title: When machine tastes coffee: instrumental approach to predict the sensory profile of espresso coffee.
abstract: A robust and reproducible model was developed to predict the sensory profile of espresso coffee from instrumental headspace data. The model is derived from 11 different espresso coffees and validated using 8 additional espressos. The input of the model consists of (i) sensory profiles from a trained panel and (ii) on-line proton-transfer reaction mass spectrometry (PTR-MS) data. The experimental PTR-MS conditions were designed to simulate those for the sensory evaluation. Sixteen characteristic ion traces in the headspace were quantified by PTR-MS, requiring only 2 min of headspace measurement per espresso. The correlation is based on a knowledge-based standardization and normalization of both datasets that selectively extracts differences in the quality of samples, while reducing the impact of variations on the overall intensity of coffees. This work represents a significant progress in terms of correlation of sensory with instrumental results exemplified on coffee.
authors.affiliation: Nestlé Research Center, Vers-Chez-les-Blanc, 1000 Lausanne 26, Switzerland. christian.lindinger@rdls.nestle.com
authors.forename: Christian
authors.initials: C
authors.lastname: Lindinger
authors.forename: David
authors.initials: D
authors.lastname: Labbe
authors.forename: Philippe
authors.initials: P
authors.lastname: Pollien
authors.forename: Andreas
authors.initials: A
authors.lastname: Rytz
authors.forename: Marcel A
authors.initials: MA
authors.lastname: Juillerat
authors.forename: Chahan
authors.initials: C
authors.lastname: Yeretzian
authors.forename: Imre
authors.initials: I
authors.lastname: Blank