The trilogy of human musk receptors: linking receptor activation, genotype, and sensory perception.

The scent of musk plays a unique role in the history of perfumery. Musk odorants comprise 6 diverse chemical classes and perception differences in strength and quality among human panelists have long puzzled the field of olfaction research. Three odorant receptors (OR) had recently been described for musk odorants: OR5AN1, OR1N2, and OR5A2. High functional expression of the difficult-to-express human OR5A2 was achieved by a modification of the C-terminal domain and the link between sensory perception and receptor activation for the trilogy of these receptors and their key genetic variants was investigated: All 3 receptors detect only musky smelling compounds among 440 commercial fragrance compounds. OR5A2 is the key receptor for the classes of polycyclic and linear musks and for most macrocylic lactones. A single P172L substitution reduces the sensitivity of OR5A2 by around 50-fold. In parallel, human panelists homozygous for this mutation have around 40-60-fold higher sensory detection threshold for selective OR5A2 ligands. For macrocyclic lactones, OR5A2 could further be proven as the key OR by a strong correlation between in vitro activation and the sensory detection threshold in vivo. OR5AN1 is the dominant receptor for the perception of macrocyclic ketones such as muscone and some nitromusks, as panelists with a mutant OR5A2 are still equally sensitive to these ligands. Finally, OR1N2 appears to be an additional receptor involved in the perception of the natural (E)-ambrettolide. This study for the first time links OR activation to sensory perception and genetic polymorphisms for this unique class of odorants.

Recent advances in the contribution of glucosinolates degradation products to cruciferous foods odor: factors that influence degradation pathways and odor attributes.

As one of the most important vegetables and oils consumed globally, cruciferous foods are appreciated for their high nutritional value. However, there is no comprehensive knowledge to sufficiently unravel the "flavor mystery" of cruciferous foods. The present review provides a comprehensive literature on the recent advances regarding the contribution of glucosinolates (GSL) degradation products to cruciferous foods odor, which focuses on key GSL degradation products contributing to distinct odor of cruciferous foods (Brassica oleracea, Brassica rapa, Brassica napus, Brassica juncea, Raphanus sativus), and key factors affecting GSL degradation pathways (i.e., enzyme-induced degradation, thermal-induced degradation, chemical-induced degradation, microwave-induced degradation) during different processing and cooking. A total of 93 volatile GSL degradation products (i.e., 36 nitriles, 33 isothiocyanates, 3 thiocyanates, 5 epithionitriles, and 16 sulfides) and 29 GSL (i.e., 20 aliphatic, 5 aromatic, and 4 indolic) were found in generalized cruciferous foods. Remarkably, cruciferous foods have a distinctive pungent, spicy, pickled, sulfur, and vegetable odor. In general, isothiocyanates are mostly present in enzyme-induced degradation of GSL and are therefore often enriched in fresh-cut or low-temperature, short-time cooked cruciferous foods. In contrast, nitriles are mainly derived from thermal-induced degradation of GSL, and are thus often enriched in high-temperature, long-time cooked cruciferous foods.

Processing and cooking can cause degradation of glucosinolates and formation of volatiles.Structure­odor relationship of glucosinolates degradation products is discussed.Nitriles, isothiocyanates, and sulfides play an important role in cruciferous foods odor.Both enzyme- and thermal-induced degradation of glucosinolates is strongly pH-dependent.

Olfactory perception of chemically diverse molecules.

