Chemical Sensor Data Research Articles

Data Lake Conceptualized Web Platform for Food Research Data Collection

Food research is uniquely intertwined with everyday life and necessitates the utilization of big data. Within this domain, the research data consist of various forms and formats, encompassing biological experiment results, chemical analysis data, nutritional information, microbiological data, sensor data, images, and videos. This diversity stems from the integration of data from various subdomains within the larger field. With recent advancements in deep learning technology, the importance of data has grown significantly, resulting in increased reliance on data-driven research. Although specialized platforms for sharing and utilizing data have been established at the national level, particularly in the bioscience field, food research lacks a dedicated infrastructure and specialized data-sharing platforms. In this study, we develop a platform that leverages Hadoop-based distributed file systems to create a data lake. This platform enables data storage and sharing through a web-based interface. The distributed file system supports scalability by adding data nodes, making it an effective solution for capacity expansion. In addition, the web-based platform ensures high accessibility, allowing users access from anywhere, at any time, using any device. Finally, we introduce the establishment of a 1.8 PB Hadoop-based physical storage system and present an approach for building a highly accessible web platform with substantial utility.

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

BackgroundBreath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported.ObjectiveThe objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm.MethodsWe conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard.ResultsWe enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84–0.91), the summary specificity was 1.00 (95% CI 0.85–1.00), the AUC was 0.96 (95% CI 0.94–0.98), the pAUC was 0.92 (95% CI 0.89–0.96), and the DOR was 207.62 (95% CI 24.62–924.64).ConclusionElectronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.

Optimizing the Spectral Properties of the Chemical Sensor to Detect Concentrations of Gas Mixtures

Monitoring aromatic hydrocarbons is environmentally important because these chemical pollutants are ubiquitous. While waiting for powerful sensors capable of detecting hydrocarbons at extremely low levels, the current study demonstrates how each of the pure gas mixtures can be quickly and accurately identified. A noise removal unit was created for the chemical sensor data and then processed on the basis of the proposed algorithms in order to achieve matching and calibration. This method can be extended to other important aromatic hydrocarbon pollutants.

Deep-Learning-Based Gas Leak Source Localization From Sparse Sensor Data

In this article, we address the problem of estimating the location of gas leak sources using sparse unreliable spatio-temporal chemical sensor data. We pose the task of estimating the underlying gas signal and predicting the source location as an inverse problem. For this purpose, we develop a novel deep-learning projection-based framework. We incorporate traditional projection-onto-convex-sets (POCS) iteration in the structure of the deep model to obtain a regularized solution that conforms to our prior knowledge of the spatio-temporal structure of the gas concentration distribution. We use discrete cosine transform (DCT) layers to model the smooth nature of the gas plume signal. In the DCT domain, we project the feature maps onto a low-pass region, whose boundary is determined during training using the backpropagation algorithm. This operation is equivalent to projecting onto a convex set. Furthermore, these projection operations are embedded in the nonlinear structure of a convolutional neural network. We address two different types of data: methane–propane leak from industrial plants and isopropyl alcohol (isopropanol) vapor leak in an indoor environment. Experimental results are presented. Our results show that we can obtain a smooth estimate of the underlying gas signal while obtaining a good source location prediction with high accuracy.

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.

Surface-Enhanced Raman Scattering-Based Odor Compass: Locating Multiple Chemical Sources and Pathogens.

