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The application of electronic nose system as an instrument for exhaled air analysis for diagnosing various diseases (lung cancer, etc.) is one of the most rapidly developing areas of predictive medicine. However, a significant disadvantage of such systems is difficulty in production of identical multi-sensor systems because the sensors of the same type are not identical, that degrades the predictive ability. To solve this problem calibration transfer methods are used.This paper analyzes currently applied calibration transfer methods. A new approach to evaluate the effectiveness of calibration transfer methods for two multi-sensor systems is proposed and implemented. One- and three-component gas mixtures are taken for the analysis. In this work, four methods are used to adjust the sensor responses: UDS, UDSwoi, DS-L1R and DS-PLS2. Applicability of one-factor standardization methods (UDS and UDSwoi) is shown on the classification tasks: multi-class classification task of one-component gas mixtures and binary classification task of three-component gas mixtures. Multi-class classification of model one-component gas mixtures for UDS, UDSwoi with 4 calibration transfer samples provided the classification accuracy of 0.985 and 0.990, respectively. The binary classification of two three-component mixtures with different ratios of three VOCs within the 20% discrepancy limits, UDS, UDSwoi and DS-PLS2 achieved an error-free accuracy of 1.000.

Antibiotic resistance is widespread throughout the world and represents a serious health concern. There is an urgent need for the development of novel tools for rapidly distinguishing antibiotic resistant bacteria from susceptible strains. Previous work has demonstrated that differences in antimicrobial susceptibility can be reflected in differences in the profile of volatile organic compounds (VOCs) produced by dissimilar strains. The aim of this study was to investigate the effect of the presence of cephalosporin antibiotics on the VOC profile of extended spectrum beta-lactamase (ESBL) and non-ESBL producing strains of Escherichia coli. In this study, VOCs from strains of Escherichia coli positive and negative for the most commonly encountered ESBL, CTX-M in the presence of cephalosporin antibiotics were assessed using solid-phase microextraction (SPME) coupled with a combined gas chromatography-mass spectrometry/metal oxide sensor (GC-MS/MOS) system. Our proof-of-concept study allowed for distinguishing CTX-M positive and negative bacteria within 2 ​h after the addition of antibiotics. One MOS signal (RT: 22.6) showed a statistically significant three-way interaction (p ​= ​0.033) in addition to significant two-way interactions for culture and additive (p ​= ​0.046) plus time and additive (p ​= ​0.020). There were also significant effects observed for time (p ​= ​0.009), culture (p ​= ​0.030) and additive (p ​= ​0.028). No effects were observed in the MS data. The results of our study showed the potential of VOC analysis using SPME combined with a GC-MS/MOS system for the early detection of CTX-M-producing, antibiotic-resistant E. coli, responsible for urinary tract infections (UTIs). [ABSTRACT FROM AUTHOR]

This work describes the technical features and the performance of two different types of metal-oxide semiconductor sensors, based on ZnO:Ga thin films and SnO2-G nanofibrous layers, for tropospheric ozone monitoring in ambient air. These nanostructures were tested and compared with commercial metal-oxide semiconductor sensors under controlled laboratory conditions and in a field campaign during summer 2021 in Monfragüe National Park (western Spain). The paper also details the design of the electronic device developed for this purpose. A machine learning algorithm based on Support Vector Regression (SVR) allowed the conversion of the resistive values into ozone concentration, which was evaluated afterward. The results showed that the manufactured sensors performed similarly to the commercial sensors in terms of R2 (0.94 and 0.95) and RMSE (5.21 and 4.83 μg∙m−3). Moreover, a novel uncertainty calculation based on European guides for air quality sensor testing was conducted, in which the manufactured sensors outperformed the commercial ones.

In this paper, we present an experimentally validated end-to-end channel model for molecular communication systems with metal-oxide sensors. In particular, we focus on the recently developed tabletop molecular communication platform. Unlike previous work, this paper separates the system into two parts-the propagation and the sensing. Based on this separation, a more realistic channel model is derived. The coefficients in the derived models are estimated using a large collection of experimental data. It is shown how the coefficients change as a function of different system parameters such as distance, spraying duration, and an initial condition. Finally, a noise model is derived for the system to complete an end-to-end system model for the tabletop platform that can be utilized with various system variables. Using this new channel model, we propose a multi-level modulation technique that represents different symbols with different spraying durations while still providing more feasibility and less computational complexity in practice.

Dissolved hydrogen (dH 2) is an important parameter in anaerobic digestion (AD) processes, in particular in a two-stage operation mode encompassing separate hydrolytic/acidogenic and methanogenic stages. Then, monitoring of dH 2 as a substrate of hydrogenotrophic methanogenesis is essential to avoid feast and famine conditions. Despite its significance, there is currently a lack of established monitoring systems which are capable of reliably measuring dH 2 concentrations in culture broth. To address this challenge, we propose a novel measurement system, which is based on the membrane-free extraction of hydrogen in an extraction chamber and the subsequent analysis by a metal-oxide (MOX) gas sensor. The response time of the MOX sensor lies in the range of seconds, while the entire measurement process completes within a total processing time of about 70 min). This study explores the measurement performance of the dH 2 sensor in the hydrolytic/acidogenic and methanogenic stage during lab-scale anaerobic digestion. The measurement principle was consistently applied for over three months. During this period, the methanogenic stage was partly sparged with gaseous hydrogen to monitor the dH 2 response afterwards. The dH 2 sensor responded reliably to these and other dynamic changes. Depending on the process conditions, concentrations between < 10 and > 4,000 Pa were detected, corresponding to dH 2 concentrations of < 0.074 to > 30 µmol L−1. The findings demonstrated the importance of dH 2 monitoring and show that it facilitates the control of H 2 addition, thereby preventing both under- and oversupply during methanogenesis. [Display omitted] • Quantification of dissolved hydrogen with metal-oxide sensor. • Measurement range and long-term stability suitable for anaerobic fermentation broth. • Reliable detection of dissolved H 2 concentrations during H 2 feeding. • Insights into microbial uptake of hydrogen in anaerobic culture broth. • Flexible production of biogas and carboxylic acids in two-stage anaerobic digestion. [ABSTRACT FROM AUTHOR]

Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring.

Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography–mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.

Abstract: A multisensor platform on plastic foil for environmental monitoring has been produced and its gas sensing performance, investigated. It is an array of conductometric metal-oxide (MOX) and capacitive polymer gas sensors integrated with a resistive platinum thermometer on a polyimide sheet substrate. The feasibility of simultaneous measurement of oxidizing and reducing gases, volatile organic compounds (VOCs), humidity and temperature has been demonstrated. MOX signals comparable with those of the devices realized on ceramic substrates have been obtained. Due to its structure, the platform is very versatile and, by using different sensor configurations and sensing materials, it allows the detection of a broad spectrum of gaseous analytes over wide concentration ranges. From the raw signals, temperature and humidity-corrected gas responses have been inferred which have been used for the calibration of the platform sensors. All the integrated devices were stable and gave reproducible signals for more than two months of operation, even when the MOXs ran continuously at 300°C. The performed investigation proved the device concept viability and the reliability of its practical implementation. [Copyright &y& Elsevier]

Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography–mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications. [ABSTRACT FROM AUTHOR]