Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem

Deep Learning for Odor Prediction on Aroma-Chemical Blends

The application of deep-learning techniques to aroma chemicals has resulted in models that surpass those of human experts in predicting olfactory qualities. However, public research in this field has been limited to predicting the qualities of individual molecules, whereas in industry, perfumers and food scientists are often more concerned with blends of multiple molecules. In this paper, we apply both established and novel approaches to a data set we compiled, which consists of labeled pairs of molecules. We present graph neural network models that accurately predict the olfactory qualities emerging from blends of aroma chemicals along with an analysis of how variations in model architecture can significantly impact predictive performance.

Data-Driven Elucidation of Flavor Chemistry

Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

OWSum: algorithmic odor prediction and insight into structure-odor relationships

We derived and implemented a linear classification algorithm for the prediction of a molecule's odor, called Olfactory Weighted Sum (OWSum). Our approach relies solely on structural patterns of the molecules as features for algorithmic treatment and uses conditional probabilities combined with tf-idf values. In addition to the prediction of molecular odor, OWSum provides insights into properties of the dataset and allows to understand how algorithmic classifications are reached by quantitatively assigning structural patterns to odors. This provides chemists with an intuitive understanding of underlying interactions. To deal with ambiguities of the natural language used to describe odor, we introduced descriptor overlap as a metric for the quantification of semantic overlap between descriptors. Thus, grouping of descriptors and derivation of higher-level descriptors becomes possible. Our approach poses a large leap forward in our capabilities to understand and predict molecular features.

Olfactory abstraction: a communicative and metacognitive account

The usual puzzle raised about olfaction is that of a deficit of abstraction: smells, by contrast notably with colours, do not easily lend themselves to abstract categories and labels. Some studies have argued that the puzzle is culturally restricted and that abstraction is more common outside urban Western societies. Here, I argue that the puzzle is misconstrued and should be reversed: given that odours are constantly changing and that their commonalities are difficult for humans to identify, what is surprising is not that abstract terms are rare, but that they should be used at all for olfaction. Given the nature of the olfactory environment and our cognitive equipment, concrete labels referring to sources seem most adaptive. To explain the use and presence of abstract terms, we need to examine their social and communicative benefits. Here these benefits are spelt out as securing a higher agreement among individuals varying in their olfactory experiences as well as the labels they use, as well as feeling a heightened sense of confidence in one's naming capacities. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

OOPS, the Ontology for Odor Perceptual Space: From Molecular Composition to Sensory Attributes of Odor Objects

When creating a flavor to elicit a specific odor object characterized by odor sensory attributes (OSA), expert perfumers or flavorists use mental combinations of odor qualities (OQ) such as Fruity, Green, and Smoky. However, OSA and OQ are not directly related to the molecular composition in terms of odorants that constitute the chemical stimuli supporting odor object perception because of the complex non-linear integration of odor mixtures within the olfactory system. Indeed, single odorants are described with odor descriptors (OD), which can be found in various databases. Although classifications and aroma wheels studied the relationships between OD and OQ, the results were highly dependent on the studied products. Nevertheless, ontologies have proven to be very useful in sharing concepts across applications in a generic way and to allow experts' knowledge integration, implying non-linear cognitive processes. In this paper, we constructed the Ontology for Odor Perceptual Space (OOPS) to merge OD into a set of OQ best characterizing the odor, further translated into a set of OSA thanks to expert knowledge integration. Results showed that OOPS can help bridge molecular composition to odor perception and description, as demonstrated in the case of wines.

More than meets the AI: The possibilities and limits of machine learning in olfaction

Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools. None of them succeeded, however, and it matters as to why this is the case. This article argues that we should be deeply skeptical about the trend to black-box the sensory system's biology in our theories of perception. Instead, we need to ground both stimulus models and psychophysical data on real causal-mechanistic explanations of the olfactory system. The central question is: Would knowledge of biology lead to a better understanding of the stimulus in odor coding than the one utilized in current machine learning models? That is indeed the case. Recent studies about receptor behavior have revealed that the olfactory system operates by principles not captured in current stimulus-response models. This may require a fundamental revision of computational approaches to olfaction, including its psychological effects. To analyze the different research programs in olfaction, we draw on Lloyd's "Logic of Research Questions," a philosophical framework which assists scientists in explicating the reasoning, conceptual commitments, and problems of a modeling approach in question.

