Aroma is a primary hedonic aspect of a good coffee. Coffee aroma quality is generally defined by cup tasting,which however is time- consuming in terms of panel training and alignment and too subjective. It is challenging to define a relationship between chemical profile and aroma sensory impact, but it might provide an objective evaluation of industrial products. This study aimed to define the chemical signature of coffee sensory notes, to develop prediction models based on analytical measurements for use at the control level. In particular, the sensory profile was linked with the chemical composition defined by HS-SPME-GC-MS, using a chemometric-driven approach. The strategy was found to be discriminative and informative, identifying aroma compounds characteristic of the selected sensory notes. The predictive ability in defining the sensory scores of each aroma note was used as a validation tool for the chemical signatures characterized. The most reliable models were those obtained for woody, bitter, and acidic properties, whose selected volatiles reliably represented the sensory note fingerprints. Prediction models could be exploited in quality control, but compromises must be determined if they are to become complementary to panel tasting.

Chemometric Modeling of Coffee Sensory Notes Through Their Chemical Signatures: Potential and Limits in Defining An Analytical Tool for Quality Control

Bressanello, Davide;Liberto, Erica
;
Cordero, Chiara Emilia Irma;Sgorbini, Barbara;Rubiolo, Patrizia;Ruosi, Manuela Rosanna;Bicchi, Carlo
2018

Abstract

Aroma is a primary hedonic aspect of a good coffee. Coffee aroma quality is generally defined by cup tasting,which however is time- consuming in terms of panel training and alignment and too subjective. It is challenging to define a relationship between chemical profile and aroma sensory impact, but it might provide an objective evaluation of industrial products. This study aimed to define the chemical signature of coffee sensory notes, to develop prediction models based on analytical measurements for use at the control level. In particular, the sensory profile was linked with the chemical composition defined by HS-SPME-GC-MS, using a chemometric-driven approach. The strategy was found to be discriminative and informative, identifying aroma compounds characteristic of the selected sensory notes. The predictive ability in defining the sensory scores of each aroma note was used as a validation tool for the chemical signatures characterized. The most reliable models were those obtained for woody, bitter, and acidic properties, whose selected volatiles reliably represented the sensory note fingerprints. Prediction models could be exploited in quality control, but compromises must be determined if they are to become complementary to panel tasting.
66
27
7096-7109
7109
http://pubs.acs.org/journal/jafcau
HS-SPME-GC-MS; chemometrics; coffee aroma; sensory note fingerprints; Chemistry (all); Agricultural and Biological Sciences (all)
Bressanello, Davide; Liberto, Erica; Cordero, Chiara Emilia Irma; Sgorbini, Barbara; Rubiolo, Patrizia; Pellegrino, Gloria; Ruosi, Manuela Rosanna; Bicchi, Carlo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1670782
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