Portable NIR Spectroscopy-Chemometric Identification of Chemically Differentiated Yerba Mate (Ilex paraguariensis) Clones

Author(s)

de Almeida, Andressa Gomes ; Pauli, Elis Daiane ; Tormena, Claudia Domiciano ; Wendling, Ivar ; Rakocevic, Miroslava ; Bruns, Roy Edward ; Scarminio, Ieda Spacino ; Marcheafave, Gustavo Galo

• Document type: Journal article
• Source: FOOD ANALYTICAL METHODS; v. N/A, p. 9-pg., 2022-12-07.

Abstract

Yerba mate plants are part of the ombrophilous mixed forest with araucaria trees. Genetically improved yerba mate clones are classified into two groups of interest to a selective consumer public, decaffeinated and high caffeine plants. Quality control strategies for this type of food are essential, and multivariate tools can help in this procedure. Partial least squares-discriminant analysis (PLS-DA) was used to identify chemically differentiated yerba mate plants together with reflectance measurement (900-1700 nm) near-infrared spectroscopy (NIRS) in direct analysis of plant material. Yerba mate plants were cultivated in the semi-hydroponic system under five plant shading levels (0%, 40%, 51%, 76%, and 82%). Robustness of the mathematical model was verified for plants with all these shading levels. The PLS-DA model showed a sensitivity of 96.52% for the training set and 93.33% for the test set. Specificity greater than 97.12% was found for both sets, with an efficiency rate of 96.82% for the training set and 95.31% for the test set. Wilcoxon signed classification, sign classification in pairs, and randomization t-tests showed an excellent model fit. Principal component analysis of the NIR spectra demonstrated that shading affected the chemical composition more in high-caffeine clones than in decaffeinated ones. This indicates that the caffeine synthesis in yerba mate plants represents an adaptative strategy to elevated light conditions. (AU)

• FAPESP's process: 20/11463-5 - Extractor solvent-detector optimization of plant responses to environmental impacts: an integrated factorial design, mixture design and ANOVA: simultaneous component analysis strategy
• Grantee: Gustavo Galo Marcheafave
• Support Opportunities: Scholarships in Brazil - Post-Doctoral