A compact and low cost electronic nose for aroma detection

Portable and miniature sensors in supply chain for food authentication: a review

Food fraud, a pervasive issue in the global food industry, poses significant challenges to consumer health, trust, and economic stability, costing an estimated $10-15 billion annually. Therefore, there is a rising demand for developing portable and miniature sensors that facilitate food authentication throughout the supply chain. This review explores the recent advancements and applications of portable and miniature sensors, including portable/miniature near-infrared (NIR) spectroscopy, e-nose and colorimetric sensors based on nanozyme for food authentication within the supply chain. After briefly presenting the architecture and mechanism, this review discusses the application of these portable and miniature sensors in food authentication, addressing the challenges and opportunities in integrating and deploying these sensors to ensure authenticity. This review reveals the enhanced utility of portable/miniature NIR spectroscopy, e-nose, and nanozyme-based colorimetric sensors in ensuring food authenticity and enabling informed decision-making throughout the food supply chain.

A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds

BACKGROUND

The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection.

METHODS

This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted.

RESULTS

The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS.

CONCLUSION

Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

Paper-Based Multiplex Sensors for the Optical Detection of Plant Stress

The rising population and the ongoing climate crisis call for improved means to monitor and optimise agriculture. A promising approach to tackle current challenges in food production is the early diagnosis of plant diseases through non-invasive methods, such as the detection of volatiles. However, current devices for detection of multiple volatiles are based on electronic noses, which are expensive, require complex circuit assembly, may involve metal oxides with heating elements, and cannot easily be adapted for some applications that require miniaturisation or limit front-end use of electronic components. To address these challenges, a low-cost optoelectronic nose using chemo-responsive colorimetric dyes drop-casted onto filter paper has been developed in the current work. The final sensors could be used for the quantitative detection of up to six plant volatiles through changes in colour intensities with a sub-ppm level limit of detection, one of the lowest limits of detection reported so far using colorimetric gas sensors. Sensor colouration could be analysed using a low-cost spectrometer and the results could be processed using a microcontroller. The measured volatiles could be used for the early detection of plant abiotic stress as early as two days after exposure to two different stresses: high salinity and starvation. This approach allowed a lowering of costs to GBP 1 per diagnostic sensing paper. Furthermore, the small size of the paper sensors allows for their use in confined settings, such as Petri dishes. This detection of abiotic stress could be easily achieved by exposing the devices to living plants for 1 h. This technology has the potential to be used for monitoring of plant development in field applications, early recognition of stress, implementation of preventative measures, and mitigation of harvest losses.

RGB-Detector: A Smart, Low-Cost Device for Reading RGB Indexes of Microfluidic Paper-Based Analytical Devices

A user-friendly, low-cost detector able to read the RGB indexes of microfluidic paper-based analytical devices (µPADs) was developed. The RGB-detector was built with 3D printing using PLA+ and reused Li-ion batteries. It is Arduino-based, which provides an easy interface between the sensor TCS3200, which reads the quadratic wave of the times corresponding to the RGB numbers, the Arduino itself, whose software translates the times into RGB values, and the touchscreen display, NX3224T028, which shows the results. This detector permits multi-sample analysis since it has a sample holder that can keep up to six µPADs simultaneously and rotate after the display's request. This work shows how the readings of the RGB indexes by the proposed RGB-detector implement the measurements' reproducibility. As a proof-of-concept, the RGB-detector application to a green array of µPADs for pH measurement coupled with chemometric analysis allowed us to achieve good results in terms of precision and agreement with the pH values measured by a classical pH-meter.

Drone-Mountable Gas Sensing Platform Using Graphene Chemiresistors for Remote In-Field Monitoring

We present the design, fabrication, and testing of a drone-mountable gas sensing platform for environmental monitoring applications. An array of graphene-based field-effect transistors in combination with commercial humidity and temperature sensors are used to relay information by wireless communication about the presence of airborne chemicals. We show that the design, based on an ESP32 microcontroller combined with a 32-bit analog-to-digital converter, can be used to achieve an electronic response similar, within a factor of two, to state-of-the-art laboratory monitoring equipment. The sensing platform is then mounted on a drone to conduct field tests, on the ground and in flight. During these tests, we demonstrate a one order of magnitude reduction in environmental noise by reducing contributions from humidity and temperature fluctuations, which are monitored in real-time with a commercial sensor integrated to the sensing platform. The sensing device is controlled by a mobile application and uses LoRaWAN, a low-power, wide-area networking protocol, for real-time data transmission to the cloud, compatible with Internet of Things (IoT) applications.

