Machine Olfaction Research Articles

Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications.

The escalating development and improvement of gas sensing ability in industrial equipment, or "machine olfactory", propels the evolution of gas sensors toward enhanced sensitivity, selectivity, stability, power efficiency, cost-effectiveness, and longevity. Two-dimensional (2D) materials, distinguished by their atomic-thin profile, expansive specific surface area, remarkable mechanical strength, and surface tunability, hold significant potential for addressing the intricate challenges in gas sensing. However, a comprehensive review of 2D materials-based gas sensors for specific industrial applications is absent. This review delves into the recent advances in this field and highlights the potential applications in industrial machine olfaction. The main content encompasses industrial scenario characteristics, fundamental classification, enhancement methods, underlying mechanisms, and diverse gas sensing applications. Additionally, the challenges associated with transitioning 2D material gas sensors from laboratory development to industrialization and commercialization are addressed, and future-looking viewpoints on the evolution of next-generation intelligent gas sensory systems in the industrial sector are prospected.

Advancing cross-subject olfactory EEG recognition: A novel framework for collaborative multimodal learning between human-machine

Odor sensory evaluation is broadly applied in food, clothing, cosmetics, and other fields. Traditional artificial sensory evaluation has poor repeatability, and the machine olfaction represented by the electronic nose (E-nose) is difficult to reflect human feelings. Olfactory electroencephalogram (EEG) contains features associated with human olfactory preference, which has unique advantages in odor sensory evaluation. However, the difficulty of cross-subject olfactory EEG recognition dramatically limits its application. In this paper, a human–machine collaborative multimodal learning method is proposed for cross-subject olfactory preference recognition. Firstly, the olfactory EEG and E-nose multimodal data acquisition and preprocessing paradigms are established. Secondly, a complementary multimodal data mining strategy is proposed to effectively mine the individual and common features representing subjects' olfactory preferences from multimodal data. Finally, the cross-subject olfactory preference recognition is achieved in 24 subjects by fusing the extracted common and individual features, and the recognition effect is superior to the state-of-the-art recognition methods. Furthermore, the advantages of the proposed method in cross-subject olfactory preference recognition indicate its potential for practical odor evaluation applications.

The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction

Soil classification stands as a pivotal aspect in the domain of agricultural practices and environmental research, wielding substantial influence over decisions related to real-time soil management and precision agriculture. Nevertheless, traditional methods of assessing soil conditions, primarily grounded in labor-intensive chemical analyses, confront formidable challenges marked by substantial resource demands and spatial coverage limitations. This study introduced a machine olfaction methodology crafted to emulate the capabilities of the human olfactory system, providing a cost-effective alternative. In the initial phase, volatile gases produced during soil pyrolysis were propelled into a sensor array comprising 10 distinct gas sensors to monitor changes in gas concentration. Following the transmission of response data, nine eigenvalues were derived from the response curve of each sensor. Given the disparate sample counts for the two distinct classification criteria, this computational procedure yields two distinct eigenspaces, characterized by dimensions of 112 or 114 soil samples, each multiplied by 10 sensors and nine eigenvalues. The determination of the optimal feature space was guided by the “overall feature information” derived from mutual information. Ultimately, the inclusion of random forest (RF), multi-layer perceptron (MLP), and multi-layer perceptron combined with random forest (MLP-RF) models was employed to classify soils under four treatments (tillage and straw management) and three fertility grades. The assessment of model performance involved metrics such as overall accuracy (OA) and the Kappa coefficient. The findings revealed that the optimal classification model, MLP-RF, achieved impeccable performance with an OA of 100.00% in classifying soils under both criteria, which showed almost perfect agreement with the actual results. The approach proposed in this study provided near-real-time data on the condition of the soil and opened up new possibilities for advancing precision agriculture management.

