Functional odor classification through a medicinal chemistry approach

FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data

Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.Scientific Contribution FlavorMiner is an advanced machine learning (ML)-based tool designed to predict molecular flavor features with high accuracy and efficiency, addressing the complexity of food metabolomics. By leveraging robust algorithmic combinations paired with mathematical representations FlavorMiner achieves high predictive performance. Applied to cocoa metabolomics, FlavorMiner demonstrated its capacity to extract meaningful insights, showcasing its versatility for flavor analysis across diverse food products. This study underscores the transformative potential of ML in accelerating flavor biochemistry research, offering a scalable solution for the food and beverage industry.

Narrative review on artificially intelligent olfaction in halitosis

Halitosis, commonly known as oral malodor, is a multifactorial health concern that significantly impacts the psychological and social well-being of individuals. It is the third most frequent reason for individuals to seek dental treatment, after dental caries and periodontal diseases. For an in-depth exploration of the topic of halitosis, an extensive literature review was conducted. The review focused on articles published in peer-reviewed journals and only those written in the English language were considered. The search for relevant literature began by employing subject headings such as 'halitosis, oral malodor, volatile sulfur compounds, artificial intelligence, and olfaction' in databases such as PubMed/Medline, Scopus, Google Scholar, Web of Science, and EMBASE. Additionally, a thorough hand search of references was conducted to ensure the comprehensiveness of the review. After amalgamating the search outcomes, a comprehensive analysis revealed the existence of precisely 134 full-text articles that bore relevance to the study. Abstracts and editorial letters were excluded from this study, and almost 50% of the full-text articles were deemed immaterial to dental practice. Out of the remaining articles, precisely 54 full-text articles were employed in this review. As primary healthcare providers, dentists are responsible for diagnosing and treating oral issues that may contribute to the development of halitosis. To effectively manage this condition, dentists must educate their patients about the underlying causes of halitosis, as well as proper oral hygiene practices such as tongue cleaning, flossing, and selecting appropriate mouthwash and toothpaste. This narrative review summarises all possible AI olfaction in halitosis.

Molecular descriptors of icewine odorants: A first insight into their relationship with metabolism and olfactory perception

To investigate the differences in physicochemical parameters of compounds that are metabolized from different precursors and contribute to the aroma perception of icewine, odor-active compounds in icewine were identified by gas chromatography-olfactometry (GC-O) analysis combined with comprehensive two-dimensional GC and time-of-flight mass spectrometry (GC  ×  GC-TOFMS) analysis, and the molecular descriptors of these odor-active compounds were calculated by computational chemistry software. The distribution pattern of these odorants classified by their precursors and their olfactory perception was visualized on the basis of their molecular descriptor differences. The results showed that the odorants sourced from different precursors could be clearly separated from each other based on their molecular descriptors, which belonged to blocks such as constitution indices and 2D matrix-based descriptors. The results also showed that honey and cooked potatoe descriptions or peach and smoke descriptions have quite different molecular descriptors. This study should contribute to future research that relates to computational chemistry-based aroma perception and prediction in fermented beverages. PRACTICAL APPLICATION: The results obtained from this study may be useful for the construction of a classification system of various odor-active compounds in a given product and may provide a molecular solution for the determination of different perceptual dimensions of an odor mixture.

Fine wine recognition and appreciation: It is time to change the paradigm of wine tasting

