Target Odor Research Articles

Decoding Odor Mixtures in the Dog Brain: An Awake fMRI Study.

In working and practical contexts, dogs rely upon their ability to discriminate a target odor from distracting odors and other sensory stimuli. Using awake functional magnetic resonance imaging (fMRI) in 18 dogs, we examined the neural mechanisms underlying odor discrimination between 2 odors and a mixture of the odors. Neural activation was measured during the presentation of a target odor (A) associated with a food reward, a distractor odor (B) associated with nothing, and a mixture of the two odors (A+B). Changes in neural activation during the presentations of the odor stimuli in individual dogs were measured over time within three regions known to be involved with odor processing: the caudate nucleus, the amygdala, and the olfactory bulbs. Average activation within the amygdala showed that dogs maximally differentiated between odor stimuli based on the stimulus-reward associations by the first run, while activation to the mixture (A+B) was most similar to the no-reward (B) stimulus. To clarify the neural representation of odor mixtures in the dog brain, we used a random forest classifier to compare multilabel (elemental) versus multiclass (configural) models. The multiclass model performed much better than the multilabel (weighted-F1 0.44 vs. 0.14), suggesting the odor mixture was processed configurally. Analysis of the subset of high-performing dogs' brain classification metrics revealed a network of olfactory information-carrying brain regions that included the amygdala, piriform cortex, and posterior cingulate. These results add further evidence for the configural processing of odor mixtures in dogs and suggest a novel way to identify high-performers based on brain classification metrics.

Dynamics of odor sampling strategies in mice

Mammalian olfactory receptor neurons in the nasal cavity are stimulated by odorants carried by the inhaled air and their activation is therefore tied to and driven by the breathing or sniffing frequency. Sniffing frequency can be deliberately modulated to alter how odorants stimulate olfactory receptor neurons, giving the animal control over the frequency of odorant exposure to potentially aid odorant detection and discrimination. We monitored sniffing behaviors and odorant discrimination ability of freely-moving mice while they sampled either decreasing concentrations of target odorants or sampled a fixed target odorant concentration in the presence of a background of increasing odorant concentrations, using a Go-NoGo behavioral paradigm. This allowed us to ask how mice alter their odorant sampling duration and sampling (sniffing) frequency depending on the demands of the task and its difficulty. Mice showed an anticipatory increase in sniffing rate prior to odorant exposure and chose to sample for longer durations when exposed to odorants as compared to the solvent control odorant. Similarly, mice also took more odorant sampling sniffs when exposed to target odorants compared to the solvent control odorant. In general, odorant sampling strategies became more similar the more difficult the task was, e.g. the lower the target odorant concentration or the lower the target odorant contrast relative to the background odorant, suggesting that sniffing patterns are not preset, but are dynamically modulated by the particular task and its difficulty.

Molecular and sensory characterization of odorants in Cembran pine (Pinus cembra L.) from different geographic regions

The wood of the Cembran pine (Pinus cembra L.), also known as Swiss pine, Arolla pine or Stone pine, has a prominent and long-lasting odor which has been reported to relieve stress in humans. However, the odorants responsible for this are unknown and it is also unclear whether the odor profile depends on the origin of the trees, namely different geographical regions and pedoclimatic conditions. Therefore, in this study human sensory analyses together with isolation and analysis of the target odorants via solvent-assisted flavor evaporation were performed. Afterwards, comparative odor extract dilution analysis (cOEDA) was applied to investigate the odorous constituents of pinewood samples from forests in Austria and Italy. Specific odorants were thereby identified using one-dimensional and two-dimensional gas chromatography-mass spectrometry/olfactometry using two capillary columns with different polarities (DB-5 and DB-FFAP). In total, 103 odorants were detected with successful identification of 98 of these substances. The identified odorants were predominately monoterpenes and sesquiterpenes as well as organic acids. Several compounds including germacrene D, thymol, carvacrol, rotundone, β-caryophyllene, α-humulene, cinnamaldehyde, and ethyl cinnamate are reported here for the first time as odorous constituents of pinewood. Principal component analysis of the data obtained from sensory evaluation and cOEDA indicated that terpenes and sesquiterpenes were correlated with higher hedonic rates for the wood samples. Moreover, a Mantel test between the geographical distances of the trees and their sensory and cOEDA values demonstrated that the origin of the trees had a significant impact on the sensory characteristics of the wood, in agreement with the differences in the relative proportions of different volatile components. As such, the odor of Cembran pinewood is prone to variation but, yet, unique.

