Odor quality profiling

If odor-evoked slow temporal patterns actually provide higher brain centers with information about the odor quality, identification and discrimination cannot be instantaneous as many of the temporal features in the response profiles appear late or even after offset of odor exposure. Honeybees need 500 ms for a response to (non-sexual pheromone) odors but at least 1 second of stimulation is required for a correct discrimination (J. Klein, unpublished, cited in Galizia el al., 2000a). Thus, it appears that time is an important factor in discrimination tasks involving non-pheromonal odors and the slow temporal patterns could theoretically contribute to an olfactory code. In contrast, these temporal patterns would be too slow to encode information about sexual pheromones. Male moths, for example, must be able to respond to rapid changes in stimulus intermittency when moving upwind in pheromone plumes in search of a calling female. 

A. Dravnieks, Odor quality semantically generated multidimensional profiles are stable, Science, 1982, 218, 799-801. 

Second, the system is testable. One can construct the mixtures in order to evaluate their sensory profile. In that respect a mixture system for odor qualities presents the opportunity for further test and validation, which some other methods do not provide. 

The objective of the data analysis was to discover how odors of mixtures related to the odors of components. This may be possible by comparisons of entire multidimensional profiles of mixtures and components, but such an approach requires assumptions on the appropriateness of selecting some specific profile comparison method. The complexity of rules that seem to govern the odor quality of even simple mixtures has been pointed out by Moskowitz, et al. (4)... 

Several theories relating molecular properties to perceived odor quality have been advanced. Examples include the work of Wright (16,] ) who links odor quality to molecular vibrations in the far-infrared, and of Amoore (18) who links odor quality to molecular shape, size, and electronic nature and who introduced the concept of primary class. Beets (19) has discussed odor quality relative to molecular shape as represented by oriented profiles, chirality, and functional groups. In a recently published book (20) he has expanded these discussions. Theimer and coworkers (, , 23) have discussed the Importance of the molecular cross-sectional areas, free energies of desorption, and chirality in relation to odor. A discussion of musk odor quality and molecular structure has been presented by Teranishi (24). Laffort and coworkers ( ) have related odor quality to four molecular properties derived from gas chromatographic retention indices measured on four stationary phases. 

As a consequence the minimvun number of monoosmatic components required to encode an odor quality is two. This seems to rule out the concept of primary odors. However, taking into account the relative intensities of the monoosmatic components one could expect that an odor profile with two monoosmatic components of which one dominates decisively would signal an odor queility approaching the simplicity of a primary odor. 

Furthermore the odorivectors could be treated the same way, with the same methods, as drug molecules are in QSAR (Quantitative Structure Activity Correlation). A computerized approach to biochemical quantitative structure-activity-correlations was introduced by the HANSCH APPROACH (ll). Definition of all the essential profiles, those capable of being expressed in monoosmatic components, would afford the foundation on which an algorithm for the calculation of odor quality based on the chemical structure of the odorivector conceivably could be designed. 

Scholars have attempted to measure odor quality in a variety of ways [66], Others have provided thorough reviews (e.g., [67-69]). We provide a brief overview of four major techniques categorization, profiling, ratings of similarity, and discrimination. 

With both styles, the obtained profile represents an operational definition of odor quality, the validity and utility of which depend on the choice of descriptors/references. No one knows how many descriptors a truly general system would include, but even Dravnieks s 146 fail to distinguish among some qualitatively distinct stimuli [80,90]. In practice, investigators must often tailor systems to the exact specifications of the substances they evaluate. Specialized systems exist for diverse products, including wine, Scotch whisky, and drinking water [91-93]. 

Profiling has produced few data of use to scientists engaged in basic research on the processing of odor quality [69]. However, in more applied settings, the reliability of average profiles allows industry researchers to describe the quality of odors with a standard vocabulary [82,88,94]. Further,... 

P Callegari, J Rouault, P Laffort. Odor quality From descriptor profiles to similarities. Chem Senses 22 1-8, 1997. 

The flavor profile analysis was performed as previously described [6]. The number of evaluated flavor attributes was reduced from six to five (sweet, green, floral, fruity, and sour), removing the smoky odor quality as it was not important for the tomatoes investigated here. The intensities of the odor attributes were scored on a category scale from 0 (not perceptible) to 3 (strongly perceptible) in increments of 0.5. 

