Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor

Most gases are odorless, colorless and also hazard to be sensed by the human olfactory system. Hence, an electronic nose system is required for the gas classification process. This study presents the design of electronic nose system using a combination of Gas Chromatography Column and a Surface Acoustic Wave (SAW). The Gas Chromatography Column is a technique based on the compound partition at a certain temperature. Whereas, the SAW sensor works based on the resonant frequency change. In this study, gas samples including methanol, acetonitrile, and benzene are used for system performance measurement. Each gas sample generates a specific acoustic signal data in the form of a frequency change recorded by the SAW sensor. Then, the acoustic signal data is analyzed to obtain the acoustic features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support Vector Machine (SVM) method using the acoustic feature as its input parameters are applied to classify the gas sample. Radial Basis Function is used to build the optimal hyperplane model which devided into two processes i.e., the training process and the external validation process. According to the result performance, the training process has the accuracy of 98.7% and the external validation process has the accuracy of 93.3%. Our electronic nose system has the average sensitivity of 51.43 Hz/mL to sense the gas samples

Design and Implementation of QCM Virtual Sensing Schemes for Analyses of Volatile Organic Compounds

Sensor arrays have evolved as powerful approaches for providing detection and discrimination of volatile organic compounds (VOCs) as required across numerous analytical applications. Such systems typically comprise a number of cross reactive sensor elements, which generate analyte specific response patterns upon exposure to VOCs, and are known as multisensor arrays. When evaluated using statistical methods, these response patterns facilitate classification of VOCs. As an alternative, a single dynamically operated sensor could also be used to generate analyte specific response patterns. This approach is known as a virtual sensor array (VSA) and can exhibit significant advantages when compared to MSAs. Some advantages include lower power consumption, sensor drift, material cost, and experimental preparatory time. Furthermore, several dynamically operated sensors could be used in tandem (using the MSA and VSA scheme in a complementary fashion) to fabricate virtual multisensor arrays (V-MSAs). Such systems would exhibit greater data density than either the MSA or VSA, and are promising for samples that are particularly challenging to discriminate. Among the various systems utilized for VOC discrimination, sorption based systems hold considerable promise because they are simple and inexpensive yet highly effective. This dissertation is focused on the development of array sensing schemes using ionic liquids (ILs), a group of uniform materials based on organic salts (GUMBOS), and binary blends of either IL or GUMBOS with polymer as recognition elements and the quartz crystal microbalance (QCM) as the transducer. Towards this end, ILs, which are defined as organic salts with melting points below 100 °C, and group of uniform materials based on organic salts (GUMBOS) which extend the melting range of ILs to 250 °C to encompass similar solid phase salts, were used to design the first examples of QCM based VSAs, and V-MSAs, for pure VOC and complex mixture analyses. Furthermore binary blends of organic salts and polymer were used to fabricate the first VSA with the capability to identify and approximate molecular weight of pure VOCs. By and large, the studies presented here demonstrate the excellent potential of these materials and techniques for advancement of vapor phase measurement science

Intelligent Sensing Using Metal Oxide Semiconductor Based-on Support Vector Machine for Odor Classification

Classifying odor in real experiment presents some challenges, especially the uncertainty of the odor concentration and dispersion that can lead to a difficulty in obtaining an accurate datasets. In this study, to enhance the accuracy, datasets arrangement based on MOS sensors parameters using SVM approach for odor classification is proposed. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method to see the sensors' performance in real situation. These performances are considered as the base of choosing the position in collecting the datasets. The sensors in dynamic experiment have average of precision of 93.8-97.0%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the collected datasets can support the SVM in improving the intelligent sensing when conducting odor classification work