Characterizing Olfactory Function in Children with Autism Spectrum Disorder and Children with Sensory Processing Dysfunction.

Abnormalities in olfactory function have been identified in a number of neurological and psychiatric disorders, including Parkinson’s disease and schizophrenia. However, little is known about olfactory function in autism spectrum disorder (ASD). The present study aims to assess the olfactory profiles of children with ASD, compared to an age- and sex-matched comparison group of typically developing children and a second clinical control group consisting of non-ASD children with sensory processing dysfunction (SPD). Participants completed a battery of sensory and behavioral assessments including olfactory tasks (Sniffin’ Sticks Threshold Test and self-reported valence ratings for two target odorants (phenylethyl alcohol and vanillin) and the University of Pennsylvania Smell Identification Test), and an autism evaluation (Autism Diagnostic Observation Schedule-2). Children with ASD showed intact odor detection with reduced odor identification ability. Poor odor identification was significantly correlated with autism symptom severity. Children with SPD demonstrated reduced odor detection and identification ability. These findings provide evidence for differential patterns of smell processing among ASD and non-ASD neurodevelopmental disorders. Future studies are needed to determine whether the association of impaired olfaction and increased autism symptoms is due to shared etiology.

Effect of Handler Knowledge of the Detection Task on Canine Search Behavior and Performance.

Detection dogs are commonly trained and tested under conditions in which the handler or the evaluator knows the true presence or absence of a target odor. Previous research has demonstrated that when handlers are deceived and led to believe that a target odor is present, more false alerts occur. However, many detection teams operate under unknown conditions, and it remains unclear how handler knowledge (or lack thereof) of odor presence/absence influences the dog's behavior. The aim of this study was to evaluate if knowing the number of hides placed influenced detection dog performance in an applied search environment. Professional (n = 20) and sport (n = 39) detection handler-dog teams were asked to search three separate areas (area 1 had one hide, area 2 had one hide, area 3 was blank). Handlers in the Unknown Group were not told any information on the number of hides whereas the Known Group were told there was a total of two hides in the three areas. The sport Unknown Group spent a longer duration (69.04 s) searching in area 3 compared to the sport Known Group (p = 0.004). Further, sport dogs in the Unknown group looked back to the handler more frequently. When a miss did occur, dogs of both sport and professional handlers showed an increase interest in the location of the target odor compared to a comparison location. Critically, however, there was no difference in false alerts between the Known Group and Unknown Group for sport or professional handlers. In a second experiment, fourteen professional, and thirty-nine sport teams from Experiment 1 conducted an additional search double-blind and an additional search single-blind. Both sport and professional-handler dog teams had statistically similar accuracy rate under single and double blind conditions. Overall, when handlers knew the number of hides, it led to significant changes in search behavior of the detection team but did not influence the overall false alert rates.

Generalization and Discrimination of Molecularly Similar Odorants in Detection Canines and the Influence of Training

Operationally-deployed canine detectors are often trained on one or a limited number of materials representing a single target odor, and training frequently occurs using materials of a high purity grade in controlled scenarios with minimal other background odors. Conversely, in the field, canine detectors are expected to generalize and identify variations of the target odor, while discriminating from similar extraneous or background odors. This exemplifies the balance between generalization and discrimination required for effective canine detectors.This research explored the tendency for detection canines to generalize or discriminate between similar odorants. Two groups of related odorants were used in two separate studies; (1) odorants of similar functional groups with differing carbon chains, and (2) odorants of similar carbon chain length but differing functional groups. Within each odorant set, the effect of training was addressed by incrementally increasing the number of odorants each canine was trained to detect.Initially, discrimination increased with increasing molecular dissimilarity in both odorant groups. After further training on additional related odorants, generalization increased across the set of odorants of the same carbon chain length, but there were no significant changes in either generalization or discrimination across the set of odorants of the same functional group. The results suggest that the canines in this study were more likely to generalize across compounds of the same chain length with differing functional group than across compounds of the same functional group, but differing chain lengths. Furthermore, some variation in performance between individual canines indicated that the tendency to generalize differed with experience, breed, and other factors affecting olfaction.

