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Determination of pH and acidity in green coffee using near-infrared spectroscopy and multivariate regression.
J Sci Food Agric. 2020 Apr; 100(6):2488-2493.JS

Abstract

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.

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Publisher Full Text (DOI)

Authors+Show Affiliations

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.

MeSH

CalibrationCoffeaHydrogen-Ion ConcentrationLeast-Squares AnalysisSeedsSpectroscopy, Near-Infrared

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31960433

Citation

Araújo, Cintia da Silva, et al. "Determination of pH and Acidity in Green Coffee Using Near-infrared Spectroscopy and Multivariate Regression." Journal of the Science of Food and Agriculture, vol. 100, no. 6, 2020, pp. 2488-2493.
Araújo CDS, Macedo LL, Vimercati WC, et al. Determination of pH and acidity in green coffee using near-infrared spectroscopy and multivariate regression. J Sci Food Agric. 2020;100(6):2488-2493.
Araújo, C. D. S., Macedo, L. L., Vimercati, W. C., Ferreira, A., Prezotti, L. C., & Saraiva, S. H. (2020). Determination of pH and acidity in green coffee using near-infrared spectroscopy and multivariate regression. Journal of the Science of Food and Agriculture, 100(6), 2488-2493. https://doi.org/10.1002/jsfa.10270
Araújo CDS, et al. Determination of pH and Acidity in Green Coffee Using Near-infrared Spectroscopy and Multivariate Regression. J Sci Food Agric. 2020;100(6):2488-2493. PubMed PMID: 31960433.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Determination of pH and acidity in green coffee using near-infrared spectroscopy and multivariate regression. AU - Araújo,Cintia da Silva, AU - Macedo,Leandro Levate, AU - Vimercati,Wallaf Costa, AU - Ferreira,Adésio, AU - Prezotti,Luiz Carlos, AU - Saraiva,Sérgio Henriques, Y1 - 2020/02/12/ PY - 2019/10/09/received PY - 2020/01/08/revised PY - 2020/01/21/accepted PY - 2020/1/22/pubmed PY - 2020/11/20/medline PY - 2020/1/22/entrez KW - PLS regression KW - multivariate calibration KW - raw coffee KW - spectroscopy SP - 2488 EP - 2493 JF - Journal of the science of food and agriculture JO - J Sci Food Agric VL - 100 IS - 6 N2 - 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. SN - 1097-0010 UR - https://www.unboundmedicine.com/medline/citation/31960433/Determination_of_pH_and_acidity_in_green_coffee_using_near_infrared_spectroscopy_and_multivariate_regression_ DB - PRIME DP - Unbound Medicine ER -