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Emissions of volatile organic compounds (VOCs) in vehicles represent a significant problem, causing unpleasant odors. To mitigate VOCs and odors in vehicles, it is critical to choose interior parts with low odor and VOC emissions. However, prevailing odor evaluation methods are subjective, costly, and potentially harmful to the health of evaluators. In this study, we analyzed 139 automotive interior parts and 92 vehicles, establishing a cost-effective, data-driven method for odor evaluation. The contents of benzene, toluene, ethylbenzene, xylene, styrene, formaldehyde, acetaldehyde, acrolein, and total volatile organic compounds (TVOC) were detected by thermal desorption gas chromatography-mass spectrometry (TD-GC/MS) and high-performance liquid chromatography with an ultraviolet detector (HPLC–UV). Professional odor evaluators assessed the odors, identifying intensity levels from 2.0 to 4.5 in interior parts and 2.5 to 3.5 in whole vehicles. Leveraging this data, we applied four supervised learning algorithms to develop predictive models for the odor intensity of both interior parts and entire vehicles. During model training, we implemented early stopping techniques for the artificial neural network (ANN) and convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) models, while optimizing the support vector machine (SVM) and extreme gradient boosting (XGBoost) models using the GridSearch algorithm. The evaluation results reveal that the CNN-BiLSTM model performs the best, achieving an average accuracy of 89% for unknown samples within an odor intensity level of 0.5. The root mean square error (RMSE) is 0.24, and the mean absolute error (MAE) is 0.08. The model also underwent a sevenfold cross-validation, achieving an accuracy of 83.43%. Additionally, we employed SHapley Additive exPlanations (SHAP) for the interpretative analysis of the model, which confirmed the consistency of each VOC’s odor contribution with human olfactory rules. By predicting odors based on VOCs through supervised learning, this study reduces the costs and enhances the efficiency and applicability of odor assessment across various vehicle interiors.

Government agencies responsible for ensuring healthful water and/or air quality are often faced with resolving public complaints of nuisance odors. Understanding variations in odor intensity may ultimately lead to the establishment and application by such agencies of quantitative limits for effective odorant control. An odor panel was trained in suprathreshold odor intensity evaluation using both the ASTM Method E544 (Butanol Method) and the APHA Method 2170 (Flavor Profile Analysis (FPA) Method). A linear mixed model was fitted to the panel data, taking into account the fixed effects of concentration levels and the random effects of panelists and sessions. The FPA method proved easier to administer and revealed less inter-session variance than the ASTM Method, suggesting its greater utility in applications involving odor panels. For both methods, there was a high standard deviation, relative to the mean. This finding indicates that the intensity scales may be useful for understanding relative odor intensities, but should not be used as a precise measure, or as a basis for establishing regulatory limits.

In insects, the search for food is highly dependent on olfactory sensory input. Here, we investigated whether a single key odorant within an odor blend or the complexity of the odor blend influences the attraction of to a food source. A key odorant is defined as an odorant that elicits a difference in the behavioral response when two similar complex odor blends are offered. To validate that the observed behavioral responses were elicited by olfactory stimuli, we used olfactory co-receptor mutants. We show that within a food odor blend, ethanol functions as a key odorant. In addition to ethanol other odorants might serve as key odorants at specific concentrations. However, not all odorants are key odorants. The intensity of the odor background influences the attractiveness of the key odorants. Increased complexity is only more attractive in a concentration-dependent range for single compounds in a blend. Orco is necessary to discriminate between two similarly attractive odorants when offered as single odorants and in food odor blends, supporting the importance of single odorant recognition in odor blends. These data strongly indicate that flies use more than one strategy to navigate to a food odor source, depending on the availability of key odorants in the odor blend and the alternative odor offered.

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors (“garlic,” “fish,” “sweet,” “fruit,” “burnt,” “spices,” “flower,” and “sour”). Regularized linear models performed nearly as well as random forest–based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule.

Humans use a family of more than 400 olfactory receptors (ORs) to detect odors, but there is currently no model that can predict olfactory perception from receptor activity patterns. Genetic variation in human ORs is abundant and alters receptor function, allowing us to examine the relationship between receptor function and perception. We sequenced the OR repertoire in 332 individuals and examined how genetic variation affected 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. Genetic variation in a single OR was frequently associated with changes in odorant perception, and we validated 10 cases in which in vitro OR function correlated with in vivo odorant perception using a functional assay. In 8 of these 10 cases, reduced receptor function was associated with reduced intensity perception. In addition, we used participant genotypes to quantify genetic ancestry and found that, in combination with single OR genotype, age, and gender, we can explain between 10% and 20% of the perceptual variation in 15 olfactory phenotypes, highlighting the importance of single OR genotype, ancestry, and demographic factors in the variation of olfactory perception.

The ability to represent both stimulus identity and intensity is fundamental for perception. Using large-scale population recordings in awake mice, we find distinct coding strategies facilitate non-interfering representations of odor identity and intensity in piriform cortex. Simply knowing which neurons were activated is sufficient to accurately represent odor identity, with no additional information about identity provided by spike time or spike count. Decoding analyses indicate that cortical odor representations are not sparse. Odorant concentration had no systematic effect on spike counts, indicating that rate cannot encode intensity. Instead, odor intensity can be encoded by temporal features of the population response. We found a subpopulation of rapid, largely concentration-invariant responses was followed by another population of responses whose latencies systematically decreased at higher concentrations. Cortical inhibition transforms olfactory bulb output to sharpen these dynamics. Our data therefore reveal complementary coding strategies that can selectively represent distinct features of a stimulus.

