Odor Mixtures Research Articles

Machine learning-enabled graphene-based electronic olfaction sensors and their olfactory performance assessment

Olfaction is an evolutionary old sensory system, which provides sophisticated access to information about our surroundings. In particular, detecting the volatile organic compounds (VOCs) emitted during natural and artificial processes can be used as characteristic fingerprints and help to identify their source. Inspired by the biological example, artificial olfaction aims at achieving similar performance and thus digitizing the sense of smell. Here, we present the development of machine learning-enabled graphene-based electronic olfaction sensors and propose an approach to assess their olfactory performance toward VOCs. Eleven transient kinetic features extracted from the sensing response profile are utilized as their fingerprint information. By mimicking the Sniffin' Sticks test, electronic olfaction sensors exhibit high olfactory performance toward four VOC odors (clove, eucalyptus, lemon, and rose scent) in terms of odor detection threshold, odor discrimination, and identification performance. Upon exposure to binary odor mixtures, response features of electronic olfaction sensors behave more similarly to that of an individual odor, with a tendency that correlates with their ratio, resembling the overshadowing effect in human olfactory perception. Molecular dynamics simulations and density functional theory calculations results reveal competing adsorption mechanisms between odorant molecules and sensing materials. This may facilitate electronic olfaction sensor applications in some emerging fields, such as environmental monitoring or public security.

Combining the Classification and Pharmacophore Approaches to Understand Homogeneous Olfactory Perceptions at Peripheral Level: Focus on Two Aroma Mixtures.

The mechanisms involved in the homogeneous perception of odorant mixtures remain largely unknown. With the aim of enhancing knowledge about blending and masking mixture perceptions, we focused on structure-odor relationships by combining the classification and pharmacophore approaches. We built a dataset of about 5000 molecules and their related odors and reduced the multidimensional space defined by 1014 fingerprints representing the structures to a tridimensional 3D space using uniform manifold approximation and projection (UMAP). The self-organizing map (SOM) classification was then performed using the 3D coordinates in the UMAP space that defined specific clusters. We explored the allocating in these clusters of the components of two aroma mixtures: a blended mixture (red cordial (RC) mixture, 6 molecules) and a masking binary mixture (isoamyl acetate/whiskey-lactone [IA/WL]). Focusing on clusters containing the components of the mixtures, we looked at the odor notes carried by the molecules belonging to these clusters and also at their structural features by pharmacophore modeling (PHASE). The obtained pharmacophore models suggest that WL and IA could have a common binding site(s) at the peripheral level, but that would be excluded for the components of RC. In vitro experiments will soon be carried out to assess these hypotheses.

Experience-dependent evolution of odor mixture representations in piriform cortex.

Rodents can learn from exposure to rewarding odors to make better and quicker decisions. The piriform cortex is thought to be important for learning complex odor associations; however, it is not understood exactly how it learns to remember discriminations between many, sometimes overlapping, odor mixtures. We investigated how odor mixtures are represented in the posterior piriform cortex (pPC) of mice while they learn to discriminate a unique target odor mixture against hundreds of nontarget mixtures. We find that a significant proportion of pPC neurons discriminate between the target and all other nontarget odor mixtures. Neurons that prefer the target odor mixture tend to respond with brief increases in firing rate at odor onset compared to other neurons, which exhibit sustained and/or decreased firing. We allowed mice to continue training after they had reached high levels of performance and find that pPC neurons become more selective for target odor mixtures as well as for randomly chosen repeated nontarget odor mixtures that mice did not have to discriminate from other nontargets. These single unit changes during overtraining are accompanied by better categorization decoding at the population level, even though behavioral metrics of mice such as reward rate and latency to respond do not change. However, when difficult ambiguous trial types are introduced, the robustness of the target selectivity is correlated with better performance on the difficult trials. Taken together, these data reveal pPC as a dynamic and robust system that can optimize for both current and possible future task demands at once.