Optimal gas sensor combination selection method for low cost machine olfaction applicated in food discrimination

In this paper an electronic nose system is proposed, together with odor-related gas information data processing and recognizing methods and an optimal gas sensor combination selecting procedure. The constructed electronic nose system consists of three parts, including a gas sensor chamber, data collecting circuits loaded with gas sensors and odor-related information data processing AI-driving programs. Utilizing the constructed electronic nose system, we accomplish the classifying missions of most 7 fruits and 8 vegetables to classifying accuracy of 96.7% using SVM classification algorithm and 10 sensor units. All probable gas sensor combinations are checked by the standard of their classifying performance to find the gas sensor combination match best when working together. The outcome turned to find that a specified gas sensor combination composed of 4 sensor units work best in the fruits and vegetables classification. This outcome suggests that these 4 sensor units are most suitable utilizing in related fruits and vegetables classification mission.

Online drift compensation framework based on active learning for gas classification and concentration prediction

Sensor drift is an urgent issue in the machine olfaction community. To date, most studies have focused on gas classification tasks based on an offline method, while neglecting concentration prediction and labeling cost. To permit multitasking including sensor drift, gas classification, concentration prediction, and labeling cost, this paper presents a novel online drift compensation framework based on active learning. Specifically, a Query Strategy for Gas Classification (QSGC) and a Query Strategy for Concentration Prediction (QSCP) are designed respectively, and an Online Domain-adaptive Extreme Learning Machine (ODELM) is proposed. First, the QSGC/QSCP is employed to select the most valuable samples for labeling in the gas classification task/concentration prediction task. Second, the ODELM utilizes only one labeled sample to update the prediction model, and thus adapts to evolving sensor drift. The proposed framework is compared with several state-of-the-art methods. Experimental results demonstrate that the proposed method achieves the best generalization ability with the minimum labeling cost.

Artificial Olfactory System for Distinguishing Oil-Contaminated Soils

Oil-contaminated soils are a major environmental problem for Kazakhstan. Oil spills or leaks lead to profound changes in the physical and agrochemical properties of the soil and the accumulation of hazardous substances. Whilst there are many remote sensing techniques and complex laboratory methods for oil spill detection, developing simple, reliable, and inexpensive tools for detecting the presence of pollutants in the soil is a relevant research task. The study aims to research the possibilities of an electronic nose combining a chemical sensor array with pattern recognition techniques to distinguish volatile organic compounds from several types of hydrocarbon soil pollutants. An electronic nose system was assembled in our laboratory. It includes eight gas metal oxide sensors, a humidity and temperature sensor, an analog-digital processing unit, and a data communication unit. We measured changes in the electrical conductivity of sensors in the presence of volatile organic compounds released from oil and petroleum products and samples of contaminated and uncontaminated soils. The list of experimental samples includes six types of soils corresponding to different soil zones of Kazakhstan, crude oil from three oil fields in Kazakhstan, and five types of locally produced fuel oil (including gasoline, kerosene, diesel fuel, engine oil, and used engine oil). We used principal component analysis to statistically process multidimensional sensor data, feature extraction, and collect the volatile fingerprint dataset. Pattern recognition using machine learning algorithms made it possible to classify digital fingerprints of samples with an average accuracy of about 92%. The study results show that electronic nose sensors are sensitive to soil hydrocarbon content. The proposed approach based on machine olfaction is a fast, accurate, and inexpensive method for detecting oil spills and leaks, and it can complement remote sensing methods based on computer vision.

Capacitive polymer sensors: Factors influencing performance and design principles

Capacitive polymer sensors present a promising approach for machine olfaction arrays capable of detecting and classifying various analytes. In this study, a comprehensive analysis of factors influencing capacitive vapor sensors is performed. An in-depth analysis of performance of sensing polymers with a wide range of solubility interactions and dielectric constants is performed providing a foundation for sensor design principles. Unique frequency-dependent signal-to-noise behavior was observed for each polymer-analyte pair due to variations in polarization mechanisms, which was in turn used to tune the array for a better sensitivity. Film thickness and background humidity on array sensitivity was also explored. Design principles based on this study are used to optimize sensor arrays for the classification and quantification of analytes exhibiting distinct solubility interactions (alcohol, ketone, ester, and two hydrocarbons). Electrochemical impedance spectroscopy analysis provides valuable insight into the different physical processes and polarization mechanisms for each sensing polymer. The design principles explored have broad implications for other dielectric-based sensing systems, including integrated circuit-based arrays such as field-effect transistors.