BACKGROUND: Understanding the relationship between a stimulus and how it is perceived reveals fundamental principles about the mechanisms of sensory perception. While this stimulus-percept problem is mostly understood for color vision and tone perception, it is not currently possible to predict how a given molecule smells. While there has been some progress in predicting the pleasantness and intensity of an odorant, perceptual data for a larger number of diverse molecules are needed to improve current predictions. Towards this goal, we tested the olfactory perception of 480 structurally and perceptually diverse molecules at two concentrations using a panel of 55 healthy human subjects. RESULTS: For each stimulus, we collected data on perceived intensity, pleasantness, and familiarity. In addition, subjects were asked to apply 20 semantic odor quality descriptors to these stimuli, and were offered the option to describe the smell in their own words. Using this dataset, we replicated several previous correlations between molecular features of the stimulus and olfactory perception. The number of sulfur atoms in a molecule was correlated with the odor quality descriptors "garlic," "fish," and "decayed," and large and structurally complex molecules were perceived to be more pleasant. We discovered a number of correlations in intensity perception between molecules. We show that familiarity had a strong effect on the ability of subjects to describe a smell. Many subjects used commercial products to describe familiar odorants, highlighting the role of prior experience in verbal reports of olfactory perception. Nonspecific descriptors like "chemical" were applied frequently to unfamiliar odorants, and unfamiliar odorants were generally rated as neither pleasant nor unpleasant. CONCLUSIONS: We present a very large psychophysical dataset and use this to correlate molecular features of a stimulus to olfactory percept. Our work reveals robust correlations between molecular features and perceptual qualities, and highlights the dominant role of familiarity and experience in assigning verbal descriptors to odorants.

Comprehensively assessing priority odorants emitted from swine slurry combining nontarget screening with olfactory threshold prediction.

The lack of one-to-one olfactory thresholds (OTs) poses an obstacle to the comprehensive assessment of priority odorants emitted from swine slurry using mass spectrometric nontarget screening. This study screened out highly performing quantitative structure-activity relationship (QSAR) models of OT prediction to complement nontarget screening in olfactory perception evaluation. A total of 27 compounds emitted at different slurry removal frequencies were identified and quantified using gas chromatography-mass spectrometry (GC-MS), including thiirane, dimethyl trisulfide (DMTS), and dimethyl tetrasulfide (DMQS) without OT records. Ridge regression (RR, R2 = 0.77, RMSE = 0.93, MAE = 0.73) and random forest regression (RFR, R2 = 0.76, RMSE = 0.97, MAE = 0.69) rather than the commonly used principal component regression (PCR) and partial least squares regression (PLSR) were used to assign OTs and assess the contributions of emerging volatile sulfur compounds (VSCs) to the sum of odor activity value (SOAV). Priority odorants were p-cresol (25.0-58.9 %) > valeric acid (8.3-31.7 %) > isovaleric acid (6.7-19.0 %) > dimethyl disulfide (4.7-15.7 %) > methanethiol (0-13.6 %) > isobutyric acid (0-8.6 %), whereas the contributions of three emerging VSCs were below 10 %. Vital olfactory active structures were identified by QSAR models as having high molecular polarity, high hydrophilicity, high charge quantity, flexible structure, high reactivity, and a high number of sulfur atoms. This protocol can be further extended to evaluate odor pollution levels for distinct odor sources and guide the development of pertinent deodorization technologies.

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (>99%) and high sensitivity (>99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost-PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship.Communicated by Ramaswamy H. Sarma.

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.

Understanding smell--the olfactory stimulus problem.

The main problem with sensory processing is the difficulty in relating sensory input to physiological responses and perception. This is especially problematic at higher levels of processing, where complex cues elicit highly specific responses. In olfaction, this relationship is particularly obfuscated by the difficulty of characterizing stimulus statistics and perception. The core questions in olfaction are hence the so-called stimulus problem, which refers to the understanding of the stimulus, and the structure-activity and structure-odor relationships, which refer to the molecular basis of smell. It is widely accepted that the recognition of odorants by receptors is governed by the detection of physico-chemical properties and that the physical space is highly complex. Not surprisingly, ideas differ about how odor stimuli should be classified and about the very nature of information that the brain extracts from odors. Even though there are many measures for smell, there is none that accurately describes all aspects of it. Here, we summarize recent developments in the understanding of olfaction. We argue that an approach to olfactory function where information processing is emphasized could contribute to a high degree to our understanding of smell as a perceptual phenomenon emerging from neural computations. Further, we argue that combined analysis of the stimulus, biology, physiology, and behavior and perception can provide new insights into olfactory function. We hope that the reader can use this review as a competent guide and overview of research activities in olfactory physiology, psychophysics, computation, and psychology. We propose avenues for research, particularly in the systematic characterization of receptive fields and of perception.