Wine quality maybe understood under two perspectives: (a) commercial quality, intended to satisfy overall consumers, and (b) fine wine quality, aimed at achieving a product with aesthetic value. The current food sensory techniques (e.g. Descriptive Analysis) have been successfully applied to develop wines accepted worldwide and characterized by pleasant sweetish flavours and smooth mouthfeel. On the contrary, these techniques are not suited to characterize fine wines given their dependence on sensory properties with aesthetic value. The conventional tasting approaches follow the sequence of vision, smell (orthonasal), taste and mouthfeel, ending by an overall evaluation. The sensory descriptors tend to be analytic (e.g. different aromas and tastes) or synthetic (e.g. body, structure) and the quality judgement is left for the final step. Some synthetic attributes may have an aesthetic significance (e.g. complexity, harmony, depth) and are more valued when the analytic or synthetic descriptors are highly praised (e.g. oakiness, silkiness, body, minerality). Consequently, these highly praised attributes are regarded as surrogates of fine wine quality. However, commercial wines are frequently judged of higher quality than fine wines irrespective of the taster expertise. We argue that the conventional sensory analysis sequence makes the overall evaluation secondary in relation to the previous analytical steps blurring the assessment of wine's aesthetic properties. Probably due to top-down processing, the initial evaluation of colour or flavours governs the final overall quality evaluation that may be inconsistently rated. Then, to promote the recognition of fine wines, tasting should begin by first acknowledging the aesthetic properties and only proceed to the analytical steps if necessary. A tasting method is proposed to consumer educational programs where emotional responses are used to explain the differences between commercial and fine wine styles. Furthermore, cultural aspects should be included to appreciate the wholeness of wine. Hopefully, this holistic perspective would turn wine appreciation more approachable and facilitate the recognition of fine wines among consumers, increasing their appreciation and enjoyment.

Tracing the origin of VOCs in post-consumer plastic film bales

Volatile organic compounds (VOCs), including odors, are still a key issue in plastic recycling, especially in case of flexible packaging. Therefore, this study presents a detailed qualitative and quantitative VOC analysis by applying gas chromatography on 17 categories of flexible plastic packaging that are manually sorted from bales of post-consumer flexible packaging (e.g., beverage shrink wrap, packaging for frozen food, packaging for dairy products, etc.). A total of 203 VOCs are identified on packaging used for food products, while only 142 VOCs are identified on packaging used for non-food products. Especially, more oxygenated compounds (e.g., fatty acids, esters, aldehydes) are identified on food packaging. With more than 65 VOCs, the highest number of VOCs is identified on packaging used for chilled convenience food and ready meals. The total concentration of 21 selected VOCs was also higher on packaging used for food products (totally 9187 μg/kg plastic) compared to packaging used for non-food packaging (totally 3741 μg/kg plastic). Hence, advanced sorting of household plastic packaging waste, e.g., via tracer-based sorting or watermarking, could open the door towards sorting on other properties than polymer type, such as mono- versus multi-material packaging, food versus non-food packaging or even their VOC profile, which might allow for tailoring washing procedures. Potential scenarios showed that sorting the categories with the lowest VOC load, which corresponds to half of the total mass of flexible packaging, could result in a VOC reduction of 56%. By producing less contaminated plastic film fractions and by tailoring washing processes recycled plastics can ultimately be used in a broader market segment.

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'.

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.

Predicting odor from molecular structure: a multi-label classification approach

Decoding the factors behind odor perception has long been a challenge in the field of human neuroscience, olfactory research, perfumery, psychology, biology and chemistry. The new wave of data-driven and machine learning approaches to predicting molecular properties are a growing area of research interest and provide for significant improvement over conventional statistical methods. We look at these approaches in the context of predicting molecular odor, specifically focusing on multi-label classification strategies employed for the same. Namely binary relevance, classifier chains, and random forests adapted to deal with such a task. This challenge, termed quantitative structure–odor relationship, remains an unsolved task in the field of sensory perception in machine learning, and we hope to emulate the results achieved in the field of vision and auditory perception in olfaction over time.

Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb

In olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better characterize this relationship in the mouse olfactory system by defining glomeruli in terms of the odorants to which they are most sensitive. Using high-throughput odorant delivery and ultrasensitive imaging of sensory inputs, we imaged responses to 185 odorants presented at concentrations determined to activate only one or a few glomeruli across the dorsal olfactory bulb. The resulting datasets defined the tuning properties of glomeruli - and, by inference, their cognate odorant receptors - in a low-concentration regime, and yielded consensus maps of glomerular sensitivity across a wide range of chemical space. Glomeruli were extremely narrowly tuned, with ~25% responding to only one odorant, and extremely sensitive, responding to their effective odorants at sub-picomolar to nanomolar concentrations. Such narrow tuning in this concentration regime allowed for reliable functional identification of many glomeruli based on a single diagnostic odorant. At the same time, the response spectra of glomeruli responding to multiple odorants was best predicted by straightforward odorant structural features, and glomeruli sensitive to distinct odorants with common structural features were spatially clustered. These results define an underlying structure to the primary representation of sensory space by the mouse olfactory system.

