Metal Oxide Sensor Array Research Articles

Establishing Healthy Breath Baselines With Tin Oxide Sensors: Fundamental Building Blocks for Noninvasive Health Monitoring.

Volatile organic compounds (VOCs) in breath serve as a source of biomarkers for medical conditions relevant to warfighter health including Corona Virus Disease and other potential biological threats. Electronic noses are integrated arrays of gas sensors that are cost-effective and miniaturized devices that rapidly respond to VOCs in exhaled breath. The current study seeks to qualify healthy breath baselines of exhaled VOC profiles through analysis using a commercialized array of metal oxide (MOX) sensors. Subjects were recruited/consented through word of mouth and using posters. For each sample, breath was analyzed using an array of MOX sensors with parameters that were previously established. Data were also collected using a lifestyle questionnaire and from a blood test to assess markers of general health. Sensor data were processed using a feature extraction algorithm, which were analyzed through statistical approaches to identify correlations with confounding factors. Reproducibility was also assessed through relative standard deviation values of sensor features within a single subject and between different volunteers. A total of 164 breath samples were collected from different individuals, and 10 of these volunteers provided an additional 9 samples over 6 months for the longitudinal study. First, data from different subjects were analyzed, and the trends of the 17 extracted features were elucidated. This revealed not only a high degree of correlation between sensors within the array but also between some of the features extracted within a single sensor. This helped guide the removal of multicollinear features for multivariate statistical analyses. No correlations were identified between sensor features and confounding factors of interest (age, body mass index, smoking, and sex) after P-value adjustment, indicating that these variables have an insignificant impact on the observed sensor signal. Finally, the longitudinal replicates were analyzed, and reproducibility assessment showed that the variability between subjects was significantly higher than within replicates of a single volunteer (P-value = .002). Multivariate analyses within the longitudinal data displayed that subjects could not be distinguished from one another, indicating that there may be a universal healthy breath baseline that is not specific to particular individuals. The current study sought to qualify healthy baselines of VOCs in exhaled breath using a MOX sensor array that can be leveraged in the future to detect medical conditions relevant to warfighter health. For example, the results of the study will be useful, as the healthy breath VOC data from the sensor array can be cross-referenced in future studies aiming to use the device to distinguish disease states. Ultimately, the sensors may be integrated into a portable breathalyzer or current military gear to increase warfighter readiness through rapid and noninvasive health monitoring.

Pulsed-Heating System With an Integrated Metal-Oxide Sensor Array Scheming Low-Power Temperature Modulation

Pulsed-Heating System With an Integrated Metal-Oxide Sensor Array Scheming Low-Power Temperature Modulation

Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning

Detection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO–Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80–100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.Graphical

How well does your e-nose detect cancer? Application of artificial breath analysis for performance assessment

Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between ‘healthy’ samples (breath without additives) and ‘cancer’ samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.

Noble metal (Pd,Pt) doped ZnO: A promising complementary array for DGA in transformer oil based on DFT method

In this study, we utilized a metal oxide sensor array for the quantitative detection of three characteristic dissolved gases (C2H2, CO, and C2H4) in transformer oil. Our focus was directed towards investigating noble metal doped ZnO materials through density-functional theory (DFT) simulations to verify the capabilities of the sensor array. Physical structures and adsorption parameters of the pure ZnO, Pd-ZnO, and Pt-ZnO toward the three gases were calculated and analyzed. Through an analysis of the density of states and charge density difference (CDD), our investigation revealed that Pd doping significantly enhances the adsorption energy and charge transfer amplitude by approximately 10 and 3 times, respectively, compared to pure ZnO for C2H2. Furthermore, to verify the array’s detection of the other characteristic gases, we calculated and analyzed the adsorption energy and partial density of states (PDOS) curves of the three gas-sensitive materials, respectively, and results indicated their complementary sensitivities to the three characteristic gases. The complementary responses of the noble metal doped ZnO materials provide a new method of fabricating the gas sensor array, and then quantitatively detecting concentrations of the characteristic gases in transformer dissolved gas analysis (DGA) through artificial intelligent algorithm based on the sensor array.

