Abstract
Near-infrared spectra were collected from green coffee and the partial least squares (PLS) regression method was used to develop multivariate models to estimate the sensory scores and content of total phenolic compounds in the coffee. The PLS models were elaborated from pretreated and original spectral data, presenting high correlation coefficients between the experimental and predicted values. Regarding sensory attributes, acidity, body, balance, flavor, aftertaste, fragrance, overall cup preference, and total specialty quality were evaluated. All sensory parameters showed satisfactory prediction results. However, the best prediction model was built for the body attribute, which presented a correlation of 0.92 in the cross-validation phase and 0.85 for the external validation. The highest correlation value for total phenolic compounds was 0.93 for cross-validation and 0.89 for external validation. Thus, the analysis by near infrared demonstrated to be applicable and to be an effective tool for sensorial and chemical analysis of arabica coffee.
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Acknowledgements
The authors thank the Instituto de Pesquisa, Assistência Técnica e Extensão Rural (INCAPER) for the donation of the samples.
Funding
This work was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES) – Finance Code 001, and Foundation for Support of Research and Innovation of Espírito Santo (FAPES).
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Cintia da Silva Araújo declares that she has no conflict of interest. Leandro Levate Macedo declares that he has no conflict interest. Wallaf Costa Vimercati declares that he has no conflict of interest. Sérgio Henriques Saraiva declares that he has no conflict of interest.
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da Silva Araújo, C., Macedo, L.L., Vimercati, W.C. et al. Spectroscopy Technique Applied to Estimate Sensory Parameters and Quantification of Total Phenolic Compounds in Coffee. Food Anal. Methods 14, 1943–1952 (2021). https://doi.org/10.1007/s12161-021-02025-0
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DOI: https://doi.org/10.1007/s12161-021-02025-0