Integration of Biomolecular Recognition Elements with Solid-State Devices

Continued advances in stand-alone chemical sensors requires the introduction of new materials and transducers, and the seamless integration of the two. Electronic sensors represent one of the most efficient and versatile sensing transducers that offer advantages of high sensitivity, compatibility with multiple types of materials, network connectivity, and capability of miniaturization. With respect to materials to be used on this platform, many classes and subclasses of materials, including polymers, oxides, semiconductors, and composites have been investigated for various sensing environments. Despite numerous commercial products, major challenges remain. These include enhancing materials for selectivity/specificity, and low cost integration/ miniaturization of devices. Breakthroughs in either area would signify a transformative innovation. In this thesis, a combined materials and devices approach has been explored to address the above challenges. Biomolecular recognition elements, exemplified by aptamers, are the most recent addition to the library of tunable materials for specific detection of analytes. At the same time, nanoscale electrical devices based on tunnel junctions offer the potential for simple design, large scale integration, field deployment, network connectivity, and importantly, miniaturization to the molecular scale. To first establish a framework for studying sorption properties of solid oligonucleotides, custom designed aptamers sequences were studied to determine equilibrium partition coefficients. Linear-solvation-energy-relationship (LSER) analysis provides quantifications of non-covalent bonding properties and reveals the dominance of hydrogen bonding basicity in oligonucleotides. We find that DNA-analyte interactions have selective sorption properties similar to synthetic polymers. LSER analysis provides a chemical basis for material-analyte interactions. Oligonucleotide sequences were integrated with gold nanoparticle chemiresistors to transfer the selective sorption properties to microfabricated electrical devices. Responses generated by oligonucleotides under dry conditions were similar to standard organic mediums used as capping agents and suggests that DNA-based chemiresistor sensors operate with a similar mechanism based on sorption induced swelling. The equilibrium mass-sorption behavior of bulk DNA films could be translated to the chemiresistor sensitivity profiles. Our work establishes oligonucleotides, including aptamers, as a class of sorptive materials that can be systematically studied, engineered, and integrated with nanoscale electronic sensor devices. Experiments to investigate secondary structure effects were inconclusive and we conclude that further work should investigate DNA aptamers in buffered, aqueous environments to unequivocally establish the ability of chemiresitors to signal molecular recognition. Concurrent with the above studies, device integration and miniaturization was investigated to combine many sensing materials into a single, compact design. Arrays of nanoscale chemiresistors with critical features on the order of 10 – 100 nm were developed, using dielectrophoretic assembly of gold nanoparticles to control placement of the sensing material with nanometer accuracy. The nanoscale chemiresistors achieved the smallest known gold nanoparticle chemiresistors relying on just 2 – 3 layers of nanoparticles within 50 nm gaps, and were found to be more robust and less dependent on film thickness than previously published designs. Due to shorter diffusion paths, the sensors are also faster in response and recovery. A proof-of-concept, integrated single-chip sensor array was created and it showed similar response patterns as non-integrated sensor arrays. Dielectrophoresis is established as a key enabler for nanoscale, integrated devices. Based on the major findings of the thesis work, additional investigations were initiated to investigate the potential for nanoscale chemiresitor sensors to operate in buffered, aqueous (liquid) flow cells. Preliminary experiments show that chemiresistor sensing is transferable to liquid environments where analyte molecules are observed to partition from the bulk liquid to the sensing materials, leading to a detectable change of the device electrical properties. Comparing micron- and nano-scale devices fabricated using aqueous oligonucleotide-functionalized gold nanoparticles, it was found that nanoscale chemiresistors are more resistant to solvent damage than 5 µm chemiresistors. We conclude that future experiments to investigate aptamer sensing in aqueous solutions is a promising direction. Overall, this thesis is a significant contribution to materials development and device design to attain improved sensor selectivity and higher levels of device integration. First, it offers a scheme for design, selection, and validation of materials that confer analyte-specific interactions. Second, it paves the way for large scale sensor integration and parallel operation on a single chip. Lastly, it offers an approach to combine biomolecular recognition elements with electronic devices into robust, nanoscale detection systems. Based on the major findings of the thesis work, additional investigations were initiated to investigate the potential for nanoscale chemiresitor sensors to operate in buffered, aqueous (liquid) flow cells. Preliminary experiments show that chemiresistor sensing is transferable to liquid environments where analyte molecules are observed to partition from the bulk liquid to the sensing materials, leading to a detectable change of the device electrical properties. Comparing micron- and nano-scale devices fabricated using aqueous oligonucleotide-functionalized gold nanoparticles, it was found that nanoscale chemiresistors are more resistant to solvent damage than 5 µm chemiresistors. We conclude that future experiments to investigate aptamer sensing in aqueous solutions is a promising direction. Overall, this thesis is a significant contribution to materials development and device design to attain improved sensor selectivity and higher levels of device integration. First, it offers a scheme for design, selection, and validation of materials that confer analyte-specific interactions. Second, it paves the way for large scale sensor integration and parallel operation on a single chip. Lastly, it offers an approach to combine biomolecular recognition elements with electronic devices into robust, nanoscale detection systems

Advances in Electronic-Nose Technologies Developed for Biomedical Applications

The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry

QCM sensor arrays for monitoring volatile plant emanations via molecularly imprinted polymers

