Evaluation of the Key Odorants of Foods by Dilution Experiments, Aroma Models and Omission

Abstract

The state of the art in aroma analysis is reviewed with emphasis on aroma-recombination studies using synthetic blends of odorants (aroma models) which have been prepared on the basis of analytical data. The model that matches the original aroma is the starting material for omission experiments which are performed to establish the odorants that actually contribute to the aroma. These experiments are discussed in detail for the aromas of two wine varieties, three olive oils of different provenance, French fries, boiled beef and coffee. The results indicate that odorants with higher odour activity values (OAV, the ratio of the concentration to the odour threshold) are frequently essential for the aroma. However, there are exceptions where odorants with high OAVs are suppressed in the aroma and compounds with lower OAVs are important contributors. These findings are discussed in the light of model experiments which have been reported in the literature to obtain an insight into the perceptual interactions of odorants in mixtures.

Gas chromatography-olfactometry

“…In addition, highly volatile compounds may contribute to the top-note of an original food, plant, flower or sample. These may be lost during solvent extraction, distillation and concentration procedures and could result in an extract that is not representative of the original sample [78]. These compounds could also coelute with the solvent peak and so not be evaluated by GC-O [78].…”

“…These may be lost during solvent extraction, distillation and concentration procedures and could result in an extract that is not representative of the original sample [78]. These compounds could also coelute with the solvent peak and so not be evaluated by GC-O [78]. A potential solution to these problems is to use headspace sampling, using either static or dynamic methods [56,78].…”

“…These compounds could also coelute with the solvent peak and so not be evaluated by GC-O [78]. A potential solution to these problems is to use headspace sampling, using either static or dynamic methods [56,78]. However, headspace methods may yield relatively low concentrations making identification more difficult.…”

“…One innovative solution for headspace analysis using SPME has been proposed, whereby direct-GC-O (D-GC-O) is carried out on a GC instrument fitted with an uncoated column, and therefore compounds are not separated prior to sniffing [87]. It may also be important to authenticate the conclusions of GC-O experiments by evaluating the similarity of synthetic models (created using important odorants identified by GC-O) with the original food product [45,65,78,88]. This is relatively straightforward for homogenous, liquid foods but can be very difficult for solid foods [78].…”

“…It may also be important to authenticate the conclusions of GC-O experiments by evaluating the similarity of synthetic models (created using important odorants identified by GC-O) with the original food product [45,65,78,88]. This is relatively straightforward for homogenous, liquid foods but can be very difficult for solid foods [78]. Importance of the individual odorants and their interactions with other compounds in a model system can then be evaluated using omission experiments [78].…”