Allosteric modulation of a human odorant receptor

Odor perception is first determined by how the myriad of environmental volatiles are detected at the periphery of the olfactory system. The combinatorial activation of dedicated odorant receptors generates enough encoding power for the discrimination of tens of thousands of odorants. Recent studies have revealed that odorant receptors undergo widespread inhibitory modulation of their activity when presented with mixtures of odorants, a property likely required to maintain discrimination and ensure sparsity of the code for complex mixtures. Here, we establish the role of human OR5AN1 in the detection of musks and identify distinct odorants capable of enhancing its activity in binary mixtures. Chemical and pharmacological characterization indicate that specific α-β unsaturated aliphatic aldehydes act as positive allosteric modulators. Sensory experiments show decreased odor detection threshold in humans, suggesting that allosteric modulation of odorant receptors is perceptually relevant and likely adds another layer of complexity to how odors are encoded in the peripheral olfactory system.

Recognition of odor mixtures by odorant receptors

嗅覚系において，個々のにおい分子は約400種類存在する嗅覚受容体の組み合わせによって認識される．一方で，複数のにおい分子が混ざった際にこれらがどのように嗅覚受容体に作用し，認識されるのかについては十分に理解されてこなかった．近年，生体イメージングによるハイスループット解析が可能となり，においの混合物が嗅神経細胞においてどのような応答を生じるのかが明らかになってきた．本稿では，複数のにおい分子の混合物が嗅覚受容体に作用した際に生じる拮抗作用と相乗効果について解説するとともに，においの混合物の識別機構について論じる．また，最近報告された嗅覚受容体の立体構造解析から得られた示唆についても議論する．

Testing effects of trigeminal stimulation on binary odor mixture quality in rats.

Prior attempts at forming theoretical predictions regarding the quality of binary odor mixtures have failed to find any consistent predictor for overshadowing of one component in a binary mixture by the other. We test here the hypothesis that trigeminality contributes to overshadowing effects in binary mixture perception. Most odorants stimulate the trigeminal nerve in the nasal sensory epithelium. In the current study we test rats' ability to detect component odorants in four binary odor sets chosen for their relative trigeminality. We predicted that the difference in trigeminal intensity would predict the degree of overshadowing by boosting or suppressing perceptual intensity of these odorants during learning or during mixture perception. We used a two-alternative choice (TAC) task in which rats were trained to recognize the two components of each mixture and tested on a range of mixtures of the two without reinforcement. We found that even though odorant concentrations were adjusted to balance volatility, all odor sets produced asymmetric psychometric curves. Odor pairs with the greatest difference in trigeminality showed overshadowing by the odorant with weaker trigeminal properties. Odor sets with more evenly matched trigeminal properties also showed asymmetry that was not predicted by either small differences in volatility or trigeminality. Thus, trigeminal properties may influence overshadowing in odor mixtures, but other factors are also likely involved. These mixed results further support the need to test each odor mixture to determine its odor quality and underscore recent results at the level of olfactory receptor neurons that show massive and unpredictable inhibition among odorants in complex mixtures.

How the experimental design associated with objectivized sensory analysis can be used to predict odor quality of gaseous mixtures?

From environmental nuisances to perfumes and aromas, the need to understand odorous interactions is growing. In this context, sensory analysis is often used but faces an obstacle when dealing with odor mixtures. Indeed, sensory approaches provide data on the global nature and intensity of these mixtures, but these methods cannot reveal the various interactions taking place when perceiving an odor. To address this issue, modeling the variations in odor nature and intensity according to the composition of complex mixtures offers insight into sensory interactions as well as a tool to predict odor nature and intensity. While several models have been developed for odor intensity, models for odor nature are scarce. In this study, an experimental mixture design is combined with sensory analysis with the aim of developing new numerical models that transform chemical data into sensory data. Mixtures of four odorants, dimethyl disulfide (DMDS), toluene, furfuryl mercaptan, and cyclopentanone are composed of multiple values of the Odor Activity Value (OAV) for each odorant. The odor nature of each mixture is described using Langage des Nez® (LdN), an objectivized odor nature description method. The variation in odor nature with the composition of the mixture is modeled using the experimental mixture design. The yielded models are shown to be valid and of relatively good descriptive (adjusted R2) between 0.610 and 0.925) and predictive (predictive R2 between 0.390 and 0.886) qualities. The models provide insight into the individual contributions and binary and ternary interactions among the odorants impacting the global odor nature. Moreover, the models allow the odor nature of a mixture to be predicted by transforming the chemical concentration of each odorant into an odor nature description. These results may be an answer to the question raised by the odor mixture effect.