Workflow to Investigate Subtle Differences in Wine Volatile Metabolome Induced by Different Root Systems and Irrigation Regimes

To allow for a broad survey of subtle metabolic shifts in wine caused by rootstock and irrigation, an integrated metabolomics-based workflow followed by quantitation was developed. This workflow was particularly useful when applied to a poorly studied red grape variety cv. Chambourcin. Allowing volatile metabolites that otherwise may have been missed with a targeted analysis to be included, this approach allowed deeper modeling of treatment differences which then could be used to identify important compounds. Wines produced on a per vine basis, over two years, were analyzed using SPME-GC-MS/MS. From the 382 and 221 features that differed significantly among rootstocks in 2017 and 2018, respectively, we tentatively identified 94 compounds by library search and retention index, with 22 confirmed and quantified using authentic standards. Own-rooted Chambourcin differed from other root systems for multiple volatile compounds with fewer differences among grafted vines. For example, the average concentration of β-Damascenone present in own-rooted vines (9.49 µg/L) was significantly lower in other rootstocks (8.59 µg/L), whereas mean Linalool was significantly higher in 1103P rootstock compared to own-rooted. β-Damascenone was higher in regulated deficit irrigation (RDI) than other treatments. The approach outlined not only was shown to be useful for scientific investigation, but also in creating a protocol for analysis that would ensure differences of interest to the industry are not missed.

OdoriFy: A conglomerate of artificial intelligence-driven prediction engines for olfactory decoding

The molecular mechanisms of olfaction, or the sense of smell, are relatively underexplored compared with other sensory systems, primarily because of its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors (ORs) allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a Web server featuring powerful deep neural network-based prediction engines. OdoriFy enables (1) identification of odorant molecules for wildtype or mutant human ORs (Odor Finder); (2) classification of user-provided chemicals as odorants/nonodorants (Odorant Predictor); (3) identification of responsive ORs for a query odorant (OR Finder); and (4) interaction validation using Odorant-OR Pair Analysis. In addition, OdoriFy provides the rationale behind every prediction it makes by leveraging explainable artificial intelligence. This module highlights the basis of the prediction of odorants/nonodorants at atomic resolution and for the ORs at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human ORs with their known agonists and nonagonists, making it a highly interactive and resource-enriched Web server. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/.

Bitter Taste and Olfactory Receptors: Beyond Chemical Sensing in the Tongue and the Nose

Abstract

The Up-and-Coming-Scientist section of the current issue of the Journal of Membrane Biology features the invited essay by Dr. Mercedes Alfonso-Prieto, Assistant Professor at the Forschungszentrum Jülich (FZJ), Germany, and the Heinrich-Heine University Düsseldorf, Vogt Institute for Brain Research. Dr. Alfonso-Prieto completed her doctoral degree in chemistry at the Barcelona Science Park, Spain, in 2009, pursued post-doctoral research in computational molecular sciences at Temple University, USA, and then, as a Marie Curie post-doctoral fellow at the University of Barcelona, worked on computations of enzyme reactions and modeling of photoswitchable ligands targeting neuronal receptors. In 2016, she joined the Institute for Advanced Science and the Institute for Computational Biomedicine at the FZJ, where she pursues research on modeling and simulation of chemical senses. The invited essay by Dr. Alfonso-Prieto discusses state-of-the-art modeling of molecular receptors involved in chemical sensing – the senses of taste and smell. These receptors, and computational methods to study them, are the focus of Dr. Alfonso-Prieto’s research. Recently, Dr. Alfonso-Prieto and colleagues have presented a new methodology to predict ligand binding poses for GPCRs, and extensive computations that deciphered the ligand selectivity determinants of bitter taste receptors. These developments inform our current understanding of how taste occurs at the molecular level.