Machine-learning-assisted sensor array for detecting COVID-19 through simulated exhaled air

Reverse transcription polymerase chain reaction (RT-PCR), the primary test for COVID-19, requires complicated sample collection and several hours to obtain results. Breath test for exhaled volatile organic compounds (VOCs) has gained substantial research attention as a simple non-invasive and fast screening method. However, a unique VOC fingerprint as a potential prognostic biomarker is still unavailable. Accordingly, this study prepared simulated VOC gases to test the classification performance of a sensing system. The simulated VOC gases were selected according to the actual composition of the exhaled breath of patients with acute respiratory diseases, including COVID-19. Two sets of metal oxide sensor arrays, comprising eight commercial sensors and eight Advanced Institute of Science and Technology (AIST) laboratory-made sensors, were prepared to test their sensing ability for the simulated gases. The principal component analysis (PCA) results revealed that the AIST sensors had better sensing ability than the commercial sensors. Moreover, the recursive feature elimination cross-validation (RFECV) of random forest (RF) further confirmed the superiority of the AIST sensors over the commercial sensors. An artificial neural network (ANN) with excellent prediction performance for gas concentration was developed. This study provides a promising method for rapidly screening respiratory diseases.

High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level

Developing a better understanding of gas-metal oxide interactions as well as improving the output performance with the aging of the gas sensing array represents a critical challenge. In this work, we have designed a surface-functionalized metal-oxide sensors array containing six units for detecting NO2, SO2, H2S, and CO hazardous gases. The responses of the sensor array to four target gases are enhanced by various catalytic effects on the surface, and they show distinctive responses to each gas. For improved selectivity, a pattern recognition algorithm is implemented under an edge computing-based environment to relieve the overload of a monitoring server. Moreover, a self-calibration method for long-term stability is designed to indirectly calibrate gas responses with aging. With its edge-computing device prototype, which includes a sensor array and its readout integrated circuit, four target gases are distinguished by an artificial neural network algorithm with 97% accuracy. The present work describes the effective platforms for emerging next-generation gas sensors with improved communication and monitoring systems.

Study of a Formaldehyde Gas Sensor Based on a Sputtered Vanadium Pentoxide Thin Film Decorated with Gold Nanoparticles

Formaldehyde (HCHO) gas sensing properties of a new sensor based on a sputtered vanadium pentoxide (V2O5) thin film decorated with evaporated gold (Au) nanoparticles (NPs) are studied and reported herein. The use of Au NPs effectively increases the surface area/volume (SA/V) ratio and enhances the catalytic activity of Au metal. Experimentally, good p-type sensing characteristics, including a high sensing response ratio of 73.1% under 20 ppm HCHO/air gas and a small recovery time of 6.8 s at 225 °C, are obtained. An extremely low detecting level of 15 ppb HCHO/air is also acquired. Due to these advantages and the benefits of simple structure and easy fabrication, the studied Au NP/V2O5 device is an excellent candidate for p-type consumption formaldehyde gas sensing and complementary metal oxide sensor array (CMOSA) applications.

(IMCS Second Place Best Poster Award) In-Situ Analysis of Volatile Organic Compounds in Biogas Fermentation Processes Using a Metal Oxide Gas Sensor Array

(IMCS Second Place Best Poster Award) In-Situ Analysis of Volatile Organic Compounds in Biogas Fermentation Processes Using a Metal Oxide Gas Sensor Array

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.

Quantile regression with a metal oxide sensors array for methane prediction over a municipal solid waste treatment plant

Abstract Methane leakage is a crucial issue regarding its potential Green House effect. This study developed a quantile regression model for methane estimation over a municipal solid waste treatment plant (MSW) subject to biogas leakages and monitored with MOS gas sensors. Experimental data from 6 MOS gas sensors and a methane FID analyser taken during fourth months have been used for that purpose. The data processing consisted of (i) a drift correction, (ii) the addition of interactions, (iii) a principal component analysis (PCA) to extract new uncorrelated predictors, and (iv) a log transform of the methane data distribution. The forecast ability of the derived field calibrated model, compared with results from PLS regression, indicates well how helpful has been the data processing methods. Moreover, it highlighted, with some caution, the interest in using the quantile regression and interactions for models with MOS gas sensors considering the cross-sensitivity.

Aerial Mapping of Odorous Gases in a Wastewater Treatment Plant Using a Small Drone

Wastewater treatment plants (WWTPs) are sources of greenhouse gases, hazardous air pollutants and offensive odors. These emissions can have negative repercussions in and around the plant, degrading the quality of life of surrounding neighborhoods, damaging the environment, and reducing employee’s overall job satisfaction. Current monitoring methodologies based on fixed gas detectors and sporadic olfactometric measurements (human panels) do not allow for an accurate spatial representation of such emissions. In this paper we use a small drone equipped with an array of electrochemical and metal oxide (MOX) sensors for mapping odorous gases in a mid-sized WWTP. An innovative sampling system based on two (10 m long) flexible tubes hanging from the drone allowed near-source sampling from a safe distance with negligible influence from the downwash of the drone’s propellers. The proposed platform is very convenient for monitoring hard-to-reach emission sources, such as the plant’s deodorization chimney, which turned out to be responsible for the strongest odor emissions. The geo-localized measurements visualized in the form of a two-dimensional (2D) gas concentration map revealed the main emission hotspots where abatement solutions were needed. A principal component analysis (PCA) of the multivariate sensor signals suggests that the proposed system can also be used to trace which emission source is responsible for a certain measurement.