Enhanced Propanol Response Behavior of ZnFe2O4 NP-Based Active Sensing Layer Induced by Film Thickness Optimization

Development of gas sensors displaying improved sensing characteristics including sensitivity, selectivity, and stability is now possible owing to tunable surface chemistry of the sensitive layers as well as favorable transport properties. Herein, zinc ferrite (ZnFe2O4) nanoparticles (NPs) were produced using a microwave-assisted hydrothermal method. ZnFe2O4 NP sensing layer films with different thicknesses deposited on interdigitated alumina substrates were fabricated at volumes of 1.0, 1.5, 2.0, and 2.5 µL using a simple and inexpensive drop-casting technique. Successful deposition of ZnFe2O4 NP-based active sensing layer films onto alumina substrates was confirmed by X-ray diffraction and atomic force microscope analysis. Top view and cross-section observations from the scanning electron microscope revealed inter-agglomerate pores within the sensing layers. The ZnFe2O4 NP sensing layer produced at a volume of 2 μL exhibited a high response of 33 towards 40 ppm of propanol, as well as rapid response and recovery times of 11 and 59 s, respectively, at an operating temperature of 120 °C. Furthermore, all sensors demonstrated a good response towards propanol and the highest response against ethanol, methanol, carbon dioxide, carbon monoxide, and methane. The results indicate that the developed fabrication strategy is an inexpensive way to enhance sensing response without sacrificing other sensing characteristics. The produced ZnFe2O4 NP-based active sensing layers can be used for the detection of volatile organic compounds in alcoholic beverages for quality check in the food sector.

Application of E-nose technique to predict sugarcane syrup quality based on purity and refined sugar percentage

Rapid test methods with portable devices along with standard chemical tests are necessary to determine raw syrup quality in the sugarcane agro-industries. On this account, a special e-nose device was developed to test the sugarcane syrup and its association with the odor emitted from it to determine the amount of sucrose (purity) in the sugarcane syrup. Samples were obtained from the farms of Hakim-Farabi agro-industry, including four varieties (CP57, CP69, IRC99-02, and CP48). Experiments included chemical tests to determine the percentage of purity (PTY) and refined sugar (RS) plus an electronic nose test. Partial least squares (PLS), principle component regression (PCR), multiple linear regression (MLR), and artificial neural network (ANN) methods were used to evaluate the correlation between the gained signals from the sensor array and chemical analysis results of the samples. In the case of PTY, among 8 sensors, MQ3, MQ5, and MQ9 had the highest response compared to the others, while regarding RS, all the sensors except for MQ8 indicated a great contribution. Also, all models for PTY and RS showed a good prediction performance. The results revealed that ANN model, with topology 8-1-2, outperformed others for prediction of the quality indices of sugarcane, with high correlation coefficients (R² = 0.96 for RS; 0.99 for PTY), and relatively low RMSE values of 0.33 for RS; 0.4 for RTY. Finally, findings indicated that e-nose technique has the potential to become an authentic tool to assess chemical features of sugarcane syrup from e-nose system signals.

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.

Design and Validation of a Portable Machine Learning-Based Electronic Nose

Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography-mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.

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.

Detection of Mackerel Fish Spoilage with a Gas Sensor Based on One Single SnO2 Nanowire

A chemosensor consisting of one single tin oxide nanowire is used to determine the freshness status of mackerel fish (Scomber scombrus) in a quick and non-invasive way. The tiny chemoresistive sensor is first tested with pure ammonia and then used to measure the total volatile basic nitrogen from different samples of fish at different degrees of freshness. The sensor has proved capable of determining the freshness of a sample in few seconds compared to traditional methods such as microbial count and chromatography, which take hours. The sensor response is well correlated with the total viable count (TVC), proving that the total volatile basic nitrogen is a good way to quickly test the bacterial population in the sample. After calibrating the sensor (following the degradation of the fish during almost two days), it has been tested with random double blind samples, proving that it can well discriminate the degree of freshness of the fish preserved at different temperatures.