Robust odor identification in novel olfactory environments in mice

Relevant odors signaling food, mates, or predators can be masked by unpredictable mixtures of less relevant background odors. Here, we developed a mouse behavioral paradigm to test the role played by the novelty of the background odors. During the task, mice identified target odors in previously learned background odors and were challenged by catch trials with novel background odors, a task similar to visual CAPTCHA. Female wild-type (WT) mice could accurately identify known targets in novel background odors. WT mice performance was higher than linear classifiers and the nearest neighbor classifier trained using olfactory bulb glomerular activation patterns. Performance was more consistent with an odor deconvolution method. We also used our task to investigate the performance of female Cntnap2-/- mice, which show some autism-like behaviors. Cntnap2-/- mice had glomerular activation patterns similar to WT mice and matched WT mice target detection for known background odors. However, Cntnap2-/- mice performance fell almost to chance levels in the presence of novel backgrounds. Our findings suggest that mice use a robust algorithm for detecting odors in novel environments and this computation is impaired in Cntnap2-/- mice.

Odor mixtures: A chord with silent notes

The olfactory world is one of complex mixtures and blends containing up to hundreds of molecules. Many of those molecules can act as agonists, antagonists or enhancers at different receptors. This complicates the mechanism by which higher centers construct perceptions of complex mixtures. We propose that along with structural chemistry, psychophysics, the techniques of medicinal chemistry and machine learning can begin to shed light on this difficult neural problem.

Expansive linguistic representations to predict interpretable odor mixture discriminability.

Language is often thought as being poorly adapted to precisely describe or quantify smell and olfactory attributes. In this work, we show that semantic descriptors of odors can be implemented in a model to successfully predict odor mixture discriminability, an olfactory attribute. We achieved this by taking advantage of the structure-to-percept model we previously developed for monomolecular odorants, using chemical descriptors to predict pleasantness, intensity and 19 semantic descriptors such as "fish," "cold," "burnt," "garlic," "grass," and "sweet" for odor mixtures, followed by a metric learning to obtain odor mixture discriminability. Through this expansion of the representation of olfactory mixtures, our Semantic model outperforms state of the art methods by taking advantage of the intermediary semantic representations learned from human perception data to enhance and generalize the odor discriminability/similarity predictions. As 10 of the semantic descriptors were selected to predict discriminability/similarity, our approach meets the need of rapidly obtaining interpretable attributes of odor mixtures as illustrated by the difficulty of finding olfactory metamers. More fundamentally, it also shows that language can be used to establish a metric of discriminability in the everyday olfactory space.

Predicting odor profile of food from its chemical composition: Towards an approach based on artificial intelligence and flavorists expertise.