Soil organic matter determination based on artificial olfactory system and PLSR-BPNN

Soil organic matter (SOM) is a key indicator of soil fertility. For accurate measurement of SOM, a novel method based on an artificial olfactory system (AOS) was proposed. The response curves of soil volatile organic compounds (VOCs) were measured using a metal-oxide semiconductor sensor array, and four features (including maximum value, mean differential coefficient, response area, and the transient value at the 20th second) were obtained from the curves and used to build olfactory feature space. Then, prediction models were established using the pattern recognition algorithm. To further enhance the accuracy of AOS measurement, we used Monte Carlo cross-validation (MCCV) to identify and eliminate the abnormal samples of the soil olfactory feature space. Then, the dimension reduction method of the genetic algorithm (GA)back-propagation (BP) was used to find the appropriate feature vectors, and two types of hybrid models were presented. One was the support vector machine (SVM) and group method of data handling (GMDH) combined model—SVM-GMDH. The other was a combination of partial least squares regression (PLSR) and back-propagation neural network (BPNN)—PLSR-BPNN. The forecasting performances of three single models (BPNN, PLSR, support vector regression: SVR) and two combined models (PLSR-BPNN, SVM-GMDH) were comparatively evaluated. The evaluation indices included coefficient of determination (R 2), root mean square error (RMSE), ratio of performance to deviation and relative prediction error (RPE). It was found that the predictive capabilities of all five tested models were improved after elimination of abnormal samples and feature reduction. Moreover, PLSR-BPNN performed the best in predicting SOM concentrations, with R 2 = 0.952, RMSE = 1.771, PRD = 4.291, and slight variation of RPE within 0–0.185, and thus can offer a reference for predicting SOM via AOS.

Selective Detection of Formaldehyde By a Flame-Made Sensor Array for Environmental Monitoring

Introduction Formaldehyde is a carcinogenic pollutant omnipresent in indoor air due to emission from wood-based furniture, textiles, and paints [1]. But it is formed also outdoors from biofuel combustion, forest fires and photooxidation of smog [2]. Therefore, its exposure is strictly regulated worldwide (e.g., World Health Organization < 80 ppb for short-term exposure). However, reliable and portable devices for on-site monitoring do not exist. Currently, formaldehyde emission testing is carried out by sampling via sorption tubes and subsequent off-site laboratory analysis by gas chromatography. This is expensive, time-consuming, and thus not applicable for distributed indoor air quality monitoring or personal exposure assessment. Chemical sensors are promising for these applications as they are compact, low-cost and simple-in-use. Current sensors, however, typically fail to fulfill the necessary sensitivity and selectivity requirements to detect relevant formaldehyde concentrations below 100 parts per billion (ppb) in ambient air, which contains hundreds of interfering compounds. Here, we present a highly sensitive, selective and compact sensor array of flame-made metal-oxide sensors for real-time quantification of formaldehyde at 90% relative humidity (RH) in gas mixtures. Method Sensing nanoparticles of Pt-, Si-, Pd- and Ti-doped SnO2 are produced by flame-spray pyrolysis and directly deposited onto microsensor substrates by thermophoresis. The individual sensors are assembled to an array (E-nose) by wire-bonding them on a leadless chip carrier, which is placed inside a Teflon sensor chamber. Sensing films are heated to 400 °C with a Pt back heater and the E-nose is characterized in synthetic gas mixtures with a gas mixing setup at 90% RH [3]. The E-nose is tested in gas mixtures containing formaldehyde, acetone, ammonia and ethanol and a multilinear linear regression model is applied to obtain individual analyte concentrations [4]. Results and Conclusions The E-nose consists of four nanostructured and highly porous Pt-, Si-, Pd- and Ti-doped SnO2 sensing films precisely deposited onto silicon wafer-based microsubstrates by flame spray pyrolysis (Figure 1a, b). Flame-made sensing particles are deposited (black patterns) within the designated area of the interdigitated electrodes, while the heater and electrode pads are shielded by the shadow mask and not contaminated by particles. This technique is scalable and be readily applied to wafer-level processing (69 sensors in a single step [5]).The individual sensors offer a sensing film consisting of a highly porous and nanostructured network of nanoparticles (Figure 1b). As a result, they are highly sensitive for formaldehyde concentrations <10 ppb even at 90% RH with fast response dynamics (response time 140 s) as shown in Figure 2. Even concentrations as low as 5 ppb are detected with high signal-to-noise ratio >25. Each dopant induces different analyte selectivity, enabling selective detection of formaldehyde in gas mixtures by multivariate linear regression. We apply this for formaldehyde detection in complex gas mixtures of formaldehyde, acetone, NH3, and ethanol at realistic concentrations and humidity. Formaldehyde is thereby detected with an average error of ≤9 pbb at concentrations 30–180 ppb despite the much higher concentrated interferants.As a result, based on its compact size and low price, this device is promising for monitoring of formaldehyde in indoor and outdoor air. When interconnected, such next-generation low-cost detectors could enable distributed chemical recognition [6] for air quality in “smart” buildings and “future” cities.

