Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors

Integrated Smart Gas Tracking Device with Artificially Tailored Selectivity for Real-Time Monitoring Food Freshness

The real-time monitoring of food freshness in refrigerators is of significant importance in detecting potential food spoiling and preventing serious health issues. One method that is commonly reported and has received substantial attention is the discrimination of food freshness via the tracking of volatile molecules. Nevertheless, the ambient environment of low temperature (normally below 4 °C) and high humidity (90% R.H.), as well as poor selectivity in sensing gas species remain the challenge. In this research, an integrated smart gas-tracking device is designed and fabricated. By applying pump voltage on the yttria-stabilized zirconia (YSZ) membrane, the oxygen concentration in the testing chamber can be manually tailored. Due to the working principle of the sensor following the mixed potential behavior, distinct differences in sensitivity and selectivity are observed for the sensor that operated at different oxygen concentrations. Typically, the sensor gives satisfactory selectivity to H2S, NH3, and C2H5OH at the oxygen concentrations of 10%, 30%, and 40%, respectively. In addition, an acceptable response/recovery rate (within 24 s) is also confirmed. Finally, a refrigerator prototype that includes the smart gas sensor is built, and satisfactory performance in discriminating food freshness status of fresh or semi-fresh is verified for the proposed refrigerator prototype. In conclusion, these aforementioned promising results suggest that the proposed integrated smart gas sensor could be a potential candidate for alarming food spoilage.

Design of a Multisensory Device for Tomato Volatile Compound Detection Based on a Mixed Metal Oxide-Electrochemical Sensor Array and Optical Reader

Insufficient control of tomato ripening before harvesting and infection by fungal pests produce large economic losses in world tomato production. Aroma is an indicative parameter of the state of maturity and quality of the tomato. This study aimed to design an electronic system (TOMATO-NOSE) consisting of an array of 12 electrochemical sensors, commercial metal oxide semiconductor sensors, an optical camera for a lateral flow reader, and a smartphone application for device control and data storage. The system was used with tomatoes in different states of ripeness and health, as well as tomatoes infected with Botrytis cinerea. The results obtained through principal component analysis of the olfactory pattern of tomatoes and the reader images show that TOMATO-NOSE is a good tool for the farmer to control tomato ripeness before harvesting and for the early detection of Botrytis cinerea.

Potentiometric-Type Gas Sensor Using MgFe2O4 Sensing Electrode for Detection of Hydrocarbon Based on Carbon Number

Hydrocarbon (HC) monitoring is necessary for safe and effective operations in industries such as petroleum and gas. In this study, total hydrocarbons can be detected by using yttria-stabilized zirconia (YSZ)-based potentiometric-type gas sensor using MgFe₂O₄ sensing electrode (SE). The sensor was found to generate a similar response magnitude to those of hydrocarbons that have the same carbon number, irrespective of the type of carbon bond (total hydrocarbon detection). Aside from being capable of detecting total hydrocarbons sensitively and selectively with rapid response time, the sensor using MgFe₂O₄-SE also exhibited a linear relationship between sensor responses and carbon number. In addition to that, the developed sensor showed a logarithmically linear relationship between sensor responses and HC concentration in the range 20-700 ppm. These sensing characteristics were confirmed to be reproducible, and sensor responses toward HC were found to be repeatable and gradually decreased with increasing in O₂ concentration in the range of 3-21 vol %.

Classification of Three Volatiles Using a Single-Type eNose with Detailed Class-Map Visualization

The use of electronic noses (eNoses) as analysis tools are growing in popularity; however, the lack of a comprehensive, visual representation of how the different classes are organized and distributed largely complicates the interpretation of the classification results, thus reducing their practicality. The new contributions of this paper are the assessment of the multivariate classification performance of a custom, low-cost eNose composed of 16 single-type (identical) MOX gas sensors for the classification of three volatiles, along with a proposal to improve the visual interpretation of the classification results by means of generating a detailed 2D class-map representation based on the inverse of the orthogonal linear transformation obtained from a PCA and LDA analysis. The results showed that this single-type eNose implementation was able to perform multivariate classification, while the class-map visualization summarized the learned features and how these features may affect the performance of the classification, simplifying the interpretation and understanding of the eNose results.

Selectivity enhancement for metal oxide (MOX) based gas sensor using thermally modulated datasets coupled with golden section optimization and chemometric techniques

The ever-increasing demand for smart sensors for internet of things applications drove the change in outlook toward smart sensor system design. This paper focuses on using low-cost gas sensors [Metal Oxide (MOX)] for detection of more than one gas, which is otherwise complex due to poor selectivity of MOX sensors. In this work, detection of two gases, namely, ammonia (NH₃) and carbon monoxide (CO), using a single metal oxide (pristine tin oxide) sensor is demonstrated. Furthermore, chemometric based algorithms have been used to classify and quantify both gases. The present investigation uses the temperature modulated gas sensor response obtained at different concentrations for the mentioned gases. The golden section based optimization technique has been employed to obtain two different ranges of temperatures for both gases. After applying certain pre-processing techniques, the acquired data from the sensors were fed to various classification techniques, such as partial least squares (PLS) discriminant analysis, k-means, and soft independent modeling by class analogy, and 100% classification results were obtained. Furthermore, PLS regression (PLS-R) was used to perform quantitative analysis on the data using the optimized temperature ranges for both gases, and R² regression coefficients, 0.999 25 for NH₃ and 0.9399 for CO, were obtained. The results obtained from both the qualitative and quantitative analyses make our approach low-cost and smart to mitigate the cross-selectivity of metal oxide semiconductor based smart sensor design.

Assessing over Time Performance of an eNose Composed of 16 Single-Type MOX Gas Sensors Applied to Classify Two Volatiles

This paper assesses the over time performance of a custom electronic nose (eNose) composed of an array of commercial low-cost and single-type miniature metal-oxide (MOX) semiconductor gas sensors. The eNose uses 16 BME680 versatile sensor devices, each including an embedded non-selective MOX gas sensor that was originally proposed to measure the total volatile organic compounds (TVOC) in the air. This custom eNose has been used previously to detect ethanol and acetone, obtaining initial promising classification results that worsened over time because of sensor drift. The current paper assesses the over time performance of different classification methods applied to process the information gathered from the eNose. The best classification results have been obtained when applying a linear discriminant analysis (LDA) to the normalized conductance of the sensing layer of the 16 MOX gas sensors available in the eNose. The LDA procedure by itself has reduced the influence of drift in the classification performance of this single-type eNose during an evaluation period of three months.