•We characterized the impacts of different odors on cortical arousal and attention.•An odor imparts its imprint on the mind state by orchestrating neural oscillations.•Odors differently modulate the small-worldness of brain network architecture.•The effects transcend the perceptual attributes of odor intensity and pleasantness.•Arousal is a unique dimension in olfactory space distinct from odor intensity. Throughout history, various odors have been harnessed to invigorate or relax the mind. The mechanisms underlying odors’ diverse arousal effects remain poorly understood. We conducted five experiments (184 participants) to investigate this issue, using pupillometry, electroencephalography, and the attentional blink paradigm, which exemplifies the limit in attentional capacity. Results demonstrated that exposure to citral, compared to vanillin, enlarged pupil size, reduced resting-state alpha oscillations and alpha network efficiency, augmented beta-gamma oscillations, and enhanced the coordination between parietal alpha and frontal beta-gamma activities. In parallel, it attenuated the attentional blink effect. These effects were observed despite citral and vanillin being comparable in perceived odor intensity, pleasantness, and nasal pungency, and were unlikely driven by semantic biases. Our findings reveal that odors differentially alter the small-worldness of brain network architecture, and thereby brain state and arousal. Furthermore, they establish arousal as a unique dimension in olfactory space, distinct from intensity and pleasantness.

•Reduced brain responses to pleasant odor, but not to neural or unpleasant odor, after acute stress.•Activation in the amygdala/piriform, OFC, and insula in response to the pleasant odor was correlated with stress-related cortisol responses.•Increased right piriform-insula coupling in response to pleasant odor was correlated with subjective stressful ratings after stress. Acute stress alters olfactory perception. However, little is known about the neural processing of olfactory stimuli after acute stress exposure and the role of cortisol in such an effect. Here, we used an event-related olfactory fMRI paradigm to investigate brain responses to odors of different valence (unpleasant, pleasant, or neutral) in healthy young adults following an acute stress (Trier Social Stress Test, TSST) induction (N = 22) or a non-stressful resting condition (N = 22). We obtained the odor pleasantness, intensity, and familiarity ratings after the acute stress induction or resting condition. We also measured the participants' perceived stress and salivary cortisol at four time points during the procedure. We found a stress-related decrease in brain activation in response to the pleasant, but not to the neutral or unpleasant odor stimuli in the right piriform cortex extending to the right amygdala, the right orbitofrontal cortex, and the right insula. In addition, activation of clusters within the regions of interest were negatively associated with individual baseline-to-peak increase in salivary cortisol levels after stress. We also found increased functional connectivity between the right piriform cortex and the right insula after stress when the pleasant odor was presented. The strength of the connectivity was positively correlated with increased perceived stress levels immediately after stress exposure. These results provide novel evidence for the effects of acute stress in attenuating the neural processing of a pleasant olfactory stimulus. Together with previous findings, the effect of acute stress on human olfactory perception appears to depend on both the valence and the concentration (e.g., peri-threshold or suprathreshold levels) of odor stimuli.

Others are trying to digitize smell to build new technologies: devices that diagnose disease on the basis of odours; better, safer insect repellents; and affordable or more-effective aroma molecules for the US$30-billion flavour and fragrance market. Researchers have come up with a few computational models that can relate structure to odour, but early versions tended to be based on quite narrow data sets or could only make predictions when smells had been calibrated to have the same perceived intensity. A master perfumer, asked to describe the same smell, noted 'ski lodge, fireplace without a fire'. Using cryo-electron microscopy, the team looked at how propionate

Cannabis flower odour is an important aspect of product quality as it impacts the sensory experience when administered, which can affect therapeutic outcomes in paediatric patient populations who may reject unpalatable products. However, the cannabis industry has a reputation for having products with inconsistent odour descriptions and misattributed strain names due to the costly and laborious nature of sensory testing. Herein, we evaluate the potential of using odour vector modelling for predicting the odour intensity of cannabis products. Odour vector modelling is proposed as a process for transforming routinely produced volatile profiles into odour intensity (OI) profiles which are hypothesised to be more informative to the overall product odour (sensory descriptor; SD). However, the calculation of OI requires compound odour detection thresholds (ODT), which are not available for many of the compounds present in natural volatile profiles. Accordingly, to apply the odour vector modelling process to cannabis, a QSPR statistical model was first produced to predict ODT from physicochemical properties. The model presented herein was produced by polynomial regression with 10-fold cross-validation from 1,274 median ODT values to produce a model with R 2 = 0.6892 and a 10-fold R 2 = 0.6484. This model was then applied to terpenes which lacked experimentally determined ODT values to facilitate vector modelling of cannabis OI profiles. Logistic regression and k-means unsupervised cluster analysis was applied to both the raw terpene data and the transformed OI profiles to predict the SD of 265 cannabis samples and the accuracy of the predictions across the two datasets was compared. Out of the 13 SD categories modelled, OI profiles performed equally well or better than the volatile profiles for 11 of the SD, and across all SD the OI data was on average 21.9% more accurate (p = 0.031). The work herein is the first example of the application of odour vector modelling to complex volatile profiles of natural products and demonstrates the utility of OI profiles for the prediction of cannabis odour. These findings advance both the understanding of the odour modelling process which has previously only been applied to simple mixtures, and the cannabis industry which can utilise this process for more accurate prediction of cannabis odour and thereby reduce unpleasant patient experiences.