Odor is central to food quality. Still, a major challenge is to understand how the odorants present in a given food contribute to its specific odor profile, and how to predict this olfactory outcome from the chemical composition. In this proof-of-concept study, we seek to develop an integrative model that combines expert knowledge, fuzzy logic, and machine learning to predict the quantitative odor description of complex mixtures of odorants. The model output is the intensity of relevant odor sensory attributes calculated on the basis of the content in odor-active comounds. The core of the model is the mathematically formalized knowledge of four senior flavorists, which provided a set of optimized rules describing the sensory-relevant combinations of odor qualities the experts have in mind to elaborate the target odor sensory attributes. The model first queries analytical and sensory databases in order to standardize, homogenize, and quantitatively code the odor descriptors of the odorants. Then the standardized odor descriptors are translated into a limited number of odor qualities used by the experts thanks to an ontology. A third step consists of aggregating all the information in terms of odor qualities across all the odorants found in a given product. The final step is a set of knowledge-based fuzzy membership functions representing the flavorist expertise and ensuring the prediction of the intensity of the target odor sensory descriptors on the basis of the products' aggregated odor qualities; several methods of optimization of the fuzzy membership functions have been tested. Finally, the model was applied to predict the odor profile of 16 red wines from two grape varieties for which the content in odorants was available. The results showed that the model can predict the perceptual outcome of food odor with a certain level of accuracy, and may also provide insights into combinations of odorants not mentioned by the experts.

Primary Culture of the Human Olfactory Neuroepithelium and Utilization for Henipavirus Infection In Vitro.

The olfactory receptor neurons (ORNs) are a unique cell type involved in the initial perception of odors. These specialized epithelial cells are located in the neuroepithelium of the nasal cavities and directly connect the nasal cavity with the central nervous system (CNS) via axons, which traverse the cribriform plate to synapse within the olfactory bulb. ORNs are derived from precursor cells that lie adjacent to the basal lamina of the olfactory epithelium. These precursor cells divide several times and their progeny differentiate into mature sensory neurons throughout life. In addition to its major and critical role in sensory transduction, the olfactory neuroepithelium may be an important tissue for viral replication and represents a potential site for viral entry into the CNS. In general, to gain access to the CNS, neurotropic viruses such as henipaviruses can use peripheral neural pathways or the circulatory system. However, the olfactory system has been reported to provide a portal of entry to the CNS for henipaviruses. The ability to obtain biopsies from living human subjects and culture these cells in the laboratory provides the opportunity to examine viral replication and effects on a neuronal cell population. As the most exposed and unprotected segment of the nervous system, the olfactory neuroepithelium may have an important role in neuropathology and systemic dissemination of viruses with established CNS effects. This chapter presents methods for primary culture of human ORNs, which have been used successfully by multiple investigators. The protocol provides a consistent, heterogeneous olfactory epithelial cell population, which demonstrates functional responses to odorant mixtures and exhibits several key features of the olfactory receptor neuron phenotype, encompassing olfactory receptors and signaling pathways.

Death and Beyond: Recent Insights and Technological Approach in Artificial Intelligence

Artificial intelligence (AI) is a field of computer science that focuses on developing cognitive computing systems capable of mimicking human-like behavior. AI is a man-made technology that enables machines or software applications to learn from real-world experiences or patterns to make predictions and generate new valuable insights through precise computations. In recent advancements in forensic medicine, devices and machines have been developed to measure, diagnose, and characterize virtual autopsies and biological omics data using AI algorithms. These technologies enable various forensic applications, including ballistic wound age estimation and postmortem interval (PMI) prediction by analyzing different biomarkers. Additionally, electronic noses have been designed to replicate the mammalian olfactory system for the purpose of detecting and classifying odor mixtures, which can also aid in estimating the PMI. While artificial intelligence offers valuable tools and insights to forensic pathologists, its implementation raises important ethical and legal concerns. It has the potential to impact the criminal justice system, and the integrity of these technologies must be carefully considered in future legal investigations. The implications of AI in law enforcement are profound, as "intelligent" algorithms are used to make critical determinations about individuals' guilt or innocence, necessitating the collection of unbiased and accurate data. The frameworks that underpin these algorithms, as well as the individuals responsible for their development, play a pivotal role in shaping the future of AI in forensic science and the legal system.