Sensory Evaluation of Odor Approximation Using Nmf with Kullback-Leibler Divergence and Itakura-Saito Divergence in Mass Spectrum Space

Introduction The odor reproduction is achieved by approximating mass spectra of various odors by blending a set of odor components. These reproduced odors should be as close to the target odor as possible. The fidelity of odor reproduction should be as in the same manner as reproducing colors. Since no primary odors have been found, thus finding an appropriate set of odor components to perform odor reproduction is essential. The odor components should be kept as small as possible whereas it should cover a wide range of odors. In the present study, we performed a sensory evaluation of odors approximated with Kullback-Leibler and Itakura-Saito divergences on mass spectrum space. Method In our previous study, the non-negative matrix factorization (NMF) method was used to extract basis vector corresponding to odor components [1], [2]. NMF was performed to analyze the mass spectrum data space of essential oils as shown in Figure 1. Non-negativity property of NMF is useful in exploring odor components. We created a mass spectrum database of 185 essential oils originating from plants. Mass spectrum data were gathered at mass to charge (m/z) region from 50 to 250. Region below 50 was discarded because we diluted the samples with ethanol (molecular weight of 46). An example of essential oils' mass spectra that we gathered is shown in Figure 2.NMF with two different cost functions were performed in odor component exploration. Kullback-Leibler divergence (NMF-KL) and Itakura-Saito divergence (NMF-IS) were performed for the odor component exploration. IS divergence can extract small peaks in the high m/z region, with much contribution to human perception [3]. Those small peaks are ignored or degraded when using KL divergences [4]. Thus NMF-IS can enhance the approximation accuracy of the small peaks in high m/z area which might have a contribution to human olfaction.Non-negative least squares methods were performed to approximate the recipe for odor approximation. Non-negative least squares methods were applied twice. First one was used to obtain the recipe to approximate odor components explored by NMF and the second one to obtain the recipe to approximate the target odor based on odor components. Non-negative least squares with IS divergence based on optimization algorithm was applied to the result of NMF-IS, whereas normal non-negative least squares were applied to the result of NMF-KL. Results and Discussions The previous study revealed that 30 odor components worked well in approximating odor. We evaluated NMF-IS and NMF-KL only using 10 odor components. Although 10 odor components were insufficient for obtaining good approximation accuracy, the difference between the two methods became large. We extracted odor components from essential oils based on the recipe approximated by first non-negative least squares method.We approximated 6 target odors (clove bud, ylang-ylang, orange, origanum, mint) by using both NMF-IS and NMF-KL methods. Duo trio sensory test was performed to evaluate the approximated odors. A comparison of the original target odor and approximated odor mass spectrum are shown in Figure 3. The peaks locations were actually reproduced in spite of certain errors in magnitude even if we used only 10 odor components. A subject was asked to pick up either of the two approximated odor (one based on NMF-IS and another one based on NMF-KL) closer to the target one. We evaluated the Z score to the results of sensory tests (Table 1, P<0.05). The Z score of “+2” means the approximated odor by NMF-IS is significantly closer to the target odor whereas the Z score of “-2” means the approximated odor by NMF-KL is significantly closer to the target odor. The Z score of between +2 and -2 means none of approximated odor from both NMFs is closer to the target odor [5]. Three of the approximated odors (orange, mint, and ylang-ylang) by NMF-IS were closer to the target odor than that by NMF-KL. One approximated odor (cypress) by NMF-KL was closer to the target odor than that by NMF-IS. There was no significant closeness for the rest of two odors (clove bud and origanum). Overall, the sensory test reveals that approximated odors by NMF-IS were closer to the target odor than the odor approximated by NMF-KL. The Approximation quality based upon IS divergence was better than that based upon KL divergence.