Zur Überwachung flüchtiger organischer Verbindungen (VOC volatile organic component) wurde ein Sensorarray bestehend aus mehrkanaligen Schwingquarzen (MQCM multichannel quartz crystal microbalance) entwickelt. Als selektive Schicht für den jeweiligen Analyten kamen molekular geprägte Polymerbeschichtungen zum Einsatz. Diese Sensor Arrays wurden vorerst zur kontinuierlichen online Überwachung und selektiven Quantifizierung von Terpenen eingesetzt, die von Arten der Familie Lamiaceae, wie beispielsweise Pfefferminze (Mentha x piperinta) und Basilikum (Ocimum Basilicum) freigesetzt werden. Dabei erzielten die Sensoren bei der Bestimmung des Frischegrades bemerkenswerte Reproduzierbarkeit der Emanationsmuster. Diese waren vergleichbar zu GC-MS Messungen, mit einem Detektionslimit unter 70 ppm. Zusätzlich können diese Muster mit Erkennungsmethoden wie zum Beispiel der Hauptkomponentenanalyse (PCA principal component analyses), der PLS (partial least squares) und mittels künstlichen neuronalen Netzwerken (ANN artifical neuronal networks) untersucht werden. Des Weiteren wurde eine elektronische Nase mit einer molekular geprägten, biomimetischen Polymerschicht (MIP molecular imprinted polymer) entwickelt, die das Terpenemissionsmuster von frischen und getrockneten Kräutern charakterisiert. Hierzu wurden Rosmarin (Rosmarinus Officinalis L.), Basilikum (Ocimum Basilicum) und Salbei (Salvia Officinalis) eingesetzt. Die dazu notwendigen Optimierungsparamater der elektronischen Nase sind: Schichtdicken, Sensitivität der Analyte <20 ppm, Selektivität bei einer Konzentration von 50 ppm der Terpene und Reproduzierbarkeit. Die reversiblen Sensorantworten sind in einem Konzentrationsbereich von <20 ppm bis 200 ppm linear. Die Isomere α- und β-Pinen sind signifikant unterscheidbar. Die Unterscheidbarkeit zwischen frischen und getrockneten Kräutern konnte durch die entsprechenden Messeffekte (20-1200 Hz) der elektronischen Nasen realisiert werden. Die erhaltenen Daten wurden zur Mustererkennung mittels PCA und ANN analysiert und durch Ergebnisse der GC-MS Messungen, welche einen ähnlichen Trend darstellen, validiert. Die Haltbarkeitsdauer der Kräuter konnte durch die Emanation der flüchtigen organischen Bestandteile über einen Zeitraum von mehreren Tagen bestimmt werden. Die Detektionslimits sind besser als 20 ppm und erlauben die Überwachung der Lagerung über mehrere Tage. Zusätzlich wurde eine MIP Screening Methode zur chemischen Bestimmung von Ethlyacetat entwickelt. Dazu wurden sechs MIPs mit unterschiedlichen Monomer¬zusammensetzungen aus VP, PS und DVB hergestellt und getestet. Als das am besten geeignete Sensormaterial für Ethylacetat erwies sich das Polymer mit einer Monomer¬zusam¬mensetzung von VP:PS:DVB 1:2:7 etabliert. Damit konnten Sensitivitäten und Selektivitäten über einen weiten Konzentrationsbereich von 25-3000 ppm für Ethylacetat erreicht werden. Die Querselektivität dieses MIP zwischen 250 und 750 ppm zu 1-Propanol erwies sich als sehr gering (≤ 1 Hz). Schlussendlich wurde ein Sensor Array mit vier Elektroden pro Substrat konstruiert. Dessen Herstellung wurde durch die Optimierung der Elektrodengröße, deren Geometrie und dem Kalibrieren der Heizwendel bestimmt. Diese neu entwickelte Strategie wurde zur massensensitiven real-time Bestimmung und Unterscheidung von Terpenen, welche von Thymian freigesetzt werden, eingesetzt. Die Muster der freigesetzten Terpene, welche mittels vier-Elektroden QCM Arrays erhalten wurden, sind mit den GC-MS Daten vergleichbar. Somit können derartige QCM Sensorarrays in der Praxis zur sensitiven und selektiven Bestimmung einer Vielzahl von biologischen Analyten im mikro- und makro-Bereich, wie beispielsweise die DNA Bestimmung, die Überwachung von VOCs von Pflanzen, bei der Kompostierung oder bei Abbauprozessen, die Qualitätskontrolle, die Haltbarkeit und Frische von Lebensmittel und in zahlreichen Industriebereichen, in unterschiedlichen Phasen zum Einsatz kommen.Arrays of chemical sensors derived from 10 MHz piezoelectric multichannel quartz crystal microbalance (MQCM) have been developed for selective monitoring of volatile organic compounds. Molecularly imprinted artificial recognition membranes have been used for imprinting the analytes of interest. At first the designed sensor array was used for continuous online surveillance and selective quantification of terpenes emanated from species of Lamiaceae family, i.e., peppermint (Mentha × piperita)and basil (Ocimum Basilicum). In terms of monitoring freshness, appreciable reproducibility in emanation patterns comparable to GC-MS analysis was attained with a limit of detection below 70 ppm. Hence, its competency to be explored jointly with pattern recognition tools, i.e., PCA, PLS and ANN. Furthermore, an e-nose with MIP coated biomimetic sensitive layers for comparative study of emanating terpenes of fresh and dried: rosemary (Rosmarinus Officinalis L.), basil (Ocimum Basilicum) and sage (Salvia Officinalis) was made. The optimal e-nose parameters: layer heights, sensitivity <20 ppm of analytes, selectivity at 50 ppm of terpenes, repeatability and reproducibility were systematically achieved. Linearity in reversible responses over a concentration range of ≤ 20-200 ppm has been observed. Isomers, α-pinene and β-pinene can be significantly differentiated by the sensor system. Sensitive and selective properties of e-nose for sensor effects (20–1,200 Hz) have been established which distinguish fresh herbs from dried. The sensor data was analyzed for pattern recognition via PCA and ANN and corroborated with GC-MS results which revealed a similar trend. Moreover, the limit of detection to ≤ 20 ppm and shelf-life of herbs to few days have been examined via designed e-nose. In addition, an ethyl acetate MIP screening strategy has been successfully developed for its chemical sensing. Six MIPs with different monomer compositions of VP, PS and DVB were prepared and tested. Polymer B with monomers ratio (VP: PS: DVB, 1:2:7) was observed as most favorable sensing material for ethyl acetate. Sensitivity and selectivity from a broad range of concentration 25-3000ppm of ethyl acetate was achieved. Cross selectivity of this MIP at 250-750 ppm against 1-propanol was observed to be quite low, i.e., ≤ 1Hz. Finally a tetra-electrode QCM sensor array has been designed. Its fabrication was done through optimizing electrodes size, geometry and with calibration of heating coil. This novel strategy was used for real-time differential mass sensing of terpenes emitted from thyme plant. Patterns of emanating terpenes observed from tetra-electrode QCM array and GC-MS were comparable. Such QCM sensors arrays in practice can explore sensitive and selective concerts for a variety of analytes in different phase’s ranging from bio- micro to macromolecules, e.g., in DNA sensing, monitoring VOCs of plants, composting and degradation process, estimating quality , shelf-life and freshness of food products and in various industries