In-Situ Analysis of Volatile Organic Compounds in Biogas Fermentation Processes Using a Metal Oxide Gas Sensor Array

1. Introduction and Motivation Considering the global climate change, the efficient use of clean and renewable sources of energy is one of the key research areas. In this context, the biological production of methane and other combustible biogas produced by anaerobic fermentation of organic wastes is gaining growing importance. These fermentation processes attract more and more interest mainly due to the possibility to use different types of residues like food waste, dairy wastes and many other organic wastes as feeding substrates. However, efficient biomass conversion to biogas is only possible if the process parameters taking direct influence on the fermentation process are continuously and reliably monitored. This allows efficient process control. One of such key process parameters are the volatile fatty acids (acetic, propionic, butanoic) formed during the biomass conversion process. The reliable monitoring of such organic acids and other volatile organic compounds (VOCs) gives valuable information and their analysis even at low concentrations (<2000 ppm) allows to model the actual microbial state and to adapt the feeding to keep their concentration and their inhibiting influence on the fermentation process low.Conventionally, the analysis of the organic acid composition in anaerobic fermentation processes is done by sophisticated methods like gas chromatography [1], infrared spectroscopy [2], and high pressure liquid chromatography (HPLC) [3]. However, the major disadvantages of these methods are their complicated and time consuming sample pre-treatment routines and high costs.In this paper, an automated measuring system developed by combining a silicon rubber membrane based carrier gas probe (Fig. 1) with a thermo-cyclically operated metal oxide gas sensor array [4] is introduced. This automated system might enable in-situ monitoring of different VOCs developing during the bio-fermentation processes in time periods of about one hour.1. Method of VOC monitoring Metal oxide gas sensors (MOGs) are well established as gas sensing devices for monitoring of VOCs and oxidizable gases like CO, H2 and CH4. Thus, before measurement of dissolved VOCs, CH4 and other to cross-sensitivity contributing gas components present in the biogas fermentation sample must be driven out. In a first step, a small amount of the fermentation liquid (about one liter) is extracted from the main reactor and its pH is shifted to an alkaline value by dosage of potassium hydroxide. This allows transformation of the dissolved organic acids to the dissociated state and enables purging out CH4, H2 and all other non-acidic, physically dissolved gas components contributing to the sensor signal, by a high flow of N2. Then, pH is shifted to a value near or even lower than the pKa value of the organic acids by dosage of phosphorous acid. Now the dissolved organic acids are in the undissociated molecular state and equilibrate with the gas state (Henry´s law). This enables the uptake of molecular dissolved organic acids from the liquid state into the constant flow of synthetic air (carrier gas: 5ml/min) by permeation through the gas permeable silicon rubber membrane of the gas carrier probe. By the help of the carrier gas the organic acids are transported to the metal oxide gas sensor array (Fig 1a) for analysis.1. Results and Discussion Several SnO2/additive gas sensing composites have been prepared and the most interesting candidates with respect to their remarkably high sensitivity and most characteristic conductance vs. time profile (CTP) shapes when operated in thermocyclic mode [4,5] will be reported. When the carrier gas probe is immersed in acetic acid/deionized water admixtures some metal oxides show characteristic CTP-shapes (Fig. 1b) representing the individual surface reaction processes with acetic acid. In addition, some first experiments with real fermentation liquids resulted in some unexpected gas developments with pH and time well indicated by characteristic CTP-changes, which will highlight the presentation.1. Conclusions and outlook By some extended in-situ monitoring experiments of acetic acid in deionized water as a model substance using a gas carrier probe combined with a 4-fold metal oxide sensor array, several metal oxide materials could be identified as good candidates for monitoring of organic acids in biogas fermentation processes.Monitoring experiments in real fermentation samples and analysis of the CTPs yielded gas formation changing with pH and time. These gas formation processes have to be referenced by simultaneous Gas Chromatograph-Mass Spectrometry (GC-MS) analysis and the results will be reported as well in context with the MOG sensitivity data.1. Acknowledgement This work is part of the EBIPREP collaboration project (www.ebiprep.eu/). It is financed by the EU International Programme INTERREG V Oberrhein 2017-2020.