Machine olfaction discrimination of Spanish-style green olives inoculated with spoilage mold species

As an alternative to traditional sensory analysis evaluation, machine olfaction represents a valuable tool for detecting incipient defects caused by microorganisms. For this purpose, different strains of spoilage molds were inoculated into Spanish-style table olives and the effect on their sensory quality and volatile profile were analyzed using an electronic nose (E-nose). The main defects obtained for the different inoculated microorganisms associated with abnormal fermentation were mold and humidity. A total of 36 volatile compounds were identified and classified into phenolics, alcohols and carboxylic acids, while minor ones included derivatives of acids and oxygenated compounds. In general, a decrease in the concentrations of acetic acid, phenolic compounds and creosol was observed in olives inoculated with mould strains. However, propanoic acid, 2-methoxy-phenol, 2,4-dimethyl-heptane, among others, increased after mold inoculation. Sensorial and volatile compound analysis showed the table olives inoculated with the strains of Aspergillus flavus A.F.18 and Penicillium expansum P.E.20 to be the most altered. The E-nose data were able to classify the inoculated olives into different categories and distinguish them from the control treatment, regardless the intensity of the defect. The principal component analysis (PCA) of table olives inoculated showed that 70.23% of the total variance of data was explained by PC1 and 23.27% by PC. The sum of the elements of the diagonal of the confusion matrix gives the percentage of success in discrimination of 95.5%. Therefore, these results show the capacity and precision of E-nose to discriminate between the alterations caused by different mold strains.

Canine-inspired Unidirectional Flows for Improving Memory Effects in Machine Olfaction.

A dog's nose differs from a human's in that air does not change direction but flows in a unidirectional path from inlet to outlet. Previous simulations showed that unidirectional flow through a dog's complex nasal passageways creates stagnant zones of trapped air. We hypothesize that these zones give the dog a "physical memory," which it may use to compare recent odors to past ones. In this study, we conducted experiments with our previously built Gaseous Recognition Oscillatory Machine Integrating Technology (GROMIT) and performed corresponding simulations in two dimensions. We compared three settings: a control setting that mimics the bidirectional flow of the human nose; a short-circuit setting where odors exit before reaching the sensors; and a unidirectional configuration using a dedicated inlet and outlet that mimics the dog's nose. After exposure to odors, the sensors in the unidirectional setting showed the slowest return to their baseline level, indicative of memory effects. Simulations showed that both short-circuit and unidirectional flows created trapped recirculation zones, which slowed the release of odors from the chamber. In the future, memory effects such as the ones found here may improve the sensitivity and utility of electronic noses.

Intelligent Perception of Multiaroma Types Based on Machine Olfaction

Machine olfactory perception (MOP) is a new type of intelligent perception technology that senses and describes odors. The difference between MOP and machine olfaction is that MOP describes odors by using some semantic labels. One of the challenges in realizing MOP is an appropriate multilabel classifier from the high-dimensional sensor responses. Here, we propose a novel aroma-type (AT) oriented MOP method, which is an MOP method of multiple AT. Wherein, the method designed an intelligent perception of multiple ATs based on a convolutional neural network (IPMAT-CNN), in which an olfactory information-shared mechanism was introduced to enhance the feature extraction of the network. In addition, by analyzing the molecular correlation between multiple ATs, a matrix addition method was designed in IPMAT-CNN to strengthen the information on adjacent ATs. These designs effectively improved the accuracy of model detection. At last, IPMAT-CNN successfully sensed and recognized seven ATs of 38 monomer flavors. The effectiveness of IPMAT-CNN was evaluated by four baseline models [classifier chain based on random forest classifier (CC-RF), label powerset based on random forest classifier (LP-RF), multilabel decision tree (ML-DT), and multilabel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor (ML-kNN)]. The results showed that the IPMAT-CNN had the best performance with a 0.0636 hamming loss, a 93.65% accuracy, and a 0.8149 mean average precision (mAP) value for classifying the seven ATs. It reveals that the IPMAT-CNN had a decent prediction performance and can be used as an effective reference method for the ATs classification.