Detecting Quality of Green Coffee Using an Electronic Nose System

Introduction Electronic noses are widely used in food science such as food freshness detection [1] and fruit ripeness classification [2]. Odor detection is one of the potential methods to identify the quality of coffee. Some researchers have used gas sensors to identify different stages of coffee roasting [3]. Another researcher has employed an electronic nose as a “cup test” tool [4]. In the aforementioned applications, the electronic nose serves to provide an objective solution. The quality identification of green coffee is very important during storage. The green coffee quality is easily affected by water vapor, which causes the green coffee getting mold. The coffee made from mold coffee bean induces bad taste and affects people health. Since the storage environment for green coffee is strict, there is a requirement to find a method to check the green coffee quality during the long storage. This paper presents an electronic nose system that can detect the quality of green coffee. In this study, the Arabica green coffee from different places of origin are used. These different kinds of green coffee are stored in a moist environment to observe the change of the coffee beans. The odor of the green coffee is detected by the electronic nose, and the result shows that the electronic nose has the potential of being a useful tool to discriminate whether the green coffee is suitable for drinking. Method The electronic nose system consists of a flow meter, a three-way valve, a pump and the gas sensor array, which contains 14 metal oxide sensors. The three-way valve controls the constant flow by a flow meter between baseline gas and the target sample. The sensor signals are acquired by a data acquisition card (USB-6343) and recorded with a laptop with National Instrument®LabVIEW Signal Express program. Figure.1(a) depicts the experiment setup.For the experiment sample, the origin Arabica green coffee beans from different places were prepared. The coffee beans were stored in an open and damp environment where the beans would mold over time. To sample the coffee odor, 30g green coffee beans were placed in a 60ml vial, and the vial was sealed for 30 minutes before the experiment. Dry air was used as the baseline for the system. Moreover, an empty vial was used to avoid the influence with and without vial. After the sensor array reached a steady-state, the coffee odor was pumped into the sensor chamber for 1.5 minutes that was long enough for the gas sensor array to react with the sample. To see the coffee beans’ change, the experiments were performed every several days, and the total experiment lasted for two or three weeks depending on the status of the beans. Results In this study, the quality of coffee was divided into fresh, sub-fresh and mold stages. These three stages were evaluated by the professional coffee maker based on the color and the shape of green coffee. Fig. 1(b, c, d) shows the different response curves of these three stages. In the sensor array, the resistance variation rate (ΔRs)/Rs0 is considered as features, where Rs0 represents the resistance of the sensor array in the baseline environment, and ΔRs represents the resistance difference of the sensor array in the baseline environment and the target odor. The odor patterns obtained from sensor responses were analyzed with principal component analysis (PCA), by which a distinctive profile between initial and the following days of storage was obtained. Fig.1(e) shows the PCA result. The PCA result shows a good correlation between the changing of gas composition and the storage time. Conclusions In this paper, the response curves of the green coffee samples with different storage times were obtained using a self-made electronic nose. The PCA results show that the electronic nose could distinguish the fresh green coffee, and the green coffee’s quality decay when stored in an inappropriate environment. The result also shows the electronic nose technology has the potential to meet the demands for rapid, low-cost, and nondestructive detection for the storage of green coffee.

Differences in the Search Behavior of Cancer Detection Dogs Trained to Have Either a Sit or Stand-Stare Final Response.

