Search Results (20)

Methanol, commonly used to cut costs in the production of counterfeit alcohol, is extremely harmful to human health, potentially leading to severe outcomes, including death. In this study, an electronic nose system was designed using 11 inexpensive gas sensors to detect the proportion of methanol in an alcohol mixture. A total of 168 odor samples were taken and analyzed from eight types of ethanol–methanol mixtures prepared at different concentrations. Only 4 features out of 264 were selected using the feature selection method based on feature importance. These four features were extracted from the data of MQ-3, MQ-4, and MQ-137 sensors, and the classification process was carried out using the data of these sensors. A Voting Classifier, an ensemble model, was used with Linear Discriminant Analysis, Support Vector Machines, and Extra Trees algorithms. The Voting Classifier achieved 85.88% classification accuracy before and 81.85% after feature selection. With its cost effectiveness, fast processing time, and practicality, the recommended system shows great potential for detecting methanol, which threatens human health in counterfeit drink production. Full article

Due to busy schedules and a lack of tracking of food that is stored, a lot of food is wasted every day in households. According to the UNEP Food Waste Index Report 2021, India’s household food waste amounts to 50 kg per person per year, or over 68 million tons. In 2023, India would have produced over 68 million tons of food waste. Food waste is rising quickly every year. The following variables will affect food loss and waste (FLW) at the consumer level: improper food storage, including not using it before it goes bad. Partially used ingredients, preparing meals beyond necessity, and poor visibility of food in freezers are the main causes of food spoiling at home. According to data from the Food Safety and Standards Authority of India (FSSAI), one-third of India’s food is wasted or spoils before it is eaten. This can be minimized by tracking food in smart containers, which work for both packed and unpacked food. This can be used with or without a refrigerator. Full article

The increasing demand for high-quality and safe seafood necessitates the development of efficient monitoring systems to ensure the freshness and safety of fish products. In this research, we present an innovative approach utilizing a sensor array consisting of MQ137, MQ135, MQ3, MQ9, TGS 2610, TGS 2620, TGS 2600, and TGS 822 sensors. These sensors, sensitive to various gases associated with fish spoilage, are integrated into a comprehensive system for fish quality monitoring and spoilage detection. The developed system includes an array of chemical gas sensors, a data acquisition system, a processing unit for handling data, and a machine learning model for classification. The chemical gas sensor array enables the real-time detection of the volatile compounds released during the spoilage of fish. The data acquisition system collects and processes information from the sensor array, while the data processing system extracts relevant features for subsequent analysis. A pattern recognition system, employing a robust LDA-XGBoost model, was employed to differentiate between fresh and spoiled fish. The experimental results demonstrate the system's high accuracy in classifying fish quality, achieving an impressive classification accuracy of 96.12%. The integration of various sensors ensures sensitivity to a broad spectrum of chemical compounds associated with fish spoilage, enhancing the system's reliability. The proposed sensor-based approach provides a cost-effective, rapid, and accurate solution for fish quality monitoring, offering potential applications in the seafood industry to ensure the delivery of safe and fresh products to consumers. Full article

Underground coal mining of gas-bearing coal seams is accompanied by the emission of large amounts of methane, which increases with depth. Coal seam methane is not only a major cause of major accidents in coal mines, but is also a greenhouse gas that has a significant negative impact on the Earth’s atmosphere. Analysis of the efficiency of underground coal mining suggests that as the depth of mining increases, the productivity of a longwall decreases by a factor of 3–5 or more, while the specific volume of methane emitted increases manifold and the efficiency of methane management decreases. Effective management of coal seam methane can only be achieved by monitoring its content at key points in a system of workings. Monitoring of methane not only eliminates the risk of explosions, but also lets us assess the effectiveness of using methane management techniques and their parameters to improve efficiency and reduce the cost of methane management (including a methane drainage) for ensuring sustainable underground coal mining. The aim of this article is to develop a software and hardware complex for monitoring methane in a coal mine by creating a simulation model for monitoring methane. The Arduino Uno board and the methane sensor MQ-4 were used for this purpose. In this article, the causes of methane emissions in coal mines, gas control systems, the structure of the mine monitoring system, and the causes of risks and occurrence of accidents in coal mines are considered. As a result of the work, the mathematical model of the methane measurement sensor was developed; the Arduino Uno board developed a simulation system for methane monitoring; and the numerical results of the research are presented in the graphs. Full article

