Determination of pH and acidity in green coffee using near-infrared spectroscopy and multivariate regression.

Autor: Araújo CDS; Postgraduate Program in Food Science and Technology, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil., Macedo LL; Postgraduate Program in Food Science and Technology, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil., Vimercati WC; Postgraduate Program in Food Science and Technology, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil., Ferreira A; Department of Agronomy, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil., Prezotti LC; Capixaba Institute of Research, Technical Assistance and Rural Extension, Vitória, Brazil., Saraiva SH; Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil.
Jazyk: angličtina
Zdroj: Journal of the science of food and agriculture [J Sci Food Agric] 2020 Apr; Vol. 100 (6), pp. 2488-2493. Date of Electronic Publication: 2020 Feb 12.
DOI: 10.1002/jsfa.10270
Abstrakt: Background: Coffee is a raw material of global interest. Due to its relevance, this work evaluated the performance of calibration models constructed from spectral data obtained using near-infrared spectroscopy (FT-NIR) to determine the pH values and acidity in coffee beans in a practical and non-destructive way. Partial least squares regression was used during the calibration and the cross-validation to optimize the number of latent variables. The predictive capacity of the spectral pre-processing methods was also accessed.
Results: The results obtained showed that the best methods of pre-processing were the first derivative for the pH variable and the standard normal variate for the acidity, which produced models with correlations of 0.78 and 0.92, ratios of prediction to deviation of 2.061 and 2.966 and biases of -0.00011 and -0.152 to test set validation, respectively. The average errors between predicted and experimental values were lower than 7%.
Conclusions: FT-NIR was successfully applied to predict properties related to the quality of coffee. The method was demonstrated to be a fast and non-destructive tool which allows the rapid inline evaluation of samples facilitating industrial and commercial processing. © 2020 Society of Chemical Industry.
(© 2020 Society of Chemical Industry.)
Databáze: MEDLINE
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