Detection of Lethal Bronzing Disease in Cabbage Palms (Sabal palmetto) Using a Low-Cost Electronic Nose

Lethal Bronzing Disease (LB) is a disease of palms caused by the 16SrIV-D phytoplasma. A low-cost electronic nose (eNose) prototype was trialed for its detection. It includes an array of eight Taguchi-type (MQ) sensors (MQ135, MQ2, MQ3, MQ4, MQ5, MQ9, MQ7, and MQ8) controlled by an Arduino NANO^® microcontroller, using heater voltages that vary sinusoidally over a 2.5 min cycle. Samples of uninfected, early symptomatic, moderate symptomatic, and late symptomatic infected palm leaves of the cabbage palm were processed and analyzed. MQ sensor responses were subjected to a 256 element discrete Fourier transform (DFT), and harmonic component amplitudes were reviewed by principal component analysis (PCA). The experiment was repeated three times, each showing clear evidence of differences in sensor responses between the samples of uninfected leaves and those in the early stages of infection. Within each experiment, four groups of responses were identified, demonstrating the ability of the unit to repeatedly distinguish healthy leaves from diseased ones; however, detection of the severity of infection has not been demonstrated. By selecting appropriate coefficients (here demonstrated with plots of MQ5 Cos1 vs. MQ8 Sin3), it should be possible to build a ruleset classifier to identify healthy and unhealthy samples.

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.

Elevating Chemistry Research with a Modern Electronics Toolkit

With the rapid development of high technology, chemical science is not as it used to be a century ago. Many chemists acquire and utilize skills that are well beyond the traditional definition of chemistry. The digital age has transformed chemistry laboratories. One aspect of this transformation is the progressing implementation of electronics and computer science in chemistry research. In the past decade, numerous chemistry-oriented studies have benefited from the implementation of electronic modules, including microcontroller boards (MCBs), single-board computers (SBCs), professional grade control and data acquisition systems, as well as field-programmable gate arrays (FPGAs). In particular, MCBs and SBCs provide good value for money. The application areas for electronic modules in chemistry research include construction of simple detection systems based on spectrophotometry and spectrofluorometry principles, customizing laboratory devices for automation of common laboratory practices, control of reaction systems (batch- and flow-based), extraction systems, chromatographic and electrophoretic systems, microfluidic systems (classical and nonclassical), custom-built polymerase chain reaction devices, gas-phase analyte detection systems, chemical robots and drones, construction of FPGA-based imaging systems, and the Internet-of-Chemical-Things. The technology is easy to handle, and many chemists have managed to train themselves in its implementation. The only major obstacle in its implementation is probably one's imagination.

A Review on the Use of Impedimetric Sensors for the Inspection of Food Quality

This paper exhibits a thorough review of the use of impedimetric sensors for the analysis of food quality. It helps to understand the contribution of some of the major types of impedimetric sensors that are used for this application. The deployment of impedimetric sensing prototypes has been advantageous due to their wide linear range of responses, detection of the target analyte at low concentrations, good stability, high accuracy and high reproducibility in the results. The choice of these sensors was classified on the basis of structure and the conductive material used to develop them. The first category included the use of nanomaterials such as graphene and metallic nanowires used to form the sensing devices. Different forms of graphene nanoparticles, such as nano-hybrids, nanosheets, and nano-powders, have been largely used to sense biomolecules in the micro-molar range. The use of conductive materials such as gold, copper, tungsten and tin to develop nanowire-based prototypes for the inspection of food quality has also been shown. The second category was based on conventional electromechanical circuits such as electronic noses and other smart systems. Within this sector, the standardized systems, such as electronic noses, and LC circuit -based systems have been explained. Finally, some of the challenges posed by the existing sensors have been listed out, along with an estimate of the increase in the number of sensors employed to assess food quality.

The Emergence of Insect Odorant Receptor-Based Biosensors

The olfactory receptor neurons of insects and vertebrates are gated by odorant receptor (OR) proteins of which several members have been shown to exhibit remarkable sensitivity and selectivity towards volatile organic compounds of significant importance in the fields of medicine, agriculture and public health. Insect ORs offer intrinsic amplification where a single binding event is transduced into a measurable ionic current. Consequently, insect ORs have great potential as biorecognition elements in many sensor configurations. However, integrating these sensing components onto electronic transducers for the development of biosensors has been marginal due to several drawbacks, including their lipophilic nature, signal transduction mechanism and the limited number of known cognate receptor-ligand pairs. We review the current state of research in this emerging field and highlight the use of a group of indole-sensitive ORs (indolORs) from unexpected sources for the development of biosensors.

Technology innovation: advancing capacities for the early detection of and rapid response to invasive species

The 2016–2018National Invasive Species Council (NISC) Management Plan and Executive Order 13751 call for US federal agencies to foster technology development and application to address invasive species and their impacts. This paper complements and draws on an Innovation Summit, review of advanced biotechnologies applicable to invasive species management, and a survey of federal agencies that respond to these high-level directives. We provide an assessment of federal government capacities for the early detection of and rapid response to invasive species (EDRR) through advances in technology application; examples of emerging technologies for the detection, identification, reporting, and response to invasive species; and guidance for fostering further advancements in applicable technologies. Throughout the paper, we provide examples of how federal agencies are applying technologies to improve programmatic effectiveness and cost-efficiencies. We also highlight the outstanding technology-related needs identified by federal agencies to overcome barriers to enacting EDRR. Examples include improvements in research facility infrastructure, data mobilization across a wide range of invasive species parameters (from genetic to landscape scales), promotion of and support for filling key gaps in technological capacity (e.g., portable, field-ready devices with automated capacities), and greater investments in technology prizes and challenge competitions.

Comparison of Sensory and Electronic Tongue Analysis Combined with HS-SPME-GC-MS in the Evaluation of Skim Milk Processed with Different Preheating Treatments

It is well known that the flavor of skim milk is inferior to whole milk due to the lack of fat. With the popularity of low-fat dairy products, improving the flavor of skim milk is a main focus for food scientists. During the production of skim milk, preheating treatments have a significant effect for the flavor of skim milk. In this study, to explore the optimal processing conditions, milk was preheated at 30 °C, 40 °C, 50 °C, 60 °C for 30 min prior to defatting. When the optimal temperature was determined, milk was then preheated at the optimal temperature for 10 min, 20 min, 30 min, 40 min and 50 min, respectively, to obtain the best preheating time. Distinctions between skim milk samples with different processing conditions were studied by sensory evaluation, e-tongue and HS-SPME-GC-MS analysis. Principle components analysis (PCA) and cluster analysis (CA) were selected to associate with e-tongue results and compare the similarities and differences among the skim milks. Sensory and e-tongue results matched and both showed that a preheating temperature of 50 °C and 30 min time might be the optimal combination of processing conditions. Thirteen volatiles, including ketones, acids, aldehydes, alcohols, alkanes and sulfur compounds, were analyzed to evaluate flavor of the skim milks produced by different preheating treatments. Combined with previous studies, the results indicated that most volatile compounds were decreased by reducing the fat concentration and the typical compound 2-heptanone was not detected in our skim milk samples.

Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor

A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for each prediction. Meanwhile, two main indicators of conformal predictor, validity and efficiency, were also compared and discussed in this work. The result shows that the approach integrating electronic nose with aggregated conformal prediction to classify the species of dendrobium with reliability and validity is promising.

A Set of Platforms with Combinatorial and High-Throughput Technique for Gas Sensing, from Material to Device and to System

In a new E-nose development, the sensor array needs to be optimized to have enough sensitivity and selectivity for gas/odor classification in the application. The development process includes the preparation of gas sensitive materials, gas sensor fabrication, array optimization, sensor array package and E-nose system integration, which would take a long time to complete. A set of platforms including a gas sensing film parallel synthesis platform, high-throughput gas sensing unmanned testing platform and a handheld wireless E-nose system were presented in this paper to improve the efficiency of a new E-nose development. Inkjet printing was used to parallel synthesize sensor libraries (400 sensors can be prepared each time). For gas sensor selection and array optimization, a high-throughput unmanned testing platform was designed and fabricated for gas sensing measurements of more than 1000 materials synchronously. The structures of a handheld wireless E-nose system with low power were presented in detail. Using the proposed hardware platforms, a new E-nose development might only take one week.

Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

Identification of Tobacco Types and Cigarette Brands Using an Electronic Nose Based on Conductive Polymer/Porphyrin Composite Sensors

Three tobacco types (Burley, Flue Cured, and Oriental) and eight cigarette brands were unequivocally identified using an electronic nose formed by only three sensors based on a single novel conducting polymer (PF-BTB) doped with different porphyrins (H₂TPP, H₂TPFP, and H₂BTBOP). The synthesis and characterization of the polymer are also discussed. Small changes in the porphyrin structure caused significant changes in the electrical conductance response patterns of the sensors upon exposure to complex chemical matrixes, representing a novel approach for tuning the selectivity of chemiresistive sensors for e-nose application. This e-nose is fast, cheap, reliable, can be easily operated, and could be a valuable tool for border agents fighting cigarette smuggling around the world, helping them prevent losses of millions in tax revenues and sales.

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent.

Research on a Visual Electronic Nose System Based on Spatial Heterodyne Spectrometer

Light absorption gas sensing technology has the characteristics of massive parallelism, cross-sensitivity and extensive responsiveness, which make it suitable for the sensing task of an electronic nose (e-nose). With the performance of hyperspectral resolution, spatial heterodyne spectrometer (SHS) can present absorption spectra of the gas in the form of a two dimensional (2D) interferogram which facilitates the analysis of gases with mature image processing techniques. Therefore, a visual e-nose system based on SHS was proposed. Firstly, a theoretical model of the visual e-nose system was constructed and its visual maps were obtained by an experiment. Then the local binary pattern (LBP) and Gray-Level Co-occurrence Matrix (GLCM) were used for feature extraction. Finally, classification algorithms based on distance similarity (Correlation coefficient (CC); Euclidean distance to centroids (EDC)) were chosen to carry on pattern recognition analysis to verify the feasibility of the visual e-nose system.

A critical review on the applications of artificial neural networks in winemaking technology

Since their development in 1943, artificial neural networks were extended into applications in many fields. Last twenty years have brought their introduction into winery, where they were applied following four basic purposes: authenticity assurance systems, electronic sensory devices, production optimization methods, and artificial vision in image treatment tools, with successful and promising results. This work reviews the most significant approaches for neural networks in winemaking technologies with the aim of producing a clear and useful review document.

Portable Electronic Nose Based on Electrochemical Sensors for Food Quality Assessment

The steady increase in global consumption puts a strain on agriculture and might lead to a decrease in food quality. Currently used techniques of food analysis are often labour-intensive and time-consuming and require extensive sample preparation. For that reason, there is a demand for novel methods that could be used for rapid food quality assessment. A technique based on the use of an array of chemical sensors for holistic analysis of the sample’s headspace is called electronic olfaction. In this article, a prototype of a portable, modular electronic nose intended for food analysis is described. Using the SVM method, it was possible to classify samples of poultry meat based on shelf-life with 100% accuracy, and also samples of rapeseed oil based on the degree of thermal degradation with 100% accuracy. The prototype was also used to detect adulterations of extra virgin olive oil with rapeseed oil with 82% overall accuracy. Due to the modular design, the prototype offers the advantages of solutions targeted for analysis of specific food products, at the same time retaining the flexibility of application. Furthermore, its portability allows the device to be used at different stages of the production and distribution process.

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

Fabrication and Characterization of a Micro Methanol Sensor Using the CMOS-MEMS Technique

A methanol microsensor integrated with a micro heater manufactured using the complementary metal oxide semiconductor (CMOS)-microelectromechanical system (MEMS) technique was presented. The sensor has a capability of detecting low concentration methanol gas. Structure of the sensor is composed of interdigitated electrodes, a sensitive film and a heater. The heater located under the interdigitated electrodes is utilized to provide a working temperature to the sensitive film. The sensitive film prepared by the sol-gel method is tin dioxide doped cadmium sulfide, which is deposited on the interdigitated electrodes. To obtain the suspended structure and deposit the sensitive film, the sensor needs a post-CMOS process to etch the sacrificial silicon dioxide layer and silicon substrate. The methanol senor is a resistive type. A readout circuit converts the resistance variation of the sensor into the output voltage. The experimental results show that the methanol sensor has a sensitivity of 0.18 V/ppm.

Universal electronics for miniature and automated chemical assays

This minireview discusses universal electronic modules (generic programmable units) and their use by analytical chemists to construct inexpensive, miniature or automated devices. Recently, open-source platforms have gained considerable popularity among tech-savvy chemists because their implementation often does not require expert knowledge and investment of funds. Thus, chemistry students and researchers can easily start implementing them after a few hours of reading tutorials and trial-and-error. Single-board microcontrollers and micro-computers such as Arduino, Teensy, Raspberry Pi or BeagleBone enable collecting experimental data with high precision as well as efficient control of electric potentials and actuation of mechanical systems. They are readily programmed using high-level languages, such as C, C++, JavaScript or Python. They can also be coupled with mobile consumer electronics, including smartphones as well as teleinformatic networks. More demanding analytical tasks require fast signal processing. Field-programmable gate arrays enable efficient and inexpensive prototyping of high-performance analytical platforms, thus becoming increasingly popular among analytical chemists. This minireview discusses the advantages and drawbacks of universal electronic modules, considering their application in prototyping and manufacture of intelligent analytical instrumentation.

