Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavour quality therefore requiring complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routinely quality control laboratory. Coffee flavour chemical data from 155 samples were obtained by analysing volatile (HS-SPME-GC-MS), and non-volatile (LC-UV/DAD) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scoresand investigate the networks between features. A comparison of the Q model parameter and RMSEP highlights the variable influence thatthe non-volatile fraction has on prediction, showing that it has a higher impact on describing Acid, Bitter and Woody notes than on Flowery and Fruity. The data fusion emphasised the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and non-volatile data deserves a thorough investigation to verify the potential of odour-taste integration

Coffee sensory properties: a complementary data fusion to simulate odour and taste integration by instrumental approach. Possibilities and limits

GIULIA STROCCHI;Erica Liberto
;
Carlo Bicchi
2021

Abstract

Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavour quality therefore requiring complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routinely quality control laboratory. Coffee flavour chemical data from 155 samples were obtained by analysing volatile (HS-SPME-GC-MS), and non-volatile (LC-UV/DAD) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scoresand investigate the networks between features. A comparison of the Q model parameter and RMSEP highlights the variable influence thatthe non-volatile fraction has on prediction, showing that it has a higher impact on describing Acid, Bitter and Woody notes than on Flowery and Fruity. The data fusion emphasised the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and non-volatile data deserves a thorough investigation to verify the potential of odour-taste integration
16th Weurman Flavour Research Symposium
Montpellier-Francia
4-6 Maggio 2021
E. Guichard and J.L. Le Quéré (Eds), Proceedings of the 16th Weurman Flavour Research Symposium (2021)
E.Guichard & J.L. Le Quéré (Eds)
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4
https://zenodo.org/record/5617710#.YcRgNWjMI2w
coffee, LC-UV, HS-SPME-GC-MS, chemometrics, sensory data
GIULIA STROCCHI, Erica Liberto, Gloria Pellegrino, Manuela R. Ruosi , Carlo Bicchi
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1827842
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