UNDERSTANDING THE SEARCHING BEHAVIOUR OF PREDATOR AND PARASITOID INSECTS: A REVIEW

The aim of this review is to understand whether the searching behaviour of natural enemies (parasitoids and predators) is affected by complex odour mixtures and if there are differences between specialist and generalist natural enemies. To accomplish this objective, a literature review of several research papers related to the topic was done. Natural enemies of herbivores mainly use herbivore induced plant volatiles (HIPVs) to find their prey or host. However, the chemical information conveyed through HIPVs toward natural enemies might be unreliable or sometimes undetectable for them (odour masking), thereby influencing their searching behaviour. Many factors influence the reliability and detectability of HIPVs such as: plant diversity, intensity of the herbivore attack, if one or more herbivore species attack the plant at the same time or sequentially, and environmental conditions like light intensity, fertilization levels, soil moisture, and high concentrations of ozone, hydroxyl, and nitrate radicals. Nevertheless, in order to overcome reliability and detectability problems of HIPVs, natural enemies have the ability to learn by association and use these HIPVs to find their prey or host. Further field experiments supported by laboratory assessments should be carried out to understand how the searching behaviour of natural enemies is affected in a multitrophic level context, where known biodiversity (above and below ground) and environmental conditions have the possibility to interact.

Masking effects on iso-valeric acid recognition by sub-threshold odor mixture.

Masking unpleasant odors with pleasant-smelling odorants has a long history and is utilized in various industries, including perfumery and consumer products. However, the effectiveness of odor masking is idiosyncratic and temporary. In this study, we employed Sniff olfactometry (SO) to investigate the psychophysics of masking using brief 70 ms stimulations with mixtures of the mal-odorant iso-valeric acid (IVA) and different masking agents. IVA is a component of human sweat that can overpower its smell and is often associated with unpleasant descriptors such as "gym locker," "smelly feet," "dirty clothes," and so on. Traditionally, high concentrations of pleasant-smelling odorants are used to mitigate the unpleasantness of IVA in situations involving clothing or environments contaminated with IVA. To examine the masking effects of sub-threshold levels of various masking agents (neohivernal, geraniol, florhydral, decanal, iso-longifolanone, methyl iso-eugenol, and s-limonene) on IVA, we conducted experiments using SO to measure the probability of recognizing IVA after 70 ms stimulations with headspaces containing mixtures of super-threshold concentrations of IVA and sub-threshold concentrations of IVA suppressors. The study involved nine subjects, and on average, a single masking agent was found to decrease IVA recognition probability by 14-72%. Moreover, a sub-threshold odor mixture consisting of 6 masking agents demonstrated a substantial decrease in IVA recognition, with a reduction of 96%.

‘Tasting Imagination’: What Role Chemosensory Mental Imagery in Multisensory Flavour Perception?

A number of perplexing phenomena in the area of olfactory/flavour perception may fruitfully be explained by the suggestion that chemosensory mental imagery can be triggered automatically by perceptual inputs. In particular, the disconnect between the seemingly limited ability of participants in chemosensory psychophysics studies to distinguish more than two or three odorants in mixtures and the rich and detailed flavour descriptions that are sometimes reported by wine experts; the absence of awareness of chemosensory loss in many elderly individuals; and the insensitivity of the odour-induced taste enhancement (OITE) effect to the mode of presentation of olfactory stimuli (i.e., orthonasal or retronasal). The suggestion made here is that the theory of predictive coding, developed first in the visual modality, be extended to chemosensation. This may provide a fruitful way of thinking about the interaction between mental imagery and perception in the experience of aromas and flavours. Accepting such a suggestion also raises some important questions concerning the ecological validity/meaning of much of the chemosensory psychophysics literature that has been published to date.

Multisensory integration of orally-sourced gustatory and olfactory inputs to the posterior piriform cortex in awake rats.

