Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors

Abstract

The perception of odor is an important component of smell; the first step of odor detection, and the discrimination of structurally diverse odorants depends on their interactions with olfactory receptors (ORs). Indeed, the perception of an odor's quality results from a combinatorial coding, in which the deciphering remains a major challenge.Several studies have successfully established links between odors and odorants by categorizing and classifying data. Hence, the categorization of odors appears to be a promising way to manage odors.In the proposed study, we performed a computational analysis using odor descriptions of the odorants present in Flavor-Base 9th Edition (2013). We converted the Flavor-Base data into a binary matrix (1 when the odor note appears in the odor description, 0 otherwise). We retained 251 odor notes and 3508 odorants, considering only the orthonasal perception. Two categorization methods were performed: agglomerative hierarchical clustering (AHC), and self-organizing map (SOM). AHC was based on a measure of the distance between the elements performed by multidimensional scaling (MDS) for the odorants, and correspondence analysis (CA) for the odor notes.The results demonstrated that the SOM classes appeared to be less dependent on the frequency of the odor notes than those of the AHC clusters. SOMs are especially useful for identifying the associations between less than 4 or 5 odor notes within groups of odorants.The obtained results highlight subsets of odorants sharing similar groups of odor notes, suggesting an interesting and promising way of using computational approaches to help decipher olfactory coding.

Exploring the Characteristics of an Aroma-Blending Mixture by Investigating the Network of Shared Odors and the Molecular Features of Their Related Odorants

“…An "ethereal odor" was also reported by Arctander ("diffusive sweet ethereal, fruity odor, milder and sweeter, more floral & less fruity than the n-butyrate" [44]). Interestingly, "winey" and "pineapple" are in the same class as our previously achieved Kohonen classification of odor notes (SOM cl-3m7 × 7) [28].…”

“…The first axis is in line with studies that highlight the significance of the biological function of odorants, that is, their odor, to understand odorant discrimination [29,30]. From this perspective, on the basis on our recent work on the analysis of a large odorant database [28], we applied the notion of a "social network" to all the odor notes shared by the STR/CAR/PNA odorants in the StCaPi-set. In social sciences, such a network is used to study relationships among individuals.…”

“…We selected molecules from a large database, previously used to identify links between odor notes and odorants by multivariate statistical analysis [28]. This database, called FB-3508, includes 3508 odorants and 251 odor notes as a binary matrix and was built from the 9th version of the commercially available Flavor-Base [33].…”

“…We selected the molecules for the training sets based on their odor notes. The molecules were extracted from the large database that we previously used for multivariate statistical analysis [28] and was built from the 9th version of Flavor-Base [33]. This database, called FB-3508, encompasses 3508 odorants and 251 odors as a binary matrix (1 when the odor note appears in the odor description, 0 otherwise).…”

“…The aim of the present work was to identify the characteristics of odorants carrying the same odor notes as those of the mixture "Et-iB + Et-M ≡ Al-H", i.e., in terms of the odor notes STR + CAR ≡ PNA, with the objective of helping to understand aroma blending perception. To explore the key features of these odorants, we extracted from a large database of odorants (3508 odorants, 251 odor notes [28]) all of the molecules having at least one of the odors-STR, CAR or PNA-in their odorant description (henceforth called STR/CAR/PNA odorants). The obtained dataset, called "StCaPi-set", included 293 molecules and 112 odors.…”