Olfaction is important for identifying and avoiding toxic substances in living systems. Many efforts have been made to realize artificial olfaction systems that reflect the capacity of biological systems. A sophisticated example of an artificial olfaction device is the odor compass which uses chemical sensor data to identify odor source direction. Successful odor compass designs often rely on plume-based detection and mobile robots, where active, mechanical motion of the sensor platform is employed. Passive, diffusion-based odor compasses remain elusive as detection of low analyte concentrations and quantification of small concentration gradients from within the sensor platform are necessary. Further, simultaneously identifying multiple odor sources using an odor compass remains an ongoing challenge, especially for similar analytes. Here, we show that surface-enhanced Raman scattering (SERS) sensors overcome these challenges, and we present the first SERS odor compass. Using a grid array of SERS sensors, machine learning analysis enables reliable identification of multiple odor sources arising from diffusion of analytes from one or two localized sources. Specifically, convolutional neural network and support vector machine classifier models achieve over 90% accuracy for a multiple odor source problem. This system is then used to identify the location of an Escherichia coli biofilm via its complex signature of volatile organic compounds. Thus, the fabricated SERS chemical sensors have the needed limit of detection and quantification for diffusion-based odor compasses. Solving the multiple odor source problem with a passive platform opens a path toward an Internet of things approach to monitor toxic gases and indoor pathogens.

Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network.

Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

Current reflearch and future development of organic laser materials and devices

Current reflearch and future development of organic laser materials and devices

Classifying chemical sensor data using GPU-accelerated bio-mimetic neuronal networks based on the insect olfactory system

Chemosensing “e-nose” technology has great potential applications in everyday life, ranging from drug detection to food quality assessment and even the diagnosis of illness. However, odour detection and classification remains a highly challenging domain, characterized by high dimensionality, unknown organization of the vast “odourant space” of volatile chemicals and complex, turbulent odour plumes. To compound these difficulties, current sensor technology continues to exhibit distinct shortcomings in speed, sensitivity, selectivity, recovery, and drift avoidance. In the research reported here we turn to a range of recent neuronal models [1-6] that were developed to describe the insect olfactory system, and have been shown to perform well across a range of classic, static classification tasks such as the MNIST handwritten digit set and the Sigma-Aldrich scent database. The insect olfactory system has been extensively studied and has been shown to be both fast and highly effective in complex natural conditions despite its limited size and complexity (when compared to the mammalian system) [2]. Insects such as moths, honey bees, locusts and fruit flies are capable of odour detection and classification tasks well beyond the abilities of current e-nose technology and machine learning algorithms [6]. We present results of applying an insect-inspired approach to the design of a learning spiking neural network that receives synchronized time series data from up to 12 metal-oxide based gas sensors, comprising an optimised [7] combination of classical doped tin oxide and novel zeolite-coated chromium titanium oxide sensors. We have collected sample data sets for classification tasks ranging from “easy” (single chemical identification presented under laboratory conditions) through to “hard” (identification of indicators of infectious diseases in breath samples taken from patients). To address slow sensor response we consider a range of transient-based processing before applying self-organisation techniques to most effectively locate “virtual receptors” (VR) in sensor space. We look to address decorrelation (separation in feature space) and the supervised association of responses with rewards by using correlates of the insect antennal lobe (AL) and mushroom body (MB) structures whilst applying reward-based spike-timing dependent plasticity mechanisms. Classification accuracy is compared with support vector machine (SVM) learning which we also look to match for speed through the use of GPU accelerated neural simulation [5] via the NVidia CUDA TM -based GeNN platform (http://sourceforge.net/projects/genn/).

English

The paper presents a new approach to surface acoustic wave (SAW) chemical sensor array design and data processing for recognition of volatile organic compounds (VOCs) based on transient responses. The array is constructed of variable thickness single polymer-coated SAW oscillator sensors. The thickness of polymer coatings are selected such that during the sensing period, different sensors are loaded with varied levels of diffusive inflow of vapour species due to different stages of termination of equilibration process. Using a single polymer for coating the individual sensors with different thickness introduces vapour-specific kinetics variability in transient responses. The transient shapes are analysed by wavelet decomposition based on Daubechies mother wavelets. The set of discrete wavelet transform (DWT) approximation coefficients across the array transients is taken to represent the vapour sample in two alternate ways. In one, the sets generated by all the transients are combined into a single set to give a single representation to the vapour. In the other, the set of approximation coefficients at each data point generated by all transients is taken to represent the vapour. The latter results in as many alternate representations as there are approximation coefficients. The alternate representations of a vapour sample are treated as different instances or realisations for further processing. The wavelet analysis is then followed by the principal component analysis (PCA) to create new feature space. A comparative analysis of the feature spaces created by both the methods leads to the conclusion that both methods yield complimentary information: the one reveals intrinsic data variables, and the other enhances class separability. The present approach is validated by generating synthetic transient response data based on a prototype polyisobutylene (PIB) coated 3-element SAW sensor array exposed to 7 VOC vapours: chloroform, chlorobenzene o-dichlorobenzene, n-heptane, toluene, n-hexane and n-octane. Defence Science Journal, 2010, 60(4), pp.377-386 , DOI:http://dx.doi.org/10.14429/dsj.60.494