Flavour refers to the sensory experience of food, which is a combination of sensory inputs sourced from multiple modalities during consumption, including taste and odour. Previous work has demonstrated that orally-sourced taste and odour cues interact to determine perceptual judgements of flavour stimuli, although the underlying cellular- and circuit-level neural mechanisms remain unknown. We recently identified a region of the piriform olfactory cortex in rats that responds to both taste and odour stimuli. Here, we investigated how converging taste and odour inputs to this area interact to affect single neuron responsiveness ensemble coding of flavour identity. To accomplish this, we recorded spiking activity from ensembles of single neurons in the posterior piriform cortex (pPC) in awake, tasting rats while delivering taste solutions, odour solutions and taste+odour mixtures directly into the oral cavity. Our results show that taste and odour inputs evoke highly selective, temporally-overlapping responses in multisensory pPC neurons. Comparing responses to mixtures and their unisensory components revealed that taste and odour inputs interact in a non-linear manner to produce unique response patterns. Taste input enhances trial-by-trial decoding of odour identity from small ensembles of simultaneously recorded neurons. Together, these results demonstrate that taste and odour inputs to pPC interact in complex, non-linear ways to form amodal flavour representations that enhance identity coding. KEY POINTS: Experience of food involves taste and smell, although how information from these different senses is combined by the brain to create our sense of flavour remains unknown. We recorded from small groups of neurons in the olfactory cortex of awake rats while they consumed taste solutions, odour solutions and taste+odour mixtures. Taste and smell solutions evoke highly selective responses. When presented in a mixture, taste and smell inputs interacted to alter responses, resulting in activation of unique sets of neurons that could not be predicted by the component responses. Synergistic interactions increase discriminability of odour representations. The olfactory cortex uses taste and smell to create new information representing multisensory flavour identity.

In-hive learning of specific mimic odours as a tool to enhance honey bee foraging and pollination activities in pear and apple crops

The areas devoted to agriculture that depend on pollinators have been sharply increased in the last decades with a concomitant growing global demand for pollination services. This forces to consider new strategies in pollinators’ management to improve their efficiency. To promote a precision pollination towards a specific crop, we developed two simple synthetic odorant mixtures that honey bees generalized with their respective natural floral scents of the crop. We chose two commercial crops for fruit production that often coexist in agricultural settings, the apple (Malus domesticus) and the pear trees (Pyrus communis). Feeding colonies with sucrose solution scented with the apple mimic (AM) or the pear mimic (PM) odour enabled the establishment of olfactory memories that can bias bees towards the flowers of these trees. Encompassing different experimental approaches, our results support the offering of scented food to improve foraging and pollination activities of honey bees. The circulation of AM-scented sucrose solution inside the hive promoted higher colony activity, probably associated with greater activity of nectar foragers. The offering of PM-scented sucrose solution did not increase colony activity but led to greater pollen collection, which is consistent with pear flowers offering mainly pollen as resources for the bees. Results obtained from apple and pear crops suggest that the offering of AM- and PM-scented sucrose solution increased fruit yields. This preliminary study highlights the role of in-hive olfactory learning to bias foraging preferences within pome fruit crops.

The Locust antenna as an odor discriminator

Identifying chemical odors rapidly and accurately is critical in a variety of fields. Due to the limited human sense of smell, much effort has been dedicated to the development of electronic sensing devices. Despite some recent progress, such devices are still no match for the capabilities of biological (animal) olfactory sensors, which are light, robust, versatile, and sensitive. Consequently, scientists are turning to a new approach: Bio-Hybrid sensors. These sensors combine animal biological sensors with electronic components to achieve maximum detection and classification while conveying a comprehensible signal to the end user.In this work, we created a bio-hybrid odor discriminator utilizing the desert locust's primary olfactory apparatus - its antennae, together with simple electroantennogram technology and artificial intelligence tools for signal analysis. Our discriminator is able to differentiate between at least eight pure odors and two mixtures of different odorants, independently of odorant concentration. With four orders of magnitude higher sensitivity than gas chromatography–mass spectrometry, it is able to detect the presence of less than 1 ng of volatile compounds and, compared to other bio-hybrid sensors available today, it can be easily operated by an unskilled individual. This study thus opens up the future for robust and simple bio-hybrid robotic sensing devices that can be widely deployed.

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.