Epoxy is used as a lining for water reservoirs, water mains, and home plumbing systems (Heim and Dietrich, 2007a). These applications can impact sensory quality of tap water in food manufacturing, food service operations, and residential homes. This effect may be most noticeable in water but residual aroma and flavor compounds may cause a taint in foods prepared with these water sources. An odor assessment, using a water industry standard flavor profile analysis method, identified a strong relationship between water (simulated tap water, pH 7.7-7.9) stored in epoxy-lined copper pipes for 3-4 days and an odor described... 

Ferreira, V., Aznar, M., Lopez, R., and Cacho, J. (2001a). Quantitative gas chromatography-olfactometry carried out at different dilutions of an extract. Key differences in the odor profiles of four high-quality Spanish aged red wines. J. Agric. Food Chem., 49, 4818-4824. 

One of the most delicate steps in product development is to impart the characteristic flavour note. This concerns both aroma and taste. While the proper taste quality can be achieved by a careful selection of ingredients representing the base note (Fig. 5.50), delivering the typical aroma of a culinary product is in many cases a real challenge. This may be due to losses of odorants caused by high volatility and chemical reactions, thus leading to a misbalanced aroma profile, perceived as weak or even as an off-flavour that is no longer characteristic for the food product. 

The three compounds presented in Table 6.34 are the key odorants of butter [63]. A comparison of the odour profiles of five samples of butter (Table 6.35) with the results of quantitative analysis (Table 6.34) show that the concentrations of these three odorants, which were found in samples 1 and 2, produce an intensive butter aroma. In samples 3 and 5, the concentration of 2,3-butanedione is too low and, therefore, the buttery odour quality is weak. In sample 4, the excessively high butyric acid concentration stimulates a rancid off-flavour. 

The key odorants of two olive oils, I and S, with very different odour profiles (Table 6.36) are listed in Table 6.37. The apple-like and green odour qualities, which are characteristic for oil I, are caused by aldehydes nos. 5 to 7 and 15 (Table 6.37). Their concentration is higher in oil I than in S. The very potent odorant no. 10 occurred only in oil S. As a result of its high OAV and its blackcurrant-like odour quality, it is the character impact odorant of this oil [64, 65]. 

The odour intensities of volatiles showing similar odour qualities are partially additive [68]. To substantiate such additive effects, three groups of odorants (terpene hydrocarbons, esters or aldehydes) were omitted from the aroma model for orange juice. For all groups, a significant difference from the complete model was observed (Table 6.39). Omission of esters nos. 12,14 and 15 with ethyl butanoate (no. 13) still present was clearly detectable. This indicates that the fruity quality in the odour profile is enhanced by additive effects. In contrast, no difference was perceivable when (R)-a-pinene (no. 17) and myrcene (no. 18) were omitted. The concentration of the odorants in juice differs depending on the variety. Thus, the weaker citrus note of Navel oranges compared with the above discussed variety Valencia late is due to a 70% lower content of (R)-limonene [67]. 

Dale MS, Moylan MS, Koch B, Davis MK (1997) MTBE taste and odor threshold determinations using the flavor profile method. Presented at the Water Quality Technology Conference, 9-13 November 1997, Denver, CO... 

It has been observed that the discriminatory capabilities of human olfaction are tremendous It was estimated that an untrained person could differentiate up to ten million odors, perhaps even significantly more than that. Information theory then shows that in order to encode the qualities of ten million odors in a simple binary mode (Monoosmatic components on or off, their intensity, albeit important, is in this connection disregarded) only 2h to 27 specific profiles, disregarding possible and probable redundancies, and therefore the same number of complementary receptor sites would be required. Assuming furthermore that said redundancy, in which the informational modalities of two different specific receptor sites of two different olfactory neurons are confluent in one collector cell and therefore contribute to the expression of only one monoosmatic component is indeed operational it becomes necessary to increase the total number of types of specific receptor sites to 2k-30. This means that only 2U-30 specific detector proteins are required for structure recognition in the transduction process. This compares to about UOOO enzyme systems in different stages of activity estimated to be present in a cell any time. 

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