A neuromorphic model of dual pathway odour classification

Event Abstract Back to Event A neuromorphic model of dual pathway odour classification Chris Häusler1, 2, 3*, Martin P. Nawrot1, 2 and Michael Schmuker1, 2 1 Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience Berlin, Germany 2 Freie Universität Berlin, Neuroinformatics and Theoretical Neuroscience, Institute of Biology, Germany 3 Technische Universität Berlin, Machine Learning, Germany Insects can identify odorant stimuli in a fast and highly reliable manner. This is evident from a large body of behavioral discrimination experiments, e.g. in the honeybee (Giurfa 2007). The neuronal circuits involved in odor discrimination are well described on the structural level. We investigate the functional implications at the circuit level in order to improve our understanding of insect olfaction at each processing step. Our ultimate goal is to employ neural working principles for processing of chemical sensor data in technical applications.Here, we show that strong lateral inhibitory connections found in the insect antennal lobe (AL) facilitate more accurate odour classification by increasing the linear separability of presented stimuli. Our findings are based on a spiking neural network extension of prior work with rate coding in the AL (Schmuker et al. 2007). By leveraging the pyNN neural network modeling language (Eppler et al 2009), we are able to run simulations using well established neuronal simulators such as Neuron and Nest along with neuromorphic hardware that employs up to several hundred analog implementations of Integrate and Fire neurons and their synaptic interconnections (Brüderle et al. 2009).As an extension of our current work, we will also investigate a spiking network model of the dual olfactory pathways in the AL of Hymenoptera (bees and ants). The first is characterised by a narrow tuning to different odours whilst being largely concentration invariant. The second is less selective toward odour identity but responds strongly to different odour concentrations (Yamagata et al. 2009). These complementary features imply a functional diversification, which possibly results in an improved odor discriminability in bees and ants over other insect species. References Brüderle D, Müller E, Davison A, Muller E, Schemmel J, Meier K (2009).Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System Front. Neuroinform. 3:17 Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L, Yger P (2009). PyNN: a common interface for neuronal network simulators. Neuroinformatics 2 (January) p. 1-10 Giurfa, M. (2007). Behavioral and neural analysis of associative learning in the honeybee: a taste from the magic well. J Comp Physiol. A 193, 801-24. Schmuker, M., and Schneider, G. (2007). Processing and classification of chemical data inspired by insect olfaction. PNAS 104, 20285-20289. Yamagata N, Schmuker M, Szyszka P, Mizunami M, Menzel R (2009). Differential odor processing in two olfactory pathways in the honeybee. Front. Neuroscience 3 Keywords: computational neuroscience Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Poster Abstract Topic: Bernstein Conference on Computational Neuroscience Citation: Häusler C, Nawrot MP and Schmuker M (2010). A neuromorphic model of dual pathway odour classification. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00043 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Sep 2010; Published Online: 23 Sep 2010. * Correspondence: Dr. Chris Häusler, Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany, chris.hausler@bccn-berlin.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Chris Häusler Martin P Nawrot Michael Schmuker Google Chris Häusler Martin P Nawrot Michael Schmuker Google Scholar Chris Häusler Martin P Nawrot Michael Schmuker PubMed Chris Häusler Martin P Nawrot Michael Schmuker Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Generalized Model of Resonant Polymer-Coated Microcantilevers in Viscous Liquid Media