An Improved Algorithm of Drift Compensation for Olfactory Sensors

This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. For this reason, we propose a domain transformation semi-supervised weighted kernel extreme learning machine (DTSWKELM) algorithm, which converts the data through the domain and uses SWKELM algorithmic classification to transform the semi-supervised classification problem of different domain data into a semi-supervised classification problem of the same domain data.

Grape Cultivar Identification and Classification by Machine Olfaction Analysis of Leaf Volatiles

Development of electronic technologies for precise identification of fruit crop cultivars in agricultural production provides an effective means for assuring product quality and authentication. The capabilities of discriminating between grape (Vitis vinifera L.) cultivars is essential for assuring certification of varieties sold in world markets. Machine olfaction, based on electronic-nose (e-nose) technologies, is readily available for rapid identification of fruit and vegetative agricultural products. This technology relies on detection of and discrimination between volatile organic compound (VOC) emissions from plant parts. It may be used in all stages of agricultural production to facilitate crop maintenance, cultivation, and harvesting decisions prior to marketing. An experimental e-nose device was constructed and tested in combination with five chemometric methods, including PCA, LDA, QDA, SVM, and ANN, as rapid, non-destructive tools for identification and classification of grape cultivars. An e-nose instrument equipped with nine metal oxide semiconductor (MOS) sensors was utilized to identify and classify five grape cultivars based on leaf VOC emissions using supervised and non-supervised methods. Grape leaf samples were first identified as belonging to specific cultivar types using PCA analyses, which are non-supervised classification methods, with the first two principal components (PC-1 and PC-2) accounting for 89% of the total variance. Four supervised statistical methods were further tested, including DA, QDA, SVM, and ANN, and provided effective discrimination accuracies of 98%, 99%, 92%, and 99%, respectively. These findings confirmed the suitable applicability of an MOS e-nose sensor array with supervised methods for accurate identification of grape cultivars, which is useful for authentication of vine cultivar types for commercial markets.

Machine Olfaction to Evaluate the Stability of the Odor Profile of Pancakes Enriched with Docosahexaenoic Acid and Anthocyanins

Increasing consumer awareness on good health has drawn the attention to health promoting natural dietary substances. However, since the organoleptic profile of foods highly influences the consumers’ preference, and it is often decisive in the purchase, it is important to objectively describe and evaluate the effect of the applied bioactive ingredients on aroma. In this study, pancakes enriched with docosahexaenoic acid and anthocyanins were tested with electronic sensor array technology against control products. Samples were analyzed with an Alpha MOS FOX4000 electronic nose (EN) after 20 to 297 days of frozen storage at −20 °C. Multivariate analysis of the acquired EN data showed a strong relation between the number of days that samples were stored and the odor describing sensor signals of enriched samples (R2 = 0.59), but the observed relation was broken in the case of control (not enriched) samples (R2 = 0.08). When a supervised classification of enriched and control samples was done, the ratio of correctly identified samples in cross-validation was 95.1% at short-term storage (< 140 days), while the hit rate dropped to 80.4% at prolonged storage (> 140 days). This signified the existing but less intensive odor differences. The electronic nose technology was proven to be applicable in the characterization of one type of bioactive-enriched foods, while it was also useful in the monitoring of odor alterations during storage.

A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons.

Growth-transform (GT) neurons and their population models allow for independent control over the spiking statistics and the transient population dynamics while optimizing a physically plausible distributed energy functional involving continuous-valued neural variables. In this paper we describe a backpropagation-less learning approach to train a network of spiking GT neurons by enforcing sparsity constraints on the overall network spiking activity. The key features of the model and the proposed learning framework are: (a) spike responses are generated as a result of constraint violation and hence can be viewed as Lagrangian parameters; (b) the optimal parameters for a given task can be learned using neurally relevant local learning rules and in an online manner; (c) the network optimizes itself to encode the solution with as few spikes as possible (sparsity); (d) the network optimizes itself to operate at a solution with the maximum dynamic range and away from saturation; and (e) the framework is flexible enough to incorporate additional structural and connectivity constraints on the network. As a result, the proposed formulation is attractive for designing neuromorphic tinyML systems that are constrained in energy, resources, and network structure. In this paper, we show how the approach could be used for unsupervised and supervised learning such that minimizing a training error is equivalent to minimizing the overall spiking activity across the network. We then build on this framework to implement three different multi-layer spiking network architectures with progressively increasing flexibility in training and consequently, sparsity. We demonstrate the applicability of the proposed algorithm for resource-efficient learning using a publicly available machine olfaction dataset with unique challenges like sensor drift and a wide range of stimulus concentrations. In all of these case studies we show that a GT network trained using the proposed learning approach is able to minimize the network-level spiking activity while producing classification accuracy that are comparable to standard approaches on the same dataset.