Recent literature has demonstrated that dogs have the potential to detect, and communicate the presence of, various human diseases. However, there is a lack of investigation into whether commonplace training differences within the field could influence a dog's behavior during a biomedical detection task. Here we report on the behavior of four dogs trained to alert to blood plasma samples taken from individuals with ovarian cancer. One hundred trials per dog were selected from routine video recordings collected over a period of 13 months. Videos were coded frame by frame to quantify sample checking, alerting behavior, and durations of alert. Dogs had previously been trained to elicit a final response behavior once they had located the target odor. Two dogs had a “sit” response while the other two had a “stand-stare” response. Alert behavior was categorized as true positive (a correct alert to a cancer sample) or false positive (an incorrect alert to biological and non-biological controls and distractors). Hesitations were also recorded, where the dog either checks the sample twice or, spends a longer duration of time sniffing the sample than a true pass without carrying out their final response. Results show individual variation in the total frequency of false alerts elicited. However, the rate of hesitations appears to be influenced by alert style, with stand-stare dogs carrying out 40 and 32, respectively (total = 72) and sit dogs carrying out 7 and 8, respectively (total = 15). The stand-stare dogs had a non-significant difference in the duration of their true and false positive alerts. In contrast, the sit dogs showed a significant difference (p < 0.001), maintaining their false alerts for, on average, two times the duration of their true alerts. Stand-stare dogs increased the duration of time spent in contact with the port when plasma samples were present, whereas sit dogs spent on average 0.3 s in contact with the port regardless of what sample type it contained. These findings suggest that the type of operant response a biomedical detection dog has been trained may influence their sample checking and response behavior.

Visual Priming Influences Olfactomotor Response and Perceptual Experience of Smells.

Whereas contextual influences in the visual and auditory domains have been largely documented, little is known about how chemical senses might be affected by our multisensory environment. In the present study, we aimed to better understand how a visual context can affect the perception of a rather pleasant (floral) and a rather unpleasant (damp) odor. To this end, 19 healthy participants performed a series of tasks including odor detection followed by perceptual evaluations of odor intensity, pleasantness, flowery, and damp characters of both odors presented at 2 different concentrations. A visual context (either congruent or incongruent with the odor; or a neutral control context) preceded odor stimulations. Olfactomotor responses as well as response times were recorded during the detection task. Results showed an influence of the visual context on semantic and motor responses to the target odors. First, congruency between context and odor increased the saliency of the olfactory feature of the memory trace, for the pleasant floral odor only (higher perceived flowery note). Clinical applications of this finding for olfactory remediation in dysosmic patients are proposed. Second, the unpleasant odor remained unaffected by visual primes, whatever the condition. In addition, incongruency between context and odor (regardless of odor type) had a disruptive effect on odor sampling behavior, which was interpreted as a protective behavior in response to expectancy violation. Altogether, this second series of effects may serve an adaptive function, especially the avoidance of, or simply vigilance toward, aversive and unpredictable stimuli.

Rapidly training African giant pouched rats (Cricetomys ansorgei) with multiple targets for scent detection

Since 1997, APOPO, a non-profit organization based in Tanzania, has deployed African giant pouched rats (Cricetomys ansorgei) to detect landmines in post-conflict areas. More recent research suggests the pouched rats can also be trained to detect tuberculosis in human sputum samples. Despite proven success on both tasks, the potential impact of each rat is limited by the required training time and constraint to a single target odor. The aim of this project was to establish a technique to rapidly train pouched rats to detect multiple odor targets. Eight pouched rats were trained to detect five unrelated target odors in Experiment 1. In addition to training duration, we measured maintenance of all odor targets. In Experiment 2, we examined response persistence under conditions of extinction. Experiment 3 investigated whether refresher sessions before tests would maintain detection accuracy. The animals mastered all odor targets in significantly fewer sessions than APOPO’s operational rats require to master a single target odor. Importantly, rats demonstrated strong discrimination between targets and non-targets despite the potential for interference and forgetting, suggesting pouched rats can be trained to detect at least five targets simultaneously. These results have the potential to increase the impact of each detection rat by both decreasing training time and expanding operational versatility, e.g., a single rat could be trained to detect multiple diseases.

