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.
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Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
ISS and MR thanks to Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for granted fellowships (PQ, Process: 302204/2018-0; PV, Process: 350509/2020-4). GGM thanks to grant 2020/11463-5, São Paulo Research Foundation (FAPESP). EDP thanks for the postdoctoral opportunity at the State University of Londrina.
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This research received partial financial support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 (CDT) that authors gratefully acknowledge.
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Andressa Gomes de Almeida: investigation, methodology, roles/writing — original draft. Elis Daiane Pauli: software, writing — review and editing. Cláudia Domiciano Tormena: writing — review and editing. Ivar Wendling: resources, writing — review and editing. Miroslava Rakocevic: writing — review and editing. Roy Edward Bruns: writing — review and editing. Ieda Spacino Scarminio: funding acquisition, project administration, resources, supervision. Gustavo Galo Marcheafave: conceptualization, formal analysis, investigation, methodology, supervision, roles/writing — original draft.
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Andressa Gomes de Almeida declares no competing interests. Elis Daiane Pauli declares no competing interests. Cláudia Domiciano Tormena declares no competing interests. Ivar Wendling declares no competing interests. Miroslava Rakocevic declares no competing interests. Roy Edward Bruns declares no competing interests. Ieda Spacino Scarminio declares no competing interests. Gustavo Galo Marcheafave declares no competing interests.
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de Almeida, A.G., Pauli, E.D., Tormena, C.D. et al. Portable NIR Spectroscopy-Chemometric Identification of Chemically Differentiated Yerba Mate (Ilex paraguariensis) Clones. Food Anal. Methods 16, 469–477 (2023). https://doi.org/10.1007/s12161-022-02431-y
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DOI: https://doi.org/10.1007/s12161-022-02431-y