Graphic Abstract

Smell compounds classification using UMAP to increase knowledge of odors and molecular structures linkages

This study aims to highlight the relationships between the structure of smell compounds and their odors. For this purpose, heterogeneous data sources were screened, and 6038 odorant compounds and their known associated odors (162 odor notes) were compiled, each individual molecule being represented with a set of 1024 structural fingerprint. Several dimensional reduction techniques (PCA, MDS, t-SNE and UMAP) with two clustering methods (k-means and agglomerative hierarchical clustering AHC) were assessed based on the calculated fingerprints. The combination of UMAP with k-means and AHC methods allowed to obtain a good representativeness of odors by clusters, as well as the best visualization of the proximity of odorants on the basis of their molecular structures. The presence or absence of molecular substructures has been calculated on odorant in order to link chemical groups to odors. The results of this analysis bring out some associations for both the odor notes and the chemical structures of the molecules such as "woody" and "spicy" notes with allylic and bicyclic structures, "balsamic" notes with unsaturated rings, both "sulfurous" and "citrus" with aldehydes, alcohols, carboxylic acids, amines and sulfur compounds, and "oily", "fatty" and "fruity" characterized by esters and with long carbon chains. Overall, the use of UMAP associated to clustering is a promising method to suggest hypotheses on the odorant structure-odor relationships.

Imaging the living brain: An argument for ruthless reductionism from olfactory neurobiology

Should theories of "higher-level" cognitive effects originate in "lower-level" molecular mechanisms? This paper supports reductionist explanations of sensory perception via molecular mechanisms in neurobiology. It shows that molecular and cellular mechanisms must constitute the material foundation to derive better theories and models for neuroscience. In support of "bottom-up theorizing", I explore the recent application of a new real-time molecular imaging technique (SCAPE microscopy) to mixture coding in olfaction. Seemingly emergent "higher-level" psychological effects in odor perception, irreducible to the physical stimulus, are linked back to underlying molecular mechanisms at the receptor level. The SCAPE study has notable theoretical impact. It provides a possible answer to the neurocomputational challenge in olfaction from combinatorial coding at the periphery: how does the brain discriminate different complex mixtures from widespread and overlapping receptor activation? The failure of previous reductionist structure-odor explanations is shown to reside in misconceptualizations of the critical causal elements involved. Causally fundamental features are not of parts independently of a mechanism. Components and their relevant features are units via their causal role within a mechanism. Here, new technologies allow revisiting our understanding of the ontology and levels of organization of a system.

Categorizing Smells: A Localist Approach

Humans are poorer at identifying smells and communicating about them, compared to other sensory domains. They also cannot easily organize odor sensations in a general conceptual space, where geometric distance could represent how similar or different all odors are. These two generalities are more or less accepted by psychologists, and they are often seen as connected: If there is no conceptual space for odors, then olfactory identification should indeed be poor. We propose here an important revision to this conclusion: We believe that the claim that there is no odor space is true only if by odor space, one means a conceptual space representing all possible odor sensations, in the paradigmatic sense used for instance for color. However, in a less paradigmatic sense, local conceptual spaces representing a given subset of odors do exist. Thus the absence of a global odor space does not warrant the conclusion that there is no olfactory conceptual map at all. Here we show how a localist account provides a new interpretation of experts and cross-cultural categorization studies: Rather than being exceptions to the poor olfactory identification and communication usually seen elsewhere, experts and cross-cultural categorization are here taken to corroborate the existence of local conceptual spaces.

Odor coding in the mammalian olfactory epithelium

Noses are extremely sophisticated chemical detectors allowing animals to use scents to interpret and navigate their environments. Odor detection starts with the activation of odorant receptors (ORs), expressed in mature olfactory sensory neurons (OSNs) populating the olfactory mucosa. Different odorants, or different concentrations of the same odorant, activate unique ensembles of ORs. This mechanism of combinatorial receptor coding provided a possible explanation as to why different odorants are perceived as having distinct odors. Aided by new technologies, several recent studies have found that antagonist interactions also play an important role in the formation of the combinatorial receptor code. These findings mark the start of a new era in the study of odorant-receptor interactions and add a new level of complexity to odor coding in mammals.