Odorant Receptor Inhibition Is Fundamental to Odor Encoding

Most natural odors are complex mixtures of volatile components, competing to bind odorant receptors (ORs) expressed in olfactory sensory neurons (OSNs) of the nose. To date, surprisingly little is known about how OR antagonism shapes neuronal representations in the detection layer of the olfactory system. Here, we investigated its prevalence, the degree to which it disrupts OR ensemble activity, and its conservation across phylogenetically related ORs. Calcium imaging microscopy of dissociated OSNs revealed significant inhibition, often complete attenuation, of responses to indole-a commonly occurring volatile associated with both floral and fecal odors-by a set of 36 tested odorants. To confirm an OR mechanism for the observed inhibition, we performed single-cell transcriptomics on OSNs exhibiting specific response profiles to a diagnostic panel of odorants and identified three paralogous receptors-Olfr740, Olfr741, and Olfr743-which, when tested in vitro, recapitulated OSN responses. We screened ten ORs from the Olfr740 gene family with ∼800 perfumery-related odorants spanning a range of chemical scaffolds and functional groups. Over half of these compounds (430) antagonized at least one of the ten ORs. OR activity fitted a mathematical model of competitive receptor binding and suggests normalization of OSN ensemble responses to odorant mixtures is the rule rather than the exception. In summary, we observed OR antagonism occurred frequently and in a combinatorial manner. Thus, extensive receptor-mediated computation of mixture information appears to occur in the olfactory epithelium prior to transmission of odor information to the olfactory bulb.

Odors: from chemical structures to gaseous plumes

We are immersed within an odorous sea of chemical currents that we parse into individual odors with complex structures. Odors have been posited as determined by the structural relation between the molecules that compose the chemical compounds and their interactions with the receptor site. But, naturally occurring smells are parsed from gaseous odor plumes. To give a comprehensive account of the nature of odors the chemosciences must account for these large distributed entities as well. We offer a focused review of what is known about the perception of odor plumes for olfactory navigation and tracking, which we then connect to what is known about the role odorants play as properties of the plume in determining odor identity with respect to odor quality. We end by motivating our central claim that more research needs to be conducted on the role that odorants play within the odor plume in determining odor identity.

Chemical features mining provides new descriptive structure-odor relationships

An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling.

An important issue in olfaction sciences deals with the question of how a chemical information can be translated into percepts. This is known as the stimulus-percept problem. Here, we set out to better understand this issue by combining knowledge about the chemistry and cognition of smells with computational olfaction. We also assumed that not only one, but several physicochemical models may describe a given olfactory quality. To achieve this aim, a first challenge was to set up a database with ~1700 molecules characterized by chemical features and described by olfactory qualities (e.g. fruity, woody). A second challenge consisted in developing a computational model enabling the discrimination of olfactory qualities based on these chemical features. By meeting these 2 challenges, we provided for several olfactory qualities new chemical models describing why an odorant molecule smells fruity or woody (among others). For most qualities, multiple (rather than a single) chemical models were generated. These findings provide new elements of knowledge about the relationship between odorant chemistry and perception. They also make it possible to envisage concrete applications in the aroma and fragrance field where chemical characterization of smells is an important step in the design of new products.

Machine-Learning-Based Olfactometer: Prediction of Odor Perception from Physicochemical Features of Odorant Molecules

Gas chromatography/olfactometry (GC/O) has been used in various fields as a valuable method to identify odor-active components from a complex mixture. Since human assessors are employed as detectors to obtain the olfactory perception of separated odorants, the GC/O technique is limited by its subjectivity, variability, and high cost of the trained panelists. Here, we present a proof-of-concept model by which odor information can be obtained by machine-learning-based prediction from molecular parameters (MPs) of odorant molecules. The odor prediction models were established using a database of flavors and fragrances including 1026 odorants and corresponding verbal odor descriptors (ODs). Physicochemical parameters of the odorant molecules were acquired by use of molecular calculation software (DRAGON). Ten representative ODs were selected to build the prediction models based on their high frequency of occurrence in the database. The features of the MPs were extracted via either unsupervised (principal component analysis) or supervised (Boruta, BR) approaches and then used as input to calibrate machine-learning models. Predictions were performed by various machine-learning approaches such as support vector machine (SVM), random forest, and extreme learning machine. All models were optimized via parameter tuning and their prediction accuracies were compared. A SVM model combined with feature extraction by BR-C (confirmed only) was found to afford the best results with an accuracy of 97.08%. Validation of the models was verified by using the GC/O data of an apple sample for comparison between the predicted and measured results. The prediction models can be used as an auxiliary tool in the existing GC/O by suggesting possible OD candidates to the panelists and thus helping to give more objective and correct judgment. In addition, a machine-based GC/O in which the panelist is no longer needed might be expected after further development of the proposed odor prediction technique.