Electronic Noses Applications in Beer Technology

This chapter describes and explains in detail the electronic noses (e-noses) as devices composed of an array of sensors that measure chemical volatile compounds and apply classification or regression algorithms. Then, it reviews the most significant applications of such devices in beer technology, with examples about defect detection, hop classification, or beer classification, among others. After the review, the chapter illustrates two applications from the authors, one about beer classification and another about beer defect detection. Finally, after a comparison with other analytical techniques, the chapter ends with a summary, conclusions, and the compelling future of the e-noses applied to beer technology

Environmental engineering applications of electronic nose systems based on MOX gas sensors

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors’ applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications

Electronic Noses for Biomedical Applications and Environmental Monitoring

This book, titled “Electronic Noses for Biomedical Applications and Environmental Monitoring”, includes original research works and reviews concerning the use of electronic nose technology in two of the more useful and interesting fields related to chemical compounds detection of gases. Authors have explained their latest research work, including different gas sensors and materials based on nanotechnology and novel applications of electronic noses for the detection of diverse diseases. Some reviews related to disease detection through breath analysis, odor monitoring systems standardization, and seawater quality monitoring are also included

Review of low-cost sensors for the ambient air monitoring of benzene and other volatile organic compounds

This report presents a literature review of the state of the art of sensor based monitoring of air quality of benzene and other volatile organic compounds. Combined with information provided by stakeholders, manufacturers and literature, the review considered commercially available sensors, including, PID based sensors, semiconductor (resistive gas sensor) and portable on-line measuring devices (sensor arrays). The bibliographic collection includes the following topics: sensor description, field of application in fixed, mobile, indoor and ambient air monitoring, range of concentration levels and limit of detection in air, model descriptions of the phenomena involved in the sensor detection process, gaseous interference selectivity of sensors in complex VOC matrix, validation data in lab experiments and under field conditions.JRC.C.5-Air and Climat

CMUT based chemical sensor for classification and quantification with machine learning in a real-world application

In a quest for further enhancing human senses, chemical sensors are developed. Chemical sensors are proved to diagnose diseases, classify and quantify chemical warfare agents as well as measuring air pollution down to parts per billion [1-3]. Connecting multiple devices in large networks can help authorities and governments respond faster and make better decisions considering the release of emissions and/or dangerous gases. In order to create such networks, an inexpensive, robust and portable sensor must be developed. The chemical capacitive micromachined ultrasonic transducer (CMUT) might be such a sensor. This thesis demonstrates a proof of concept for a CMUT based chemical sensor as a gas detecting unit that can classify and quantify chemicals with machine learning in a real-world application. The CMUT is a sensor consisting of an array of polymer coated cells adsorbing different gases. Adsorption causes a frequency shift in the sensor output. This shift can be correlated to chemicals and their concentrations through machine learning. Reference data collected for the machine learning models was identified as a time-consuming process. An autosampler was devised, reducing time and cost related to the data collection. The CMUT sensor was tested in a greenhouse for 4 weeks to measure CO2 concentration in a plant bed under varying conditions. Testing the following statement: If the sensor can detect low concentrations of CO2 in ambient air it can also detect other compounds. The machine learning models were trained on the collected samples, and later compared to find the best model. The results showed that the CMUT sensor successfully measured CO2 down to 120 ppm in ambient air, the machine learning models could classify between high and low concentrations. For classification purposes the neural network with relu activation showed the best results, with a 15% error for both high and low concentrations. Quantification of the data had poor performance due to sensor drift. Large RMSE scores was found for all quantification models. The drift is most likely caused by the breakdown of the polymer, causing a frequency shift. The dataset was unbalanced and had a higher distribution on lower concentrations. Which to some extent undermine the results from the machine learning, although giving an indication of sensor performance. Further research is recommended to assess the polymer coating on the CMUT as well as removing drift. Reducing the size of the sensor and equipment, as well as connecting the sensor to a cloud database, is recommended and identified as important steps for creating a sensor network.I søken etter å forbedre menneskets sanser ønsker man å utvikle kjemiske sensorer. Kjemiske sensorer har blitt brukt til å diagnostisere sykdommer, klassifisere og kvantifisere nervegass i tillegg til å måle luftforurensing som har svært lav oppløsning. Ved å sette sammen flere elektroniske neser i større nettverk vil det bidra med økt informasjon om utslipp i byer. Dette vil hjelpe myndigheter med å ta bedre og raskere beslutninger for å unngå spredning av farlige kjemikalier og/eller forurensning. For å lage slike nettverk må sensorene som benyttes være pålitelige, kostnadseffektive og robuste. En sensor som oppfyller disse kravene er den kjemiske kapasitive mikromaskinerte ultralyd transduceren (CMUT).M-MP

