Sensory Evaluation of Character Impact Components in an Apple ModelMixture

• Published in: Chemical senses Vol. 27; no. 6; pp. 485 - 494
• Main Authors: Bult, Johannes H.F., Schifferstein, Hendrick N.J., Roozen, Jacques P., Boronat, Estanislau Dalmau, Voragen, Alphons G.J., Kroeze, Jan H.A.
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.07.2002
• ISSN: 0379-864X; 1464-3553
• DOI: 10.1093/chemse/27.6.485

Food aromas generally are complex mixtures of volatiles. In the present study, we investigated the joint effects of hexyl acetate, trans-2-hexenal and 1-hexanol on the multi-attribute perception of an apple aroma. The first two substances were identified earlier as positive contributors to the apple aroma (high character impact), whereas the third component was identified as an irrelevant or negative contributor (low character impact). Aroma quality was quantified using a set of eight graphic rating scales. All three components had significant effects on the aroma profiles. These effects consist mainly of an effect of each component on the attribute that described its individual character and an effect of all three components on ratings on the main character attribute `apple'. As expected, the high impact components increased `apple' ratings, whereas the low character impact component decreased `apple' ratings. Furthermore, intensity ratings on the attribute that corresponded with the odour of the low impact component were suppressed by the presence of high impact components. These results indicate that the contributions of odorants to the mixture's aroma are not linear combinations of separate odour intensities, because sensory interactions were observed. In addition, humans detect components in complex mixtures more accurately than studies on identification performance have suggested. We conclude that for an adequate assessment of the effects of multiple mixture components on changes in aroma perception, it is sufficient to employ multiple response scales measuring intensities of attributes that are distinctive with respect to the expected qualitative changes. Results of this approach should be subjected to multivariate methods of statistical analysis.