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Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy.
J Sci Food Agric. 2021 Jun; 101(8):3500-3507.JS

Abstract

BACKGROUND

The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded.

RESULTS

The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively.

CONCLUSION

The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Levate Macedo L
Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil.
da Silva Araújo C
Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil.
Costa Vimercati W
Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil.
Gherardi Hein PR
Department of Forest Science, Federal University of Lavras, Lavras, Brazil.
Pimenta CJ
Department of Food Science, Federal University of Lavras, Lavras, Brazil.
Henriques Saraiva S
Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil.

MeSH

BrazilCoffeaCoffeeQuality ControlSeedsSpectroscopy, Near-Infrared

Pub Type(s)

Evaluation Study
Journal Article

Language

eng

PubMed ID

33274765

Citation

Levate Macedo, Leandro, et al. "Evaluation of Chemical Properties of Intact Green Coffee Beans Using Near-infrared Spectroscopy." Journal of the Science of Food and Agriculture, vol. 101, no. 8, 2021, pp. 3500-3507.
Levate Macedo L, da Silva Araújo C, Costa Vimercati W, et al. Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy. J Sci Food Agric. 2021;101(8):3500-3507.
Levate Macedo, L., da Silva Araújo, C., Costa Vimercati, W., Gherardi Hein, P. R., Pimenta, C. J., & Henriques Saraiva, S. (2021). Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy. Journal of the Science of Food and Agriculture, 101(8), 3500-3507. https://doi.org/10.1002/jsfa.10981
Levate Macedo L, et al. Evaluation of Chemical Properties of Intact Green Coffee Beans Using Near-infrared Spectroscopy. J Sci Food Agric. 2021;101(8):3500-3507. PubMed PMID: 33274765.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy. AU - Levate Macedo,Leandro, AU - da Silva Araújo,Cintia, AU - Costa Vimercati,Wallaf, AU - Gherardi Hein,Paulo Ricardo, AU - Pimenta,Carlos José, AU - Henriques Saraiva,Sérgio, Y1 - 2020/12/20/ PY - 2020/11/20/revised PY - 2020/05/17/received PY - 2020/12/04/accepted PY - 2020/12/5/pubmed PY - 2021/7/17/medline PY - 2020/12/4/entrez KW - green coffee KW - multivariate regression KW - near infrared KW - predictive models KW - preprocessing SP - 3500 EP - 3507 JF - Journal of the science of food and agriculture JO - J Sci Food Agric VL - 101 IS - 8 N2 - BACKGROUND: The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded. RESULTS: The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively. CONCLUSION: The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry. SN - 1097-0010 UR - https://www.unboundmedicine.com/medline/citation/33274765/Evaluation_of_chemical_properties_of_intact_green_coffee_beans_using_near_infrared_spectroscopy_ DB - PRIME DP - Unbound Medicine ER -