One of the significant issues facing the world right now is food deterioration. If the freshness or deterioration of a fruit can be determined before it is lost, the fruit waste problem may be mitigated. The goal of this work is to develop a simple model for tracking fruit quality using sensors with a machine learning (ML) approach. This model uses from the gases emitted by fruits to determine the ones that will ripen and require use earlier. Two gas sensors (MQ3 and MQ7) and an Arduino Uno serve as the main processing components of the suggested system. Principal component study (PCA) is a widely employed discriminating approach that has been utilised to differentiate between fresh and rotten apples based on sensed data. The study yielded a cumulative variance of 99.1% over a span of one week. The data were also evaluated using a linear Support vector machine (SVM) classifier, which achieved an accuracy of 99.96%. The distinctive feature of the system is that it evaluates the levels of spoilage based on real-time data and deploys a low-cost, straightforward model that can be used anywhere to preserve any type of fruit. Full article

Degrading air quality is a matter of concern nowadays, and monitoring air quality helps us keep an eye on it. Air pollution is a pressing global issue with far-reaching impacts on public health and the environment. The need for effective and real-time monitoring systems has become increasingly apparent to combat this growing concern. Here, an innovative air pollution surveillance system (APSS) that leverages Internet of Things (IoT) technology to enable comprehensive and dynamic air quality assessment is introduced. The proposed APMS employs a network of Io enabled sensors strategically deployed across urban and industrial areas. These sensors are equipped to measure various pollutants, including particulate matter (PM2.5 and PM10), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), and volatile organic compounds (VOCs). Here, a regression model is created to forecast air quality using sensor data while taking into account variables including weather information, traffic patterns, and pollutants. Additionally, air quality categories (such as good, moderate, and harmful) are classified using classification algorithms based on preset thresholds. The IoT architecture facilitates seamless data transmission from these sensors to a centralized cloud-based platform. The developed APSS monitors the air quality using an MQ-135 gas sensor, and the data are shared over a web server using the Internet. An alarm will trigger when the air quality goes below a certain level. Also, the air quality, which is measured in parts per million (PPM), is displayed on the unit connected to it. Furthermore, when the PPM goes beyond a certain level, an alert message is sent to the air pollution control board, which takes preventive measures to control the pollution and also alerts the people, which helps each person in that society save their environment from pollution and have a good air quality environment. Additionally, the APSS offers user-friendly interfaces, accessible through web and mobile applications, to empower citizens with real-time air quality information. The effectiveness of the IoT-based air pollution monitoring system has been validated through successful field trials in urban and industrial environments, and it has the ability to provide real-time data insights and empower stakeholders in promoting environmental sustainability and fostering citizen engagement. Full article

Ensuring the quality of meat is crucial to preventing health hazards caused by improper handling. To address this issue, a smart packaging system is necessary for continuous monitoring of beef quality and microbial population, benefiting both meat industries and end consumers. The presence of spoilage-causing microbes can be detected using an electronic nose (e-nose), a cost-effective and rapid instrument for beef quality classification. This research introduces the development of a mobile e-nose system for beef quality detection and monitoring. The system comprises a chemical gas sensor array, a data acquisition system, a data processing system, and a pattern recognition system. The gas sensors utilized in the sensor array include MQ135, MQ137, MQ9, MQ3, TGS 2620, TGS 2610, TGS 2600, and TGS 822. The experiment involved a dataset with 1800 data points. The experimental results demonstrate the system’s ability to accurately distinguish between fresh and spoiled beef. Furthermore, it exhibits a promising classification accuracy of 95.89% using the Support Vector Machine model. Therefore, this system presents a potential solution for a low-cost, user-friendly, and real-time meat quality monitoring system. This research contributes to the development of an accessible and efficient meat quality monitoring system, addressing the need for continuous assessment and ensuring consumer safety. Full article

Essential oils are a valuable raw material for several industries. Low-cost methods cannot detect its adulteration; specialised equipment is required. In this paper, we proposed the use of gas sensors to detect the adulteration process in the essential oil of Cistus ladanifer. Gas sensors are used in a measuring chamber to measure pure and adulterated oils. We compare the suitability of the tested sensors for detecting adulterated oil and the required measuring time. A total of five samples are determined, with a measuring time of 12 h. Each gas sensor is configured to be sensitive to different compounds. Even though sensors are not specific to detect the volatile organic compounds (VOCs) present in the essential oil, our objective is to evaluate if these VOCs might interact with the sensors as an interferent. Results indicate that various gas sensors sensitive to the same chemical compound offered different values. It might indicate that the interaction of VOCs is different among the tested sensors or that the location of the sensors and the heterogeneous distribution of VOCs along the measurement chamber impact the data. Regarding the performed analyses, we can affirm that identifying the adulterated essential oil is possible using the generated data. Moreover, the results suggest that most of the data, even for different compounds and sensors, are highly correlated, allowing a reduction in the studied variables. According to the high correlation, data are reduced, and 100% of correct classification can be obtained even when only the MQ3 and MQ8 are used. Full article