The volatile organic compound profile from Cimex lectularius in relation to bed bug detection canines

Cimex lectularius are parasitic insects that feed on blood from a human host and are found in homes, hotels, libraries, and airports. Their behavior makes them difficult to prevent, detect, and/or treat. As well as traps and monitors, bed bug detection canines are routinely used to locate C. lectularius infestations. The role of the bed bug detector canines team has recently expanded as a potential forensic tool due to the ability to isolate human DNA from bed bugs up to three days after taking a blood meal. The effectiveness of the detector canine is in part dependent on access to target odor and for handlers understanding how to maximize target odor exposure. Little is known about the volatile organic compounds (VOCs) emitted by C. lectularius. In this study, different stages of C. lectularius were analyzed, including eggs, live adults, eggs and live adults combined, dead adults, and shed casings. VOC sampling using headspace solid-phase microextraction arrow (SPME Arrow) was performed and samples were analyzed by gas chromatography – mass spectrometry (GC–MS). The samples each had varying VOC profiles. Live and dead adult samples gave diverging profiles, providing a foundation for the success of bed bug detection canines in live targets. All samples containing eggs had the most dissimilar VOC profile. A slight increase in total VOCs was observed with increased insects present, but only after removal of volatile pheromone components. The identification of VOCs emitted by C. lectularius may help as scientific foundation to guide the development of detection canine training and certification.

Functional characterization of one sex pheromone receptor (AlucOR4) in Apolygus lucorum (Meyer-Dür)

Traps baited with female-produced sex pheromones have been very effective in the monitoring and management of mirid bugs in numerous field trials. However, none of the target odorant receptors for sex pheromone components in Apolygus lucorum have been identified. Here, we identified one candidate sex pheromone receptor, AlucOR4, from A. lucorum. Quantitative real-time PCR (qPCR) analysis revealed that AlucOR4 was antennae-enriched and male-biased in adult A. lucorum. Xenopus oocyte expression system assays demonstrated that AlucOR4/AlucOrco was sensitive to two major sex pheromone constituents and exhibited high sensitivity to (E)-2-hexenyl butyrate (E2HB) and lower sensitivity to hexyl butyrate (HB). The expression level of target mRNA was significantly reduced (>80%) in dsAlucOR4-injected bugs after five days. The electroantennogram (EAG) responses of male antennae to E2HB and HB were also reduced significantly (~40%). Our findings suggest that AlucOR4 is essential to sex pheromone perception in A. lucorum.

Abnormal Odor Detection in Electronic Nose via Self-Expression Inspired Extreme Learning Machine

The electronic nose (E-nose), as a metal oxide semiconductor gas sensor system coupled with pattern recognition algorithms, is developed for approximating artificial olfaction functions. Ideal gas sensors should be with selectivity, reliability, and cross-sensitivity to different odors. However, a new problem is that abnormal odors (e.g., perfume, alcohol, etc.) would show strong sensor response, such that they deteriorate the usual usage of E-nose for target odor analysis. An intuitive idea is to recognize abnormal odors and remove them online. A known truth is that the kinds of abnormal odors are countless in real-world scenarios. Therefore, general pattern classification algorithms lose effect because it is expensive and unrealistic to obtain all kinds of abnormal odors data. In this paper, we propose two simple yet effective methods for abnormal odor (outlier) detection: 1) a self-expression model (SEM) with l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> /l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularizer is proposed, which is trained on target odor data for coding and then a very few abnormal odor data is used as prior knowledge for threshold learning and 2) inspired by self-expression mechanism, an extreme learning machine (ELM) based self-expression (SE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> LM) is proposed, which inherits the advantages of ELM in solving a single hidden layer feed-forward neural network. Experiments on several datasets by an E-nose system fabricated in our laboratory prove that the proposed SEM and SE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> LM methods are significantly effective for real-time abnormal odor detection.

Olfactory Generalization in Detector Dogs.

