The distinction between Washed and Natural coffee has a fundamental importance for coffee producers, the second process involving the removal of the fruit covering the beans before drying. Predictive models for NA, WA and R coffee typologies were elaborated for Green, Roasted and Brew coffee. Identification of coffee variety has gained increasing attention for the control of coffee adulteration. The scope of this study is to investigate the feasibility of non destructive FT-NIR approach for the discrimination of Robusta, Natural Arabica and Washed Arabica typologies for Green, Roasted and Brew coffee. NIR spectra were elaborated on the basis of spectral features and in association with chemometrics: predictive models were obtained using different classification methods. Best resulting models were validated in cv and test set validation.
Washed Arabica, Natural Arabica and Robusta coffee non-destructive discrimination using FT-NIR spectroscopy / Bertone, Elisa; Venturello, Alberto; Casiraghi, E.; Geobaldo, Francesco. - STAMPA. - (2013), pp. 29-29. (Intervento presentato al convegno Icnirs2013 - 16th International Conference on Near Infrared Spectroscopy tenutosi a La Grande-Motte, France nel 2 - 7 June 2013).
Washed Arabica, Natural Arabica and Robusta coffee non-destructive discrimination using FT-NIR spectroscopy
BERTONE, ELISA;VENTURELLO, ALBERTO;GEOBALDO, FRANCESCO
2013
Abstract
The distinction between Washed and Natural coffee has a fundamental importance for coffee producers, the second process involving the removal of the fruit covering the beans before drying. Predictive models for NA, WA and R coffee typologies were elaborated for Green, Roasted and Brew coffee. Identification of coffee variety has gained increasing attention for the control of coffee adulteration. The scope of this study is to investigate the feasibility of non destructive FT-NIR approach for the discrimination of Robusta, Natural Arabica and Washed Arabica typologies for Green, Roasted and Brew coffee. NIR spectra were elaborated on the basis of spectral features and in association with chemometrics: predictive models were obtained using different classification methods. Best resulting models were validated in cv and test set validation.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2513475
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