Common roasting defects in coffee: Aroma composition, sensory characterization and consumer perception

By D. Giacalone, Tina Kreuzfeldt Degn, N. Yang, Chujiao Liu, Ian D Fisk, Morten Münchow. Food Quality and Preference, 2019

103 citations recorded by the source corpus.

Abstract The demand for high quality and specialty coffee is increasing worldwide. In order to meet these demands, a more uniform and standardized quality assessment of coffee is essential. The aim of this study was to make a sensory scientific and chemical characterization of common roasting defects in coffee, and to investigate their potential relevance for consumers’ acceptance of coffee. To this end, six time-temperature roasting profiles based on a single origin Arabica bean were developed: one ‘normal’, representing a reference coffee free of defects, and five common roast defects (‘dark’, ‘light’, ‘scorched’, ‘baked’ and ‘underdeveloped’. The coffee samples obtained from these beans were evaluated by means of (1) aroma analysis by Gas Chromatography-Mass Spectrometry (GC–MS), (2) sensory descriptive analysis (DA) by trained assessors, and (3) hedonic and sensory evaluation by consumers using a Check-All-That-Apply (CATA) questionnaire. Multivariate analyses of aroma, DA, and CATA data produced similar sample spaces, showing a clear opposition of the light roast to the dark and scorched roasts), with the normal roast having average values of key aroma compounds. The DA data confirmed this indications and showed the normal roast to have a balanced sensory profile compared to the other defects. Importantly, the normal roast was also significantly preferred in the consumer test ( N = 83 ), and significantly associated to positive CATA attributes ‘Harmonic’, ‘Pleasant’, and ‘Balanced’. Taken overall, the results provide a solid basis for understanding chemical and sensory markers associated with common roasting defects, which coffee professionals may use internally in both quality control and product development applications.

Subfields: Food Science, Pharmacology, Plant Science

View Paper: https://www.semanticscholar.org/paper/aa5359ccf75ab8b73614b9e6ae50660d84594377

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In Food Science

  1. Morten Münchow
  2. Jesper Alstrup
  3. D. Giacalone
  4. I. Steen

In Pharmacology

  1. Morten Münchow
  2. Jesper Alstrup
  3. D. Giacalone
  4. I. Steen

In Plant Science

  1. Morten Münchow
  2. D. Giacalone

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