A Comparative Study Using UV-Vis, NIR, and FTIR Spectral Fingerprinting in Yerba Mate Leaves through Mixture Design Extractions and ASCA Models

Abstract

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.