Few-Shot Time-Series Classification of Chemosensor Data

Recent advances in machine olfaction have demonstrated deep learning algorithms' capabilities in mining patterns in chemosensor data [1, 2]. While these algorithms can perform effective automated feature extraction, they are invariably dependent on a large amount of data. However, there is a pressing need to develop olfactory systems that learn rapidly and adapt continuously in time-critical applications such as gas leak monitoring or fire detection. The primary objectives of this work include the demonstration of rapid-learning and generalization capabilities on chemical sensor data. A simple application is considered in this direction where a system's readiness for rapid classification of gas mixtures is tested. The system consists of a low-cost metal-oxide sensor array that responds to gas mixtures from a headspace. The application in focus is the binary classification of gas sensor data (beverage vs. air). The primary choice of model for algorithm development is a convolutional neural network due to its promising inferencing capabilities. Owing to these algorithms' data-hungry nature, a partial meta-learning approach, known as transfer learning, is adopted. A baseline convolutional neural network is trained on the sensor data to distinguish beverages from the air. This baseline model is fine-tuned on new beverages, referred to as novel classes, using a one-shot and a five-shot regime. Results show that the fine-tuned models successfully distinguish new beverages from a minimal amount of data, besides overcoming the challenges posed by the chemical sensors, such as short-term and long-term drifts in the measurements. The resulting models perform favourably with an average test accuracy of 0.9165 for one-shot learning and 0.9170 for five-shot learning, given that the average baseline model test accuracy is 0.9356. Despite fine-tuning on novel classes, the model preserves its generalizability and is immune to catastrophic forgetting, a shortcoming often faced due to iterative training of neural networks. Method The aroma of fruit juices results from a complex combination of several volatile organic compounds such as esters, aldehydes, alcohols, ketones, and hydrocarbons [3] and depends on ambient factors such as humidity, temperature, etc. While the composition of each fruit juice varies depending on the fruits used, a fruity odour characterizes any fruit beverage. Therefore, an overarching motivation for this study is to detect the presence of fruity odour with chemical sensors. 50 ml samples of commercial juices, namely, orange, apple, blackcurrant, and multivitamin, are used for data collection. The sensor setup consisted of eight AS-MLV metal-oxide (MOX) sensors, humidity, and temperature sensors. The MOX sensors are heated by supplying pulse-modulated voltages for 12 seconds. Voltages are recorded continuously from four MOX sensors placed in the juice headspace. The data collection is carried out over multiple days. Classification of time-series voltages is performed by a convolutional neural network composed of a feature extractor and a classifier (refer Fig. 1). The transfer learning approach, inspired by Sun et al. [4], is split into meta-training and meta-testing stages. In the meta-training stage, both the feature extractor and the classifier are trained with data corresponding to Air and Beverage (a selection of three juices) classes, resulting in a baseline model. A novel juice's data is considered in the meta-testing phase. Classifier weights are fine-tuned with a small support set, comprising one or five samples carrying maximum information. Four experiments are performed holding each juice in the meta-testing dataset. The models are tested for catastrophic forgetting effects after fine-tuning. Furthermore, zero-shot testing of the baseline model is done to justify the need for fine-tuning and to determine the performance improvement on the new composition of Beverage class. Results Meta-training and meta-testing results are presented in Fig. 2. The average validation accuracy during meta-training is 0.9356. Upon fine-tuning with a one-shot regime, the resulting average accuracy on the query set is 0.9165, whereas the five-shot regime resulted in a slightly higher performance of 0.9170. This concludes that a pretrained convolutional neural network can learn and generalize a novel juice from a small amount of data. On the other hand, the low zero-shot test results depict that, apart from apple juice, the network cannot generalize the novel juice samples to the Beverage class without requiring at least one sample of the novel juice. The fine-tuned network does not undergo significant catastrophic forgetting, likely due to the shallow network architecture. The current results suggest that a model pretrained with a combination of juices can detect a new juice upon fine-tuning with a significantly small number of samples. The collected data can be used in the future for performance optimization through compensation of the influence of dynamic conditions such as room air quality, temperature, and humidity and the juice temperature.

