Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.

Wireless E-Nose Sensors to Detect Volatile Organic Gases through Multivariate Analysis

Gas sensors are critical components when adhering to health safety and environmental policies in various manufacturing industries, such as the petroleum and oil industry; scent and makeup production; food and beverage manufacturing; chemical engineering; pollution monitoring. In recent times, gas sensors have been introduced to medical diagnostics, bioprocesses, and plant disease diagnosis processes. There could be an adverse impact on human health due to the mixture of various gases (e.g., acetone (A), ethanol (E), propane (P)) that vent out from industrial areas. Therefore, it is important to accurately detect and differentiate such gases. Towards this goal, this paper presents a novel electronic nose (e-nose) detection method to classify various explosive gases. To detect explosive gases, metal oxide semiconductor (MOS) sensors are used as reliable tools to detect such volatile gases. The data received from MOS sensors are processed through a multivariate analysis technique to classify different categories of gases. Multivariate analysis was done using three variants—differential, relative, and fractional analyses—in principal components analysis (PCA). The MOS sensors also have three different designs: loading design, notch design, and Bi design. The proposed MOS sensor-based e-nose accurately detects and classifies three different gases, which indicates the reliability and practicality of the developed system. The developed system enables discrimination of these gases from the mixture. Based on the results from the proposed system, authorities can take preventive measures to deal with these gases to avoid their potential adverse impacts on employee health.

Nanostructured MOS Sensor for the Detection, Follow up, and Threshold Pursuing of Campylobacter Jejuni Development in Milk Samples

Food poisoning is still the first cause of hospitalization worldwide and the most common microbial agent, Campylobacter jejuni, is the most commonly reported gastrointestinal disease in humans in the EU (European Union) as is reported by the European Union One Health 2018 Zoonoses Report styled by the EFSA (European Food Safety Authority) and ECDC (European Center for Disease Prevention and Control). One of the vehicles of transmission of this disease is milk. Nanostructured MOS (Metal Oxide Semiconductor) sensors have extensively demonstrated their ability to reveal the presence and follow the development of microbial species. The main objective of this work was to find a set up for the detection and development follow up of C. jejuni in milk samples. The work was structured in two different studies, the first one was a feasibility survey and the second one was to follow up the development of the bacteria inside milk samples. The obtained results of the first study demonstrate the ability of the sensor array to differentiate the contaminated samples from the control ones. Thanks to the second study, it has been possible to find the limit of microbial safety of the contaminated milk samples.

Fabrication of SnO2 Composite Nanofiber-Based Gas Sensor Using the Electrospinning Method for Tetrahydrocannabinol (THC) Detection

This paper presents the development of a metal oxide semiconductor (MOS) sensor for the detection of volatile organic compounds (VOCs) which are of great importance in many applications involving either control of hazardous chemicals or noninvasive diagnosis. In this study, the sensor is fabricated based on tin dioxide (SnO2) and poly(ethylene oxide) (PEO) using electrospinning. The sensitivity of the proposed sensor is further improved by calcination and gold doping. The gold doping of composite nanofibers is achieved using sputtering, and the calcination is performed using a high-temperature oven. The performance of the sensor with different doping thicknesses and different calcination temperatures is investigated to identify the optimum fabrication parameters resulting in high sensitivity. The optimum calcination temperature and duration are found to be 350 °C and 4 h, respectively and the optimum thickness of the gold dopant is found to be 10 nm. The sensor with the optimum fabrication process is then embedded in a microchannel coated with several metallic and polymeric layers. The performance of the sensor is compared with that of a commercial sensor. The comparison is performed for methanol and a mixture of methanol and tetrahydrocannabinol (THC) which is the primary psychoactive constituent of cannabis. It is shown that the proposed sensor outperforms the commercial sensor when it is embedded inside the channel.

Performance of gas nanosensor in 1-4 per cent hydrogen concentration

Purpose This study aims to investigate the fabrication of hydrogen gas sensor based on metal–oxide–semiconductor (MOS) microstructure. The palladium nanoparticles (PdNPs) as gate metal have been deposited on the oxide film using spin coating. Design/methodology/approach The PdNPs and the surface of oxide film were analyzed using Transmission electron microscopy. The capacitance-voltage (C-V) curves for the MOS sensor in 1, 2 and 4 per cent hydrogen concentration and in 100 KHz frequency at the room temperature were reported. Findings The response times for 1, 2 and 4 per cent hydrogen concentration were 2.5 s, 1.5 s and 1 s, respectively. The responses (R per cent) of MOS sensor to 1, 2 and 4 per cent hydrogen concentration were 42.8, 47.3 and 52.6 per cent, respectively. Originality/value The experimental results demonstrate that the MOS hydrogen gas sensor based on the PdNPs gate, shows the fast response and recovery compared to other hydrogen gas sensors based on the Pd.