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing

Quartz Crystal Microbalance Based Sensors for Detection and Discrimination of Volatile Organic Compounds Using Ionic Materials

Volatile organic compounds (VOCs) are prevalent in everyday life, ranging from household chemicals, naturally occurring scents from common plants and animals, to industrial-scale chemicals. Many of these VOCs are known to cause adverse health and environmental effects and require regulation to prevent pollution. Detecting VOCs plays a critical role in food quality control, environmental quality control, medical diagnostics, and explosives detection. Thus, development of adequate sensing devices for detection and discrimination of VOCs is of great importance. In recent years, use of quartz crystal microbalance (QCM) based sensor arrays for analyses of VOCs has attracted significant interest. Detection of VOCs using QCM-based sensors is dependent upon coating materials; hence, development of suitable coating materials is also of great importance. Over the years, QCM-based sensors have provided great promise for detecting VOCs; however, they have not provided this same potential for discrimination between different VOCs. Thus, this dissertation is focused on development of reusable QCM-based sensor arrays for detection and discrimination of VOCs using ionic liquids (ILs) and a group of uniform materials based on organic salts (GUMBOS) as coating materials. GUMBOS and ILs are similar classes of ionic materials, where GUMBOS represent solid phase organic salts with melting points between 25°C and 250°C, while ILs are organic salts with melting points below 100°C and are typically liquid at room temperature. Within this dissertation the synthesis and characterization of novel ILs and GUMBOS are discussed. Moreover, composite materials using IL-polymer blends are also presented. Vapor sensing properties of all ILs, GUMBOS, and composites were evaluated for use as coating materials in sensor arrays for detection and discrimination towards a wide range of VOCs. Two different sensor array schemes, multisensor array (MSA) and virtual sensor array (VSA), are described and examined throughout this dissertation. Finally, statistical techniques, such as principal component analysis (PCA) and discriminant analysis (DA), were used to develop predictive models to quantify the accuracy of MSAs and VSAs. The first reports of a QCM-based MSA to discriminate VOCs by classes, and a QCM-based VSA for discrimination of closely related chlorinated VOCs are presented within this dissertation. Overall, these studies demonstrate capabilities of QCM-based vapor sensor arrays with ionic coating materials for accurate discrimination and detection of VOCs

Electronic sensor technologies in monitoring quality of tea: A review

Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea