Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification

Characterizing the Effect of Simultaneous Enhancements of Reducing Gas Species on Figaro Taguchi Gas Sensor Resistance Response

The resistance of the Figaro Taguchi Gas Sensor (TGS) decreases when exposed to reducing gas enhancements. TGS gas response can be characterized by comparing measured resistance to a reference resistance, representative of sampling in identical environmental conditions but with no reducing gas enhancement. Thus, this resistance ratio (RR) allows for characterization of reducing gas response, independent of other environmental effects. This work presents controlled laboratory experiments, measurements, and modeling for an analysis on the effect of reducing gas cross-sensitivities on RR. The methane mole fraction ([CH4]) was raised to approximately 9 ppm from a 0.492 ppm reference level, and carbon monoxide mole fraction ([CO]) was raised to approximately 4 ppm from a 0 ppm reference level, through multiple simultaneous steps. The independent effect of each gas on RR was directly multiplied, resulting in an inferior RR compared with measurements, implying an interdependence effect. For example, for one TGS unit, when deriving [CH4] from RR, a 6 ppm [CH4] measurement would be underestimated by 6% at 1 ppm [CO], but only by 1.6% at 0.1 ppm [CO]. A key implication of residual interdependence effects is that any gas characterization must be conducted with the same reference levels of each other reducing gas expected during field deployment, even if measuring a single gas. A first-order interdependence correction is proposed to account for such interdependence effects. Yet, each TGS behaves differently, and interdependence testing takes time. Therefore, the TGS best serves to detect single reducing gases, assuming all other reducing gases to remain constant at their reference levels.

Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose

The printing process of box packaging paper can generate volatile organic compounds, resulting in odors that impact product quality and health. An efficient, objective, and cost-effective detection method is urgently needed. We utilized a self-developed electronic nose system to test four different cigarette packaging paper samples. Employing multivariate statistical methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Statistical Quality Control (SQC), and Similarity-based Independent Modeling of Class Analogy (SIMCA), we analyzed and processed the collected data. Comprehensive evaluation and quality control models were constructed to assess sample stability and distinguish odors. Results indicate that our electronic nose system rapidly detects odors and effectively performs quality control. By establishing models for quality stability control, we successfully identified samples with acceptable quality and those with odors. To further validate the system's performance and extend its applications, we collected two types of cigarette packaging paper samples with odor data. Using data augmentation techniques, we expanded the dataset and achieved an accuracy rate of 0.9938 through classification and discrimination. This highlights the significant potential of our self-developed electronic nose system in recognizing cigarette packaging paper odors and odorous samples.

DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath

Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring.

Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection of Ciboria batschiana

In the construction of electronic nose devices, two groups of measurement setups could be distinguished when we take into account the design of electronic nose chambers. The simpler one consists of placing the sensors directly in the environment of the measured gas, which has an important advantage, in that the composition of the gas is not changed as the gas is not diluted. However, that has an important drawback in that it is difficult to clean sensors between measurement cycles. The second, more advanced construction, contains a pneumatic system transporting the gas inside a specially designed sensor chamber. A new design of an electronic nose gas sensor chamber is proposed, which consists of a sensor chamber with a sliding chamber shutter, equipped with a simple pneumatic system for cleaning the air. The proposal combines the advantages of both approaches to the sensor chamber designs. The sensors can be effectively cleared by the flow of clean air, while the measurements are performed in the open state when the sensors are directly exposed to the measured gas. Airflow simulations were performed to confirm the efficiency of clean air transport used for sensors' cleaning. The demonstrated electronic nose applies eight Figaro Co. MOS TGS series sensors, in which a transient response caused by a change of the exposition to measured gas, and change of heater voltage, was collected. The new electronic nose was tested as applied to the differentiation between the samples of Ciboria batschiana fungi, which is one of the most harmful pathogens of stored acorns. The samples with various coverage, thus various concentrations of the studied odor, were measured. The tested device demonstrated low noise and a good level of repetition of the measurements, with stable results during several hours of repetitive measurements during an experiment lasting five consecutive days. The obtained data allowed complete differentiation between healthy and infected samples.

Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine

Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.

A Review of Gas Measurement Set-Ups

Measurements of the properties of gas-sensitive materials are a subject of constant research, including continuous developments and improvements of measurement methods and, consequently, measurement set-ups. Preparation of the test set-up is a key aspect of research, and it has a significant impact on the tested sensor. This paper aims to review the current state of the art in the field of gas-sensing measurement and provide overall conclusions of how the different set-ups impact the obtained results.

High Density Resistive Array Readout System for Wearable Electronics

This work presents a wearable sensing system for high-density resistive array readout. The system comprising readout electronics for a high-density resistive sensor array and a rechargeable battery, was realized in a wristband. The analyzed data with the proposed system can be visualized using a custom graphical user interface (GUI) developed in a personal computer (PC) through a universal serial bus (USB) and using an Android app in smartphones via Bluetooth Low Energy (BLE), respectively. The readout electronics were implemented on a printed circuit board (PCB) and had a compact dimension of 3 cm × 3 cm. It was designed to measure the resistive sensor with a dynamic range of 1 KΩ–1 MΩ and detect a 0.1% change of the base resistance. The system operated at a 5 V supply voltage, and the overall system power consumption was 95 mW. The readout circuit employed a resistance-to-voltage (R-V) conversion topology using a 16-bit analog-to-digital converter (ADC), integrated in the Cypress Programmable System-on-Chip (PSoC^®) 5LP microcontroller. The device behaves as a universal-type sensing system that can be interfaced with a wide variety of resistive sensors, including chemiresistors, piezoresistors, and thermoelectric sensors, whose resistance variations fall in the target measurement range of 1 KΩ–1 MΩ. The system performance was tested with a 60-resistor array and showed a satisfactory accuracy, with a worst-case error rate up to 2.5%. The developed sensing system shows promising results for applications in the field of the Internet of things (IoT), point-of-care testing (PoCT), and low-cost wearable devices.

Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It to Fusarium oxysporum and Rhizoctonia solani

Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first electronic nose used six non-specific Figaro Inc. metal oxide gas sensors. The second one used ten sensors from only two models (TGS 2602 and TGS 2603) operating at different heater voltages. Sets of features describing the shapes of the measurement curves of the sensors' responses when exposed to the odours were extracted. Machine learning classification models using the logistic regression method were created. We demonstrated the possibility of applying the low-cost electronic nose data to differentiate between the two studied species of fungi with acceptable accuracy. Improved classification performance could be obtained, mainly for measurements using TGS 2603 sensors operating at different voltage conditions.

Application of Machine Learning for Fenceline Monitoring of Odor Classes and Concentrations at a Wastewater Treatment Plant

The development of low-cost sensors, the introduction of technical performance specifications, and increasingly effective machine learning algorithms for managing big data have led to a growing interest in the use of instrumental odor monitoring systems (IOMS) for odor measurements from industrial plants. The classification and quantification of odor concentration are the main goals of IOMS installed inside industrial plants in order to identify the most important odor sources and to assess whether the regulatory thresholds have been exceeded. This paper illustrates the use of two machine learning algorithms applied to the concurrent classification and quantification of odors. Random Forest was employed, which is a machine learning algorithm that thus far has not been used in the field of odor quantification and classification for complex industrial situations. Furthermore, the results were compared with commonly used algorithms in this field, such as artificial neural network (ANN), which was here employed in the form of a deep neural network. Both techniques were applied to the data collected from an IOMS installed for fenceline monitoring at a wastewater treatment plant. Cohen’s kappa and Normalized RMSE are used as specifical performance indicators for classification and regression: the indicators were calculated for the test dataset, and the results were compared with data in the literature obtained in contexts of similar complexity. A Cohen’s kappa of 97% was reached for the classification task, while the best Normalized RMSE, namely 4%, for the interval 20–2435 ouE/m³ was obtained with Random Forest.

Market Perspectives and Future Fields of Application of Odor Detection Biosensors within the Biological Transformation-A Systematic Analysis

The technological advantages that biosensors have over conventional technical sensors for odor detection and the role they play in the biological transformation have not yet been comprehensively analyzed. However, this is necessary for assessing their suitability for specific fields of application as well as their improvement and development goals. An overview of biological basics of olfactory systems is given and different odor sensor technologies are described and classified in this paper. Specific market potentials of biosensors for odor detection are identified by applying a tailored methodology that enables the derivation and systematic comparison of both the performance profiles of biosensors as well as the requirement profiles for various application fields. Therefore, the fulfillment of defined requirements is evaluated for biosensors by means of 16 selected technical criteria in order to determine a specific performance profile. Further, a selection of application fields, namely healthcare, food industry, agriculture, cosmetics, safety applications, environmental monitoring for odor detection sensors is derived to compare the importance of the criteria for each of the fields, leading to market-specific requirement profiles. The analysis reveals that the requirement criteria considered to be the most important ones across all application fields are high specificity, high selectivity, high repeat accuracy, high resolution, high accuracy, and high sensitivity. All these criteria, except for the repeat accuracy, can potentially be better met by biosensors than by technical sensors, according to the results obtained. Therefore, biosensor technology in general has a high application potential for all the areas of application under consideration. Health and safety applications especially are considered to have high potential for biosensors due to their correspondence between requirement and performance profiles. Special attention is paid to new areas of application that require multi-sensing capability. Application scenarios for multi-sensing biosensors are therefore derived. Moreover, the role of biosensors within the biological transformation is discussed.

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

Compared with traditional gas chromatography-mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors' response to the odors' exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models' performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the two mentioned species with acceptable accuracy by our low-cost electronic nose.

FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network

The integrated electronic nose (e-nose) design, which integrates sensor arrays and recognition algorithms, has been widely used in different fields. However, the current integrated e-nose system usually suffers from the problem of low accuracy with simple algorithm structure and slow speed with complex algorithm structure. In this article, we propose a method for implementing a deep neural network for odor identification in a small-scale Field-Programmable Gate Array (FPGA). First, a lightweight odor identification with depthwise separable convolutional neural network (OI-DSCNN) is proposed to reduce parameters and accelerate hardware implementation performance. Next, the OI-DSCNN is implemented in a Zynq-7020 SoC chip based on the quantization method, namely, the saturation-flooring KL divergence scheme (SF-KL). The OI-DSCNN was conducted on the Chinese herbal medicine dataset, and simulation experiments and hardware implementation validate its effectiveness. These findings shed light on quick and accurate odor identification in the FPGA.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.