Simple SummaryDogs are valued for their odor detection capabilities in a vast range of fields. They help to find hidden and elusive targets, such as explosives, narcotics, missing persons, and invasive or endangered species, amongst an extensive list. In all these roles, dogs are required to find real target odors that vary somewhat from those with which they were trained. For example, dogs might be trained with an explosive mixture or certain explosive compounds, and then must be able to find homemade explosives of differing compositions or manufacturing processes. This ability, to respond to similar odors in the same way as they would respond to the originally trained odor, is known as generalization. A failure to generalize can result in dogs missing targets in working scenarios. Although generalization is usually desired to some extent, dogs must also discriminate against related odors that are not targets. Therefore, research that investigates factors that can influence dogs’ tendency to generalize, and conversely to discriminate, can inform training strategies to improve detection outcomes. However, this field requires further research with greater application to practical training.Generalizing to target odor variations while retaining specificity against non-targets is crucial to the success of detector dogs under working conditions. As such, the importance of generalization should be considered in the formulation of effective training strategies. Research investigating olfactory generalization from pure singular compounds to more complex odor mixtures helps to elucidate animals’ olfactory generalization tendencies and inform ways to alter the generalization gradient by broadening or narrowing the range of stimuli to which dogs will respond. Olfactory generalization depends upon both intrinsic factors of the odors, such as concentration, as well as behavioral and cognitive factors related to training and previous experience. Based on the current research, some training factors may influence generalization. For example, using multiple target exemplars appears to be the most effective way to promote elemental processing and broaden the generalization gradient, whereas increasing the number of training instances with fewer exemplars can narrow the gradient, thereby increasing discrimination. Overall, this research area requires further attention and study to increase our understanding of olfactory generalization in dogs, particularly detector dogs, to improve training and detection outcomes.

A novel multi-odour identification by electronic nose using non-parametric modelling-based feature extraction and time-series classification

The electronic nose (e-nose) is an olfaction system that consists of an array of chemical sensors and effective machine learning algorithms for the detection of various target odours. Feature extraction and classification methods are of great importance in improving the performance of the e-nose system. In this paper, a novel odour identification method is presented. Firstly, we use the kernel-based system modelling approach to extract odour features. Its solution is a series of finite impulse responses which containing discriminant information of different odours. In addition, a parameter optimisation method based on normalised mean square error and information entropy is proposed to optimise the kernel function. The entropy is effective in preventing the finite impulse responses from overfitting. Multi-odour classification is achieved based on Gaussian mixture density hidden Markov model (GMM-HMM) considering the characteristic of the extracted features. Also, parameter selection for GMM-HMM is realised according to BIC index and cross-validation. Then, we validate the performance of the proposed feature extraction method in resistance to noise and compare it with other existed features. The modelling-based feature reached the highest performance even without applying any filtering or smoothing techniques. Finally, we compare the proposed combination of feature extraction and classification algorithms with other approaches. The proposed method outperformed other approaches reaching 93.56% in sensitivity and 98.71% in specificity. The results demonstrate that the proposed method is applicable in e-nose-based odour identification.

Testing ovarian cancer cell lines to train dogs to detect ovarian cancer from blood plasma: A pilot study

Ovarian cancer is known as a “silent killer” because of its nonspecific symptoms and generally late diagnosis due to the lack of reliable early detection tools. Medical detection canines have been shown to recognize the odor profile of malignant tumors of many types, including ovarian cancer, and may be utilized to help produce a much-needed early detection system for ovarian cancer. One significant drawback, however, is that to train detection canines on the odor signature of ovarian cancer, different samples or sets of samples must be used in every training session so that the canines do not begin to recognize an individual patient's odor signature or attune to patient-specific noncancerous olfactory cues. Therefore, numerous patient-derived samples must be obtained and stored for use in the training process. Ovarian cancer cell lines may be able to solve this obstacle, as they have low sample-to-sample variability and do not present patient-specific confounding olfactory cues. In this study, we attempted to train three medical detection dogs to alert to an ovarian cancer cell line (OVKATE) but not the culture media or nonmalignant cultured fallopian tube cells. Only one dog was able to discriminate between OVKATE and culture media and was able to be tested on other ovarian cancer cell lines (OVCAR-4, OVCAR-8, and SK-OV-3). The dog's responses suggest a common or related olfactory signature of malignant ovarian cancer cell lines. We then tested whether an OVKATE-trained dog recognized the blood plasma of patients with confirmed ovarian cancer. We did not find strong evidence that the dog recognized the blood plasma of a patient with confirmed ovarian cancer within three testing trials, with the dog only hesitating at the target odor, suggesting that while training on the cell lines may prepare medical dedication canines to recognize the blood plasma of patient with ovarian cancer, it is not a spontaneous switch to blood plasma. Despite this outcome, the behavior of the dog led us to believe that this method could help further the use of medical detection canines to inform the production of early detection tools for ovarian cancer, which would lead to earlier diagnoses and more favorable outcomes from therapy.