Inkjet Production of Innovative Metal Oxide Sensors for e-Nose Systems

In recent years, electronic noses have been successfully adopted for odour detection and characterization in different fields, as environmental monitoring, safety, food quality control and clinical diagnosis [1-4]. However, industrial applications are still limited [5]. One of the major criticalities associated to e-nose adoption is sensor drift, which consists in small temporal variations of the sensor response exposed to the same analyte under identical conditions [6]. Indeed, signal variations over time result in a decrease of sensor sensitivity and selectivity that affects measurement reproducibility and limits the translation of e-nose laboratory results to industrial applications [5]. For the purpose of improving e-nose stability over time, many researchers started working on the development of drift mitigation algorithms to counteract drift effects and make sensor responses comparable over time and many different methods have been proposed in the scientific literature [6]. This paper proposes an alternative approach to the problem, which involves the development of an innovative technique for the production of Metal Oxide Sensors MOS, based on inkjet print. This technology is particularly suitable for microfabrication applications requiring high patterning precision and controlled deposition of functional materials. In particular, both ZnO sol-gel solutions and ZnO nanoparticles suspensions were developed and subsequently printed on alumina substrates. Inks physical properties were tailored to allow efficient jetting from a Dimatix materials printer. Annealing conditions were optimized and active films with different morphological and crystallographic characteristics obtained from the printed layers. Innovative sensors performance was tested by means of specific tests with a reference compound and evaluated in terms of efficiency towards reference compound:ε = (R0 - R)/R0 where R0 is the resistance at the beginning of the measurement and R the minimum resistance value recorder during the analysis of the reference compound. Innovative sensors were tested for a period of 9 months and n-butanol was used as reference compound. For the entire period of testing, sensor efficiencies toward n-butanol were above 90%, highlighting very good sensibility of inkjet sensors towards the reference compound and great stability of responses over time. These results proved the feasibility of the adoption of inkjet print for the production of MOS sensors, encouraging future investigations in this field. Kiani, S.; Minaei, S.; Ghasemi-Varnamkhasti, M. Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review. Journal of Applied Research on Medicinal and Aromatic Plants 2016, 3, 1-9.Bax, C.; Taverna, G.; Eusebio, L.; Sironi, S.; Grizzi, F.; Guazzoni, G.; Capelli, L. Innovative diagnostic methods for early prostate cancer detection through urine analysis: A review. Cancers (Basel) 2018, 10, 123.Capelli, L.; Sironi, S.; Del Rosso, R. Electronic noses for environmental monitoring applications. Sensors (Basel, Switzerland) 2014, 14, 19979-20007.Gliszczyńska-Świgło, A.; Chmielewski, J. Electronic nose as a tool for monitoring the authenticity of food. A review. Food Analytical Methods 2017, 10, 1800-1816.Marco, S.; Gutierrez-Galvez, A. Signal and data processing for machine olfaction and chemical sensing: A review. IEEE Sensors Journal 2012, 12, 3189-3214.Di Carlo, S., Falasconi, M. Drift correction methods for gas chemical sensors. In Artifical olfaction systems: Techniques and challenges, InTech, Ed. New York, USA, 2012; pp 305-326.