Development of Electronic Nose for Qualitative and Quantitative Monitoring of Volatile Flammable Liquids.

A real-time electric nose (E-nose) with a metal oxide sensor (MOS) array was developed to monitor 5 highly flammable liquids (ethanol, tetrahydrofuran, turpentine, lacquer thinner, and gasoline) in this work. We found that temperature had a significant impact on the test results and temperature control could efficiently improve the performance of our E-nose. The results of our qualitative analysis showed that principal component analysis (PCA) could not efficiently distinguish these samples compared to a back-propagation artificial neural network (BP-ANN) which had a 100% accuracy rate on the test samples. Quantitative analysis was performed by regression analysis and the average errors were 9.1%–18.4%. In addition, through anti-interference training, the E-nose could filter out the potential false alarm caused by mosquito repellent, perfume and hair jelly.

Effect of Storage Time and Packing Method on the Freshness of Dried Lycium Fruit Using Electronic Nose and Chemometrics

The effect of storage time and packing method on dried Lycium fruits was studied through an electronic olfactory system with the metal oxide sensor array that provides an overall perception of the volatile compounds presented in the sample headspace. Principle component analysis (PCA), canonical discriminant analysis (CDA), and cluster analysis (CA) were used for freshness and packing methods discrimination of dried Lycium fruits. The stale samples of 2015 and 2016 could be separated with those of 2017 by PCA, CDA, and CA analysis. Better discrimination results were obtained by CDA, with samples of 2015 and 2016 separated with each other. For samples of 2017, the unpackaged samples of 2017-4 were distinguished with the vacuumed samples, while samples of grade C were separated with B and D. For quantitative analysis, predictive models for prediction of the storage years of dried Lycium fruits were built with methods of partial least square (PLS) analysis, multiple linear regression (MLR), and back propagation neural network (BPNN). The model built by BPNN showed the best predict ability with R2 = 0.9994, while PLS and MLR were also effective in the prediction of storage years of dried Lycium fruits, with high determination coefficients of 0.9316 and 0.9330. These findings showed that E-nose can be used in the discrimination of the storage time and package method of dried Lycium fruits.