Microcontroller-assisted compensation of adenosine triphosphate levels: instrument and method development

In order to ascertain optimum conditions for biocatalytic processes carried out in vitro, we have designed a bio-opto-electronic system which ensures real-time compensation for depletion of adenosine triphosphate (ATP) in reactions involving transfer of phosphate groups. The system covers ATP concentration range of 2-48 μM. The report demonstrates feasibility of the device operation using apyrase as the ATP-depleting enzyme.

Monitoring and evaluation of alcoholic fermentation processes using a chemocapacitor sensor array

The alcoholic fermentation of Savatiano must variety was initiated under laboratory conditions and monitored daily with a gas sensor array without any pre-treatment steps. The sensor array consisted of eight interdigitated chemocapacitors (IDCs) coated with specific polymers. Two batches of fermented must were tested and also subjected daily to standard chemical analysis. The chemical composition of the two fermenting musts differed from day one of laboratory monitoring (due to different storage conditions of the musts) and due to a deliberate increase of the acetic acid content of one of the musts, during the course of the process, in an effort to spoil the fermenting medium. Sensor array responses to the headspace of the fermenting medium were compared with those obtained either for pure or contaminated samples with controlled concentrations of standard ethanol solutions of impurities. Results of data processing with Principal Component Analysis (PCA), demonstrate that this sensing system could discriminate between a normal and a potential spoiled grape must fermentation process, so this gas sensing system could be potentially applied during wine production as an auxiliary qualitative control instrument.

A low-cost real color picker based on Arduino

Color measurements have traditionally been linked to expensive and difficult to handle equipment. The set of mathematical transformations that are needed to transfer a color that we observe in any object that doesn't emit its own light (which is usually called a color-object) so that it can be displayed on a computer screen or printed on paper is not at all trivial. This usually requires a thorough knowledge of color spaces, colorimetric transformations and color management systems. The TCS3414CS color sensor (I2C Sensor Color Grove), a system for capturing, processing and color management that allows the colors of any non-self-luminous object using a low-cost hardware based on Arduino, is presented in this paper. Specific software has been developed in Matlab and a study of the linearity of chromatic channels and accuracy of color measurements for this device has been undertaken. All used scripts (Arduino and Matlab) are attached as supplementary material. The results show acceptable accuracy values that, although obviously do not reach the levels obtained with the other scientific instruments, for the price difference they present a good low cost option.

Improving short term instability for quantitative analyses with portable electronic noses

One of the main problems when working with electronic noses is the lack of reproducibility or repeatability of the sensor response, so that, if this problem is not properly considered, electronic noses can be useless, especially for quantitative analyses. On the other hand, irreproducibility is increased with portable and low cost electronic noses where laboratory equipment like gas zero generators cannot be used. In this work, we study the reproducibility of two portable electronic noses, the PEN3 (commercial) and CAPINose (a proprietary design) by using synthetic wine samples. We show that in both cases short term instability associated to the sensors' response to the same sample and under the same conditions represents a major problem and we propose an internal normalization technique that, in both cases, reduces the variability of the sensors' response. Finally, we show that the normalization proposed seems to be more effective in the CAPINose case, reducing, for example, the variability associated to the TGS2602 sensor from 12.19% to 2.2%.

A discriminant distance based composite vector selection method for odor classification

We present a composite vector selection method for an effective electronic nose system that performs well even in noisy environments. Each composite vector generated from a electronic nose data sample is evaluated by computing the discriminant distance. By quantitatively measuring the amount of discriminative information in each composite vector, composite vectors containing informative variables can be distinguished and the final composite features for odor classification are extracted using the selected composite vectors. Using the only informative composite vectors can be also helpful to extract better composite features instead of using all the generated composite vectors. Experimental results with different volatile organic compound data show that the proposed system has good classification performance even in a noisy environment compared to other methods.