Development of a Korean Culture-Friendly Olfactory Function Test and Optimization of a Diagnostic Cutoff Value

Objectives

Cultural familiarity and safety must be considered when assessing olfactory ability. The YSK olfactory function (YOF) test is a new olfactory function test using culturally familiar odorants to Koreans.

Methods

The YOF test comprises three subtests for threshold (T), discrimination (D), and identification (I). The identification test included eight universal and four Korean culture-friendly odorants, which were selected considering eight major functional groups. Data were obtained from 1,127 subjects over 19 years old. Subjects were classified as having normosmia (n=542), hyposmia (n=472), and anosmia (n=113) by self-reported olfactory function. The YOF test and the Korean version of the Sniffin’ stick test (KVSS-II) were performed on the same day in random order. Diagnostic cutoffs for anosmia and hyposmia were calculated using the Youden index (J).

Results

The mean values for each T/D/I subtest and the total TDI score were as follows: normosmia (T, 4.6±2.3; D, 8.6±2.1; I, 11.1±1.7; TDI score, 24.2±4.5); hyposmia (T, 3.3±2.2; D, 7.1±2.5; I, 9.2±3.1; TDI score, 19.5±6.4); and anosmia (T, 1.7±1.2; D, 5.1±2.5; I, 5.0±3.2; TDI score, 11.8±5.6). The correlation coefficients between the YOF test and KVSS-II were 0.57, 0.65, 0.80, and 0.86 for T, D, I, and the TDI score, respectively (P<0.001). The diagnostic cutoffs were a TDI score ≤14.5 (J=0.67) for anosmia and 14.5(TDI score ≤21.0 (J=0.38) for hyposmia. The diagnostic efficacy of the YOF test (area under the curve [AUC], 0.88) was equivalent to that of the KVSS-II (AUC, 0.88; P=0.843; DeLong method).

Conclusion

The YOF test is a new olfactory test using safe and Korean culture-friendly odorants. It showed equivalent validity with the conventional olfactory function test. Furthermore, the YOF test provides information on the major functional groups of odorants, potentially enabling a more comprehensive interpretation for patients with olfactory disorders.

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.

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

The perception of aroma mixtures is based on interactions beginning at the peripheral olfactory system, but the process remains poorly understood. The perception of a mixture of ethyl isobutyrate (Et-iB, strawberry-like odor) and ethyl maltol (Et-M, caramel-like odor) was investigated previously in both human and animal studies. In those studies, the binary mixture of Et-iB and Et-M was found to be configurally processed. In humans, the mixture was judged as more typical of a pineapple odor, similar to allyl hexanoate (Al-H, pineapple-like odor), than the odors of the individual components. To explore the key features of this aroma blend, we developed an in silico approach based on molecules having at least one of the odors—strawberry, caramel or pineapple. A dataset of 293 molecules and their related odors was built. We applied the notion of a “social network” to describe the network of the odors. Additionally, we explored the structural properties of the molecules in this dataset. The network of the odors revealed peculiar links between odors, while the structural study emphasized key characteristics of the molecules. The association between “strawberry” and “caramel” notes, as well as the structural diversity of the “strawberry” molecules, were notable. Such elements would be key to identifying potential odors/odorants to form aroma blends.

Development of a Piezoelectric-Based Odor Reproduction System

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

Is It Possible to Predict the Odor of a Molecule on the Basis of its Structure?

The olfactory sense is the dominant sensory perception for many animals. When Richard Axel and Linda B. Buck received the Nobel Prize in 2004 for discovering the G protein-coupled receptors’ role in olfactory cells, they highlighted the importance of olfaction to the scientific community. Several theories have tried to explain how cells are able to distinguish such a wide variety of odorant molecules in a complex context in which enantiomers can result in completely different perceptions and structurally different molecules. Moreover, sex, age, cultural origin, and individual differences contribute to odor perception variations that complicate the picture. In this article, recent advances in olfaction theory are presented, and future trends in human olfaction such as structure-based odor prediction and artificial sniffing are discussed at the frontiers of chemistry, physiology, neurobiology, and machine learning.