Study of Linkage between Indoor Air Quality along with Indoor Activities and the Severity of Asthma Symptoms in Asthma Patients

Asthma, a chronic respiratory disease affecting millions of people worldwide, can vary in severity depending on individual triggers such as Carbon Dioxide, Particulate Matter, dust mites, tobacco smoke, and indoor household activities such as cooking, cleaning, use of heating, and window opening, which can have a negative impact on indoor air quality (IAQ) and exacerbate asthma symptoms. Investigating the relationship between IAQ and asthma severity, a case study was conducted on five asthmatic participants from Bradford, UK. IAQ was measured using IoT indoor air quality monitoring devices. Indoor activities were recorded using a daily household activities questionnaire, and asthma severity was assessed using the Asthma Control Questionnaire (ACQ). Machine learning prediction models were used to analyse various IAQ parameters, such as particulate matter, carbon dioxide, and humidity levels, to identify the most significant predictors of asthma severity with IAQ. The study aimed to develop targeted interventions to improve IAQ and reduce the burden of asthma. Results showed that higher asthma severity scores were associated with increased indoor activity and higher levels of indoor air pollution. Some interventions were implemented to improve ventilation hours, significantly improving IAQ and reducing asthma symptoms, particularly those with more severe asthma. The findings indicate that interventions targeting IAQ, and indoor activities can effectively reduce asthma severity, with up to a 60% reduction in symptoms for asthma patients

An informatics based approach to respiratory healthcare.

By 2005 one person in every five UK households suffered with asthma. Research has shown that episodes of poor air quality can have a negative effect on respiratory health and is a growing concern for the asthmatic. To better inform clinical staff and patients to the contribution of poor air quality on patient health, this thesis defines an IT architecture that can be used by systems to identify environmental predictors leading to a decline in respiratory health of an individual patient. Personal environmental predictors of asthma exacerbation are identified by validating the delay between environmental predictors and decline in respiratory health. The concept is demonstrated using prototype software, and indicates that the analytical methods provide a mechanism to produce an early warning of impending asthma exacerbation due to poor air quality. The author has introduced the term enviromedics to describe this new field of research. Pattern recognition techniques are used to analyse patient-specific environments, and extract meaningful health predictors from the large quantities of data involved (often in the region of '/o million data points). This research proposes a suitable architecture that defines processes and techniques that enable the validation of patient-specific environmental predictors of respiratory decline. The design of the architecture was validated by implementing prototype applications that demonstrate, through hospital admissions data and personal lung function monitoring, that air quality can be used as a predictor of patient-specific health. The refined techniques developed during the research (such as Feature Detection Analysis) were also validated by the application prototypes. This thesis makes several contributions to knowledge, including: the process architecture; Feature Detection Analysis (FDA) that automates the detection of trend reversals within time series data; validation of the delay characteristic using a Self-organising Map (SOM) that is used as an unsupervised method of pattern recognition; Frequency, Boundary and Cluster Analysis (FBCA), an additional technique developed by this research to refine the SOM

Assessment of Occupational Exposures in the 3D Printing: Current Status and Future Prospects

3D (three-dimensional) printing technologies are widespread and rapidly evolving, creating new specific working conditions, and their importance has been highlighted by increasing publications in recent years. The report provides a compilation of current information on 3D technologies, materials, and measurements, considering the determination of the potential actual exposure dose of chemicals through airborne inhalation and dermal exposure, including workers’ exhaled breath condensate and urine data. Noninvasive assessment methods are becoming increasingly popular, as they are painless, easy to perform, and inexpensive. Investigation of biomarkers reflecting pulmonary inflammation and local and systemic oxidative stress in exhaled breath, exhaled breath condensate, and urine are among them. It is also important to consider the occupational health and safety risks associated with the use of various new materials in 3D printing, which are associated with skin irritation and sensitivity risks. Therefore, EDI (estimated daily intake) calculations for assessment of the potential occupational health risk purposes via inhalation and dermal exposure are critical in future. The assessment of occupational exposure and health risks of 3D printing processes is essential for the proper identification, control, and prevention of working conditions, also for the diagnosis and monitoring of occupational diseases among workers to improve public health and well-being in general