the ethanol, dichloromethane and hexane ternary mixture for the Bourbon cultivar and by the quaternary mixture of these solvents with ethyl acetate for the IPR101 cultivar. Trigonelline and cafestol were extracted in both cultivars. The reference coffee showed higher relative abundances of cafestol ester, chlorogenic acids and trigonelline whereas the crossed coffee showed higher levels of caffeine. To confirm these results, UPLC-MS was used as a complementary method to confirm the presence of the metabolites in these extracts. The three way PARAFAC strategy determines correlations of HPLC-DAD chromatographic and spectral data simultaneously with samples permitting a more unambiguous assignment of metabolic groups than can be obtained treating chromatographic and spectral data separately by two way methods. This can provide higher quality chromatographic fingerprints for food chemistry.