A Critique of Olfactory Objects

Does the sense of smell involve the perception of odor objects? General discussion of perceptual objecthood centers on three criteria: stimulus representation, perceptual constancy, and figure-ground segregation. These criteria, derived from theories of vision, have been applied to olfaction in recent philosophical debates about psychology. An inherent problem with such framing of olfactory objecthood is that philosophers explicitly ignore the constitutive factors of the sensory systems that underpin the implementation of these criteria. The biological basis of odor coding is fundamentally different from the coding principles of the visual system. This article analyzes the three measures of perceptual objecthood against the biological background of the olfactory system. It contrasts the coding principles in olfaction with the visual system to show why these criteria of objecthood fail to be instantiated in odor perception. The argument demonstrates that olfaction affords perceptual categorization without the need to form odor objects.

Design of self-assembly dipeptide hydrogels and machine learning via their chemical features

Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure-property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.

Stochastic and Temporal Models of Olfactory Perception

Olfactory systems typically process signals produced by mixtures composed of very many natural odors, some that can be elicited by single compounds. The several hundred different olfactory receptors aided by several dozen different taste receptors are sufficient to define our complex chemosensory world. However, sensory processing by selective adaptation and mixture suppression leaves only a few perceptual components recognized at any time. Thresholds determined by stochastic processes are described by functions relating stimulus detection to concentration. Relative saliences of mixture components are established by relating component recognition to concentration in the presence of background components. Mathematically distinct stochastic models of perceptual component dominance in binary mixtures were developed that accommodate prediction of an appropriate range of probabilities from 0 to 1, and include errors in identifications. Prior short-term selective adaptation to some components allows temporally emergent recognition of non-adapted mixture-suppressed components. Thus, broadly tuned receptors are neutralized or suppressed by activation of other more efficacious receptors. This ‘combinatorial’ coding is more a process of subtraction than addition, with the more intense components dominating the perception. It is in this way that complex chemosensory mixtures are reduced to manageable numbers of odor notes and taste qualities.

The Odor Characterizations and Reproductions in Machine Olfactions: A Review

Machine olfaction is a novel technology and has been developed for many years. The electronic nose with an array of gas sensors, a crucial application form of the machine olfaction, is capable of sensing not only odorous compounds, but also odorless chemicals. Because of its fast response, mobility and easy of use, the electronic nose has been applied to scientific and commercial uses such as environment monitoring and food processing inspection. Additionally, odor characterization and reproduction are the two novel parts of machine olfaction, which extend the field of machine olfaction. Odor characterization is the technique that characterizes odorants as some form of general odor information. At present, there have already been odor characterizations by means of the electronic nose. Odor reproduction is the technique that re-produces an odor by some form of general odor information and displays the odor by the olfactory display. It enhances the human ability of controlling odors just as the control of light and voice. In analogy to visual and auditory display technologies, is it possible that the olfactory display will be used in our daily life? There have already been some efforts toward odor reproduction and olfactory displays.

How to be rational about empirical success in ongoing science: The case of the quantum nose and its critics

Empirical success is a central criterion for scientific decision-making. Yet its understanding in philosophical studies of science deserves renewed attention: Should philosophers think differently about the advancement of science when they deal with the uncertainty of outcome in ongoing research in comparison with historical episodes? This paper argues that normative appeals to empirical success in the evaluation of competing scientific explanations can result in unreliable conclusions, especially when we are looking at the changeability of direction in unsettled investigations. The challenges we encounter arise from the inherent dynamics of disciplinary and experimental objectives in research practice. In this paper we discuss how these dynamics inform the evaluation of empirical success by analyzing three of its requirements: data accommodation, instrumental reliability, and predictive power. We conclude that the assessment of empirical success in developing inquiry is set against the background of a model's interactive success and prospective value in an experimental context. Our argument is exemplified by the analysis of an apparent controversy surrounding the model of a quantum nose in research on olfaction. Notably, the public narrative of this controversy rests on a distorted perspective on measures of empirical success.