Application of electronic nose with chemometrics methods to the detection of juices fraud

Today, consumer demands for fresh foods with desirable sensory properties are increasing. Lack of easy, cheap, and nondestructive methods to control the quality of fruit juices is one of the main challenges in the beverage industry. The present study has examined two types of natural and industrial juices. Machine olfaction with 9-metal oxide semiconductor (MOS) sensors was used for the experiments. Sensor response patterns were analyzed using chemometrics methods, including principal component analysis (PCA), linear and quadratic discriminant analyses (LDA and QDA), and support vector machine (SVM). According to the results, the classification accuracy of SVM, QDA, and LDA methods was 96.25%, 95.8%, and 94.4%, respectively. Practical applications It is important to note that most fruit juices sold in supermarkets contain only a small percentage of natural fruit juice and usually include added sweeteners. As a result, these juices lead to the intake of large amounts of calories without providing nutrients for the body. In addition, performing chemical tests to determine the quality of fruit juices are time-consuming, destructive, and costly and requires several glassware and reagents. E-nose could be used for real-time monitoring of the volatile components of the food to evaluate different features of the product. Generally, E-nose evaluates a mixture of smells released from a sample and is a reliable, nondestructive, cost-effective, and portable method with high feasibility and speed as well as simple use.

Sniffing speeds up chemical detection by controlling air-flows near sensors

Most mammals sniff to detect odors, but little is known how the periodic inhale and exhale that make up a sniff helps to improve odor detection. In this combined experimental and theoretical study, we use fluid mechanics and machine olfaction to rationalize the benefits of sniffing at different rates. We design and build a bellows and sensor system to detect the change in current as a function of odor concentration. A fast sniff enables quick odor recognition, but too fast a sniff makes the amplitude of the signal comparable to noise. A slow sniff increases signal amplitude but delays its transmission. This trade-off may inspire the design of future devices that can actively modulate their sniffing frequency according to different odors.

Feasibility of integrating canine olfaction with chemical and microbial profiling of urine to detect lethal prostate cancer.

Prostate cancer is the second leading cause of cancer death in men in the developed world. A more sensitive and specific detection strategy for lethal prostate cancer beyond serum prostate specific antigen (PSA) population screening is urgently needed. Diagnosis by canine olfaction, using dogs trained to detect cancer by smell, has been shown to be both specific and sensitive. While dogs themselves are impractical as scalable diagnostic sensors, machine olfaction for cancer detection is testable. However, studies bridging the divide between clinical diagnostic techniques, artificial intelligence, and molecular analysis remains difficult due to the significant divide between these disciplines. We tested the clinical feasibility of a cross-disciplinary, integrative approach to early prostate cancer biosensing in urine using trained canine olfaction, volatile organic compound (VOC) analysis by gas chromatography-mass spectroscopy (GC-MS) artificial neural network (ANN)-assisted examination, and microbial profiling in a double-blinded pilot study. Two dogs were trained to detect Gleason 9 prostate cancer in urine collected from biopsy-confirmed patients. Biopsy-negative controls were used to assess canine specificity as prostate cancer biodetectors. Urine samples were simultaneously analyzed for their VOC content in headspace via GC-MS and urinary microbiota content via 16S rDNA Illumina sequencing. In addition, the dogs' diagnoses were used to train an ANN to detect significant peaks in the GC-MS data. The canine olfaction system was 71% sensitive and between 70-76% specific at detecting Gleason 9 prostate cancer. We have also confirmed VOC differences by GC-MS and microbiota differences by 16S rDNA sequencing between cancer positive and biopsy-negative controls. Furthermore, the trained ANN identified regions of interest in the GC-MS data, informed by the canine diagnoses. Methodology and feasibility are established to inform larger-scale studies using canine olfaction, urinary VOCs, and urinary microbiota profiling to develop machine olfaction diagnostic tools. Scalable multi-disciplinary tools may then be compared to PSA screening for earlier, non-invasive, more specific and sensitive detection of clinically aggressive prostate cancers in urine samples.

Machine-OlF-Action: a unified framework for developing and interpreting machine-learning models for chemosensory research.

Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively and speedily identify biologically relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here, we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular input line entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring approximately 103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state-of-the-art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds. MOA is available for Windows, Mac and Linux operating systems. It's accessible at (https://ahuja-lab.in/). Source code, user manual, step-by-step guide and support is available at GitHub (https://github.com/the-ahuja-lab/Machine-Olf-Action). For results, reproducibility and hyperparameters, refer to Supplementary Notes. Supplementary data are available at Bioinformatics online.