Analysis of variance (ANOVA)-simultaneous component analysis (ASCA) is a method of choice for factorial design studies of environmental impacts on plant metabolomes and can be used to quantitatively carry out this comparative analysis. The impacts of seven mixture design extractor systems made up of ethanol, dichloromethane, and hexane and their 1:1 binary and 1:1:1 ternary solvents for several replicate experiments (n = 3, 4, 5) were assessed using ASCA models determined from Fourier-transform infrared (FTIR), near-infrared (NIR), and UV-Vis spectroscopic measurements on yerba mate leaf extracts. This analysis considered two-factor effects: secondary sexual dimorphism (male and female plants) and cultivation systems (monoculture and agroforestry), as well as their interaction effect. The three binary solvents were found to be more efficient extractor systems for all four detectors as they found 83 main and interaction effects significant at or above the 95% confidence level compared with only 47 for the pure solvent extracts. Binary solvent extracts resulted in averages between 44.04 and 86.61% for the ASCA total effect variances compared with 40.62 to 71.07% for pure extractors. Of the 60 significant effects found for experiments with 5 repetitions 53 or 88% were obtained with only triplicate determinations. The choice of spectroscopic technique and solvent system have large impacts on metabolomic analysis results. ANOVAsimultaneous ANOVA simultaneous (ASCA ethanol dichloromethane 11 1 1: 111 1:1: n 3 4 Fouriertransform Fourier transform FTIR, FTIR , (FTIR) nearinfrared near NIR, NIR (NIR) UVVis UV Vis twofactor two factor male plants monoculture agroforestry, agroforestry agroforestry) 8 95 4404 44 04 44.0 8661 86 61 86.61 4062 40 62 40.6 7107 71 07 71.07 extractors 6 88 determinations results (FTIR (NIR 9 440 0 44. 866 86.6 406 40. 710 7 71.0 86. 71.

Biodiesel contains esters in its composition which make it susceptible to oxidation. Among the factors that cause this reaction is the contamination with metal ions. In the present work, it was analyzed biodiesel samples obtained from a mixture containing 39.0% m/m soybean oil, 22.0% m/m beef tallow and 39.0% m/m poultry fat using a methyl route. Copper ions were added to the biodiesel to evaluate its catalytic action in the oxidation reaction, in the presence and absence of rosemary extract. During 192 h of assay, samples containing copper, without extract, showed shorter induction periods, higher rate constants and lower activation energy, when compared to samples containing extract, at the same assay temperature. Copper ions presented a strong catalytic action causing linearity deviations showing a super-Arrhenius behavior, while rosemary extract delayed the oxidative process and showed no linearity deviations.

The effects of water-deficit stress on irrigated and unirrigated field plants of Coffea arabica L. genotype IAPAR 59 were investigated. Plant extracts were obtained following an ethanol-dichloromethane-hexane statistical mixture design. Proton nuclear magnetic resonance (1H NMR) fingerprints of the extracts were discriminated using factor analysis (FA) and hierarchical clustering techniques. Extracts from the 1:1:1 ternary mixture presented the largest discriminations compared with those from the pure solvents or their 1:1 binary mixtures. Metabolites resulting from fermentation processes and nutritional deficiencies as well as senescence and abscission precursors such as lactate, arginine and methionine were prevalent in unirrigated plants that can provoke expressive decreases in bean productivity as well as premature plant aging. Amino acids that control regulatory, physiological processes and soil salinization have higher concentrations in the irrigated plants. The NMR assignments of eighteen substances observed here were confirmed by electrospray ionization mass spectrometry.

Rollinia mucosa fruit has generated great interest due to the presence of bioactive compounds, which exhibit promising biological activities. Fingerprinting analytical techniques, chromatographic-diode array detection (DAD), infrared, mass spectrometric, 1H and 13C nuclear magnetic resonance (NMR) spectra, associated with chemometric analysis and mixture design, were used to determine chemical discriminations of sun-exposed and self-shaded leaves of Rollinia mucosa in each of the four seasons. Sunlit leaves in winter presented higher metabolite signals related to the acetogenins, whereas shaded leaves have higher abundances of carbohydrates and terpenes. Sunlit leaves harvested in the summer had the smallest metabolite abundances. Fingerprints confirmed the presence of two acetogenins, annonacin A and annonastatin. Ternary and quaternary mixtures of the statistical mixture design were most effective for revealing important discriminations.

Ultraviolet characteristic profiles and infrared spectroscopic (FTIR) fingerprints of green bean extracts of Coffea arabica L., cv. IAPAR 59, cultivated in two planting patterns, rectangular and square, and at two different densities, 10,000 and 6,000 plants ha-1, identified as R10, R6, S10, and S6 were analyzed with principal component and hierarchical cluster analyses. A simplex centroid design for four solvents (ethanol, acetone, dichloromethane, hexane) was used for sample extraction. The largest chlorogenic acid (CGA) contents were found at the lower planting density. The dichloromethane extracts of the S10 treatment showed the highest levels of unsaponifiable lipids (cafestol and kahweol). The R6 treatment showed a slightly higher content of cafestol and kahweol. Cluster analysis of FTIR fingerprints confirmed that the CGA and caffeine levels differentiate the spatial arrangements. The FTIR fingerprints suggest that green beans from S6 and R10 were richer in lipids and the other two treatments had more sugars and proteins.