Automation can now be found in nearly every industry. However, home automation has yet to reach Pakistan. This paper presents an Internet of Things smart kitchen project that includes automation and monitoring. In this project, a system was developed that automatically detects the kitchen temperature. It also monitors the humidity level in the kitchen. This system includes built-in gas detection sensors that detect any gas leaks in the kitchen and notify the user if the gas pressure in the kitchen exceeds a certain level. This system also allows the user to remotely control appliances such as freezers, ovens, and air conditioners using a mobile phone. The user can control gas levels using their phone with this system. In this paper, the ESP32, DHT11 Sensor, 5 V Relay X 8, and MQ-135 gas sensors create a smart kitchen by controlling the temperature, managing humidity, and detecting gas leakage. The system was built on an Arduino board that is connected to the Internet. The hardware was integrated and programmed using an Arduino, and a user Android application was developed. The project’s goal is to allow any Android smartphone to remotely control devices. This method is commonly used in homes, businesses, and grocery stores. Users will be able to control all of their instruments from anywhere, including switches, fans, and lights. Furthermore, simulation was performed using Matlab2016b on multiple houses. In the simulation, not only was the kitchen considered, but also two, four, and six houses. Each house has two bedrooms, one living room, one guest room, two bathrooms, and one kitchen. The results revealed that using this system will have a scientifically significant impact on electricity consumption and cost. In the case of the houses, the cost was USD 33.32, 32.64, 22.32, and 19.54 for unscheduled, two, four, and six houses, respectively. Thus, it was observed that the cost and power are directly proportional to each other. The results reveal that the proposed solution efficiently reduces the cost as compared to that of unscheduled houses. Full article

The paper presents the results of a 1.5-year evaluation study of low- and medium-cost ozone sensors. The tests covered electrochemical sensors: SensoriC O3 3E 1 (City Technology) and semiconductor gas sensors: SM50 OZU (Aeroqual), SP3-61-00 (FIS) and MQ131 (Winsen). Three copies of each sensor were enclosed in a measurement box and placed near the reference analyser (MLU 400). In the case of SensoriC O3 3E 1 sensors, the R² values for the 1-h data were above 0.90 for the first 9 months of deployment, but a performance deterioration was observed in the subsequent months (R² ≈ 0.6), due to sensor ageing processes. High linear relationships were observed for the SM50 devices (R² > 0.94), but some periodic data offsets were reported, making regular checking and recalibration necessary. Power-law functions were used in the case of SP3-61-00 (R² = 0.6–0.7) and MQ131 (R² = 0.4–0.7). Improvements in the fittings were observed for models that included temperature and relative humidity data. In the case of SP3-61-00, the R² values increased to above 0.82, while for MQ131 they increased to above 0.86. The study also showed that the measurement uncertainty of tested sensors meets the EU Directive 2008/50/EC requirements for indicative measurements and, in some cases, even for fixed measurements. Full article

Fire alarm systems are typically equipped with various sensors such as heat, smoke, and gas detectors. These provide fire alerts and notifications of emergency exits when a fire has been detected. However, such systems do not give early warning in order to allow appropriate action to be taken when an alarm is first triggered, as the fire may have already caused severe damage. This paper analyzes a new dataset gathered from controlled realistic fire experiments conducted in an indoor laboratory environment. The experiments were conducted in a controlled manner by triggering the source of fire using electrical devices and charcoal on paperboard, cardboard or clothing. Important data such as humidity, temperature, MQ139, Total Volatile Organic Compounds (TVOC) and eCO₂ were collected using sensor devices. These datasets will be extremely valuable to researchers in the machine learning and data science communities interested in pursuing novel advanced statistical and machine learning techniques and methods for developing early fire detection systems. The analysis of the collected data demonstrates the possibility of using eCO₂ and TVOC reading levels for early detection of smoldering fires. The experimental setup was based on Low-Power Wireless Area Networks (LPWAN), which can be used to reliably deliver fire-related data over long ranges without depending on the status of a cellular or WiFi Network. Full article