Expressions describing the resonant frequency and quality factor of a dynamically driven, polymer-coated microcantilever in a viscous liquid medium have been obtained. These generalized formulas are used to describe the effects the operational medium and the viscoelastic coating have on the device sensitivity when used in liquid-phase chemical sensing applications. Shifts in the resonant frequency are normally assumed proportional to the mass of sorbed analyte in the sensing layer. However, the expression for the frequency shift derived in this work indicates that the frequency shift is also dependent on changes in the sensing layer's loss and storage moduli, changes in the moment of inertia, and changes in the medium of operation's viscosity and density. Not accounting for these factors will lead to incorrect analyte concentration predictions. The derived expressions are shown to reduce to well-known formulas found in the literature for the case of an uncoated cantilever in a viscous liquid medium and the case of a coated cantilever in air or in a vacuum. The theoretical results presented are then compared to available chemical sensor data in aqueous and viscous solutions.

Characterizing the scale dimension of a high-dimensional classification problem

Classification of high-dimensional data is inherently difficult. We present an exploratory data analysis methodology for characterizing the scale dimension of a classification problem. The idea is to characterize the support of one distinguished target class as a collection of balls covering the class, with each ball centered at an observation in that class such that the radius is maximal without containing observations from the other classes. The scale dimension is defined to be the number of distinct radii (ball sizes) required to cover the class without covering observations from the other class. A greedy algorithm is used to fit the balls. The balls then provide a description of the support of the target class, with information about the complexity of the classification problem implicit in the number, radii, adjacency and position of the balls. Clustering the balls by radius and pruning the cluster tree yields an estimate of the scale dimension for the problem. We illustrate the methodology with pedagogical simulations and a chemical sensor data analysis application.

Olfactory classification via interpoint distance analysis

Detection of the presence of a single prespecified chemical analyte at low concentration in complex backgrounds is a difficult application for chemical sensors. The article considers a database of artificial nose observations designed specifically to allow for the investigation of chemical sensor data analysis performance on the problem of trichloroethylene (TCE) detection. We consider an approach to this application which uses an ensemble of subsample classifiers based on interpoint distances. Experimental results are presented indicating that our nonparametric methodology is a useful tool in olfactory classification.

Data preprocessing enhances the classification of different brands of Espresso coffee with an electronic nose

Two different ways of preprocessing chemical sensor data are presented as a means to improve the interpretation and the classification ability of an electronic nose (EN). The practical problem at hand is the distinction between four commercial coffee blends — containing up to 12 types of coffees — all of which are to be consumed as Espresso. Coffee was sampled in three successive preparation phases: as beans, ground (powder) or liquid (the actual Espresso). In the case of beans, stress is put on the improved clusters visualization after the preprocessing and before the actual classification is performed. Different catalysed sensors and successive extractions were used to differentiate the response pattern towards the various coffees. The features which permitted the best samples' classification as judged from Principal Component Analysis (PCA) score plots were selected. To this end, an empirical search strategy inside the feature space is presented. Scores from PCA were subsequently utilized as inputs for a feed forward multilayer perceptron (MLP) with cross-validation resulting in 100% correct classification with just two sensors. In the case of ground coffee, a (supervised) drift compensation algorithm was developed. It essentially consists of removing the first principal component (PC) for every cluster since this is seen to be given by the drift. An 87.5% classification performance was achieved. Liquid coffee, on the other hand, was not successfully classified, probably due to the difficulty in assuring reproducible sampling conditions.

Detection of perfumes in diesel fuels with semiconductor and mass-spectrometry based electronic noses

The gas-phase of two diesel fuels scented with perfume mixtures were examined with a FOX 4000 electronic nose (Alpha-M.O.S., France) and a Smart Nose GA (LDZ Laboratory, Switzerland). The first system uses chemical gas sensors, the second a mass spectrometer. The data of both instruments were evaluated by principal component analysis (PCA) and linear discriminant analysis (LDA). The LDA results obtained allowed the detection of a much lower perfume concentration with the mass spectrometry data than with the chemical sensor data. Copyright © 2000 John Wiley & Sons, Ltd.