Towards Gas Sensors for Aquatic Chemical Sensing Inspired By the Star-Nosed Mole

Aquatic chemical sensors, as any other surface submerged in water, are subject to biofouling, by bacteria and algae that quickly form biofilms on the submerged surface [1]. In the case of sensors, accumulated biofilms skew the chemical measurements until the sensors are cleaned and recalibrated. A number of mechanical and chemical solutions have been proposed to scrape off or kill the biofilms, but they can be sensor-specific, costly, or have adverse effects on the environment [2]. The process of biofouling happens over the span of days, making long-term chemical measurements labor-intensive and oftentimes unfeasible. The star-nosed mole, with its bizarre nose geometry, presents a way to circumvent the issue of biofouling. These semi-aquatic moles exhale and inhale bubbles rapidly underwater. When the bubbles contact a target of interest, molecules are collected on the bubble surface and brought back into the nose. In this fashion, its olfactory receptors never contact the water and the mole can use its terrestrial nose to track prey at the bottom of swamps [3]. The appendages that make up the star on their nose stabilize the bubbles, allowing them to consistently blow larger bubbles without the bubble pinching off and floating away [4]. We develop an underwater electronic nose, inspired by the star-nosed mole’s underwater sniffing. A motor pushes and pulls on a syringe to form and contract a 0.5 mL bubble underwater at 1 Hz. A 3D printed star, inspired by the mole’s nose geometry, stabilizes the bubble and prevents pinch-off [4]. The 6 fins of the star, separated by 30-degree angles, are slanted downward at a 35-degree angle to optimally stabilize the largest volume bubble. The air from the bubble is pushed and pulled past an array of metal oxide sensors. As a proof of concept, this underwater e-nose is used in an on-going experiment to detect the presence or absence of sparkler fragments at the bottom of a water sample. In the event of the bubble pinching off, inhaled water shorts out the sensor array. At the same time, if the volume of dead space between the star and the sensor array is greater than the volume of the bubble, tests show that the e-nose will be incapable of detecting changes in the underwater environment. Thus, stabilizing the bubble during the sniff is key in designing this underwater e-nose. This prototype e-nose marks the expansion of machine olfaction to underwater applications. Figure 1. Underwater bubble sniffing can allow gas sensors to be used underwater. A) The star-nosed mole sniffs bubbles to pick up scents underwater. Photo courtesy of K. Catania. B) Mimicking this behavior could allow gas sensors to detect chemicals without directly exposing the sensor to its environment. C) A motor and syringe sniff underwater across a metal oxide sensor to form an underwater e-nose. [1] G. Xu, W. Shen, and X. Wang, “Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey,” Sensors, vol. 14, no. 9, pp. 16932–16954, Sep. 2014. [2] L. Delauney, C. Compère, and M. Lehaitre, “Biofouling protection for marine environmental sensors,” Ocean Sci, vol. 6, no. 2, pp. 503–511, May 2010. [3] K. C. Catania, “Olfaction: Underwater ‘sniffing’ by semi-aquatic mammals,” Nature, vol. 444, no. 7122, p. 1024, Dec. 2006. [4] A. B. Lee and D. L. Hu, “Bubble stabilization by the star-nosed mole,” Phys. Rev. Fluids, vol. 3, no. 12, 2018. Figure 1

A Metal Oxide Gas Sensors Array for Lung Cancer Diagnosis Through Exhaled Breath Analysis.

Lung cancer high mortality rate is mainly related to late-stage tumor diagnosis. Survival rates and treatments could be greatly improved with an effective early diagnosis. Volatile organic compounds (VOCs) in exhaled breath have been known for long to be linked to the presence of a disease. Exhaled breath analysis for early diagnosis of lung cancer represents a non-invasive, low-cost and user-friendly approach. In this paper we present the design and development of an electronic nose based on a metal oxide sensors array for the early diagnosis of lung cancer. Breath samples collected from healthy controls (n=10) and lung cancer subjects (n=6) were analyzed by the electronic nose, and classification was performed using an artificial neural network (ANN). A sensitivity of 85.7%, specificity of 100%, and accuracy of 93.8% were reached with leave one out cross validation (LOOCV). The presented device demonstrates that a simple, cost-effective, and non-invasive approach based on exhaled breath analysis has the potential to be of great help in decreasing lung cancer mortality.

Rapid detection of grape syrup adulteration with an array of metal oxide sensors and chemometrics

Among the different cases of emerging food fraud during the post-harvest processing, the adulteration in grape syrup is one. Typically, the grape syrup is adulterated with some illegitimate foreign materials such as grape paste (sauce), date syrup and even adding sugar-water solution to the pure grape syrup. The present study deals with assessing an electronic nose (e-nose) consisting of eight different metal oxide semiconductor (MOS) sensors for prompt detection of adulteration in the grape syrup. Three different adulterants i.e. grape paste, date syrup and sugar-water solution, each at three levels of 50, 60 and 75%, were tested. The collected data from MOS were normalised and visualised with the help of standard normal variate (SNV) and principal component analysis (PCA), respectively. Moreover, the scores obtained from PCA were used to perform hierarchal cluster analysis (HCA) to identify the similarities between different adulterated mixtures and pure grape syrup. Three different classification cases were considered to (i) address the presence of adulteration, (ii) detect the different adulterants and (iii) classify the amount of each adulteration. Linear discriminant analysis (LDA) and multi-class support vector machine (SVM) were used for classification analyses. Results showed that PCA identified provided separate clusters for the MOS data corresponding to different adulterants and their levels. The HCA showed a hierarchal of similarities between pure grape syrup and different levels of adulterations. LDA and SVM resulted in a successful classification modelling. However, the performance of SVM was considerably better than LDA with classification accuracies of 98.6 ± 0.10%, 98.9 ± 1.16% and 95.1 ± 1.39% for detecting adulteration, different adulterants and different concentrations of adulterants, respectively. MOS sensors coupled with chemometrics could provide a useful instrument and fast procedure for detection of adulteration in grape syrup.