An ammonia gas (NH₃) generator was developed to maintain a set concentration of ammonia gas in a controlled environment chamber to study poultry physiological responses to sustained elevated levels of ammonia gas. The goal was to maintain 50 parts per million (ppm) of ammonia gas in a 3.7 m × 4.3 m × 2.4 m (12 ft × 14 ft × 8 ft) controlled environment chamber. The chamber had a 1.5 m³/s (3000 cfm) recirculation system that regulated indoor temperature and humidity levels and a 0.06 m³/s (130 cfm) exhaust fan that exchanged indoor air for fresh outdoor air. The ammonia generator was fabricated by coupling ultrasonic humidifiers with an Arduino-based microcontroller and a metallic oxide MQ-137 ammonia gas sensor. Preliminary evaluation under steady conditions showed the average MQ-137 gas sensor accuracy was within 1.4% of the 65.4 ppm concentration measured using a highly accurate infrared gas analyzer. Further evaluation was performed for a setpoint concentration of 50 ppm where ammonia generator reservoirs were filled with 2% or 10% ammonia liquid. For the system tested, it was found that two generators operating at the same time filled with 3.8 L (1.0 gallon) of 2% ammonia cleaning liquid each (7.6 L or 2.0 gallons total) could maintain a gas level of 49.45 ± 0.79 ppm in the chamber air for a duration of 30 h before refilling was required. One generator filled with 3.8 L of 10% ammonia cleaning liquid could maintain 51.24 ± 1.53 ppm for 195 h. Two ammonia generators were deployed for a six-week animal health experiment in two separate controlled environment chambers. The two ammonia generators maintained an average ammonia concentration of 46.42 ± 3.81 ppm and 45.63 ± 4.95 ppm for the duration of the experiment. Full article

Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p < 0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = −0.93), decanoic acid, ethyl ester (r = −0.94), and octanoic acid, 3-methylbutyl ester (r = −0.89). The two models developed in this study may offer winemakers a rapid, cost-effective, and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making. Full article

This research focuses on the production and construction stages of the life cycle analysis (LCA) of asphalt mixtures modified with industrial waste and by-products, based on the quantification of methane (CH₄), carbon monoxide (CO) and carbon dioxide (CO₂) emissions produced during these processes. A laboratory-designed and calibrated gas measurement system with a microcontroller and MQ sensors is used to compare the emissions (GHG) of a “conventional” asphalt mix with those emitted by waste-modified asphalt mixes (polyethylene terephthalate and nylon fibres) and industrial by-products (copper slag and cellulose ash). The results obtained show that the gases emitted by each type of material can influence the design criteria from an environmental perspective. Methane gas emissions for asphalt mixes made with polymeric materials increase compared to the production phase of a conventional mix (M1) by 21% for PET and 14% for nylon. In contrast, for mixtures made with copper slag and cellulose ash, this emission is reduced by 12%. In addition, the use of copper slag and cellulose ash to replace natural aggregates reduces greenhouse gas emissions by 15% during the production phase and contributes to the creation of photochemical ozone for a shorter period of time. Regarding carbon dioxide emission, it increases considerably for all asphalt mixes, by 26% and 44.5% for cellulose ash and copper slag, respectively. For asphalt mixtures made of polymeric materials, the increase in carbon dioxide emission is significant, 130% for PET and 53% for nylon. In addition, it is noted that for this type of material, not only the emission of the gas must be taken into consideration, but also the time that the volatile particles spend in the atmosphere, affecting climate change and photochemical ozone (smog). The carbon monoxide gases emitted in the production phase of all the asphalt mixes analysed is similar among them. Full article

Global warming, as the result of the negative impact of humans on climate change, has been observed based on various data sources. Various measures have aimed to reduce anthropogenic factors, and also to lower carbon dioxide (CO₂) and methane CH₄ emissions. One of the main contributors to anthropogenic factors is organic waste in municipal solid waste landfills. There are many landfills where cost-effective rapid technologies for the identification and quantification of CH₄ emission sites are not applied. There is still a need for the development of accessible and cost-effective methods that react in a real-time manner for the rapid detection and monitoring of methane emissions. This paper’s main goal is to create a prototype sensor suitable for operational measurement of the gas value, suitable for integration into geodetic equipment or an unmanned aerial vehicle system. A sensor system (device) was developed, which consisted of three semiconductor sensors—MQ2, MQ4, and MQ135—which aimed to capture flammable gases (CO₂, CH₄, O₂ purity) and to evaluate the averages of the measured values from the components mounted on the board—the semiconductor sensors. The sensors were calibrated in a laboratory and tested in a closed landfill. The measurement data consisted of the read resistances (analog signal) from the MQ2, MQ4, and MQ135 sensors, and the relative humidity and the temperature (digital signal) of the DHT2 sensor with a timestamp calculated by the RTC module. The use of the method was confirmed because the sensors reacted as expected when placed in the vicinity of the gas collection well. Furthermore, the sensor will be tested and improved for field work in landfill sites. Full article