|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||When machine tastes coffee : an instrumental approach to predict the sensory profile of espresso coffee|
Juillerat, Marcel A.
|Published in:||Analytical Chemistry|
|Publisher / Ed. Institution:||American Chemical Society|
|Publisher / Ed. Institution:||Columbus|
|Subjects:||Proton-transfer-reaction mass-spectrometry (ptr-ms); Sensory; Coffee; Analytical|
|Subject (DDC):||663: Beverage technology|
|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) online 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.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||Life Sciences and Facility Management|
|Organisational Unit:||Institute of Chemistry and Biotechnology (ICBT)|
|Appears in collections:||Publikationen Life Sciences und Facility Management|
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