Competitive binding predicts nonlinear responses of olfactory receptors to complex mixtures

In color vision, the quantitative rules for mixing lights to make a target color are well understood. By contrast, the rules for mixing odorants to make a target odor remain elusive. A solution to this problem in vision relied on characterizing receptor responses to different wavelengths of light and subsequently relating these responses to perception. In olfaction, experimentally measuring receptor responses to a representative set of complex mixtures is intractable due to the vast number of possibilities. To meet this challenge, we develop a biophysical model that predicts mammalian receptor responses to complex mixtures using responses to single odorants. The dominant nonlinearity in our model is competitive binding (CB): Only one odorant molecule can attach to a receptor binding site at a time. This simple framework predicts receptor responses to mixtures of up to 12 monomolecular odorants to within 15% of experimental observations and provides a powerful method for leveraging limited experimental data. Simple extensions of our model describe phenomena such as synergy, overshadowing, and inhibition. We demonstrate that the presence of such interactions can be identified via systematic deviations from the competitive-binding model.

Odor lateralization and spatial localization: Null effects of blindness

People can navigate through an environment using different sensory information, including olfactory cues. Correct intranasal localization and external location of odors can be learned, and some people are able to lateralize olfactory stimuli above chance, which raises the question: What determines the spectrum of olfactory localization abilities. Here, we explored whether odor lateralization and localization abilities are increased in the course of sensory compensation. In a series of studies, we combined two different aspects of odor localization. Study 1 compared abilities of 69 blind people (Mage = 41 ± 1.6 years; 32 females) and 45 sighted controls (Mage = 38.3 ± 2.1 years; 25 females) to correctly lateralize eucalyptol, an odorant with a strong trigeminal component, presented to either nostril. Studies 2 and 3 involved a more ecologically valid task, namely spatial localization of olfactory stimuli. In Study 2, 13 blind individuals (Mage = 28.5 ± 3.5 years; seven females) and 16 sighted controls (Mage = 34.9 ± 3.2 years; ten females) tried to localize a single odorant, while in Study 3, 97 blind individuals (Mage = 43.1 ± .5 years; 48 females) and 47 sighted controls (Mage = 38.7 ± .7 years; 27 females) attempted to localize a single target odor in an experimental olfactory space comprising four different odorants. Blind and sighted subjects did not differ in their abilities to lateralize and to localize odors, and their performance across all tasks suggests that odor lateralization and localization are important for navigation in an environment regardless of visual status.

Simultaneous Monitoring of Canine Heart Rate and Respiratory Patterns During Scent Detection Tasks

Man-made technological odor detection systems continue to improve but still cannot match the tracking efficiency, mobility, and selectivity in the presence of interfering odors achieved by detector dogs. The reliability of dogs as olfactory detectors does not depend solely on their performance but also on the handler’s skill in interpreting the behavior of the dog that signals the detection of the target odor. We present our efforts on a wireless wearable system combining electrocardiogram (ECG) and an electronic stethoscope for direct monitoring of cardiopulmonary events in dogs toward enabling cybernetic dog–machine interfaces. This is, to our knowledge, the first cyber-physical attempt to simultaneously record heart rate, heart rate variability, and continuous auscultation of respiratory behavior in a wearable form factor during scent-detection tasks. In this paper, we present: 1) a method to automate the detection of sniffing episodes based on power spectral density of the respiratory sounds; 2) proof-of-concept extraction and quantification of the ECG and respiratory sounds features that would enable the discovery of physiological patterns associated to scent-detection tasks; and 3) proof-of-concept correlation of such patterns with the presence/absence of target odors. These contributions pave the way for a novel real-time cybernetic olfactory detection monitoring system to provide decision support for handlers in the field in addition to enabling future computer-sniffing dog interfaces.