The document summarizes a presentation on electronic noses (e-noses). It discusses how e-noses mimic the human olfactory system and are used to identify odors. E-noses contain an array of chemical sensors that change resistance when exposed to vapors and produce unique response patterns. The presentation covers the main components of e-noses including sample handling, sensing, data acquisition, and pattern recognition systems. Various sensor types like metal oxide, conducting polymer, optical, and piezoelectric sensors are also described. Examples of commercial e-nose applications in areas like agriculture, food, and manufacturing are provided.
The document describes the components and functioning of an electronic nose. It discusses how electronic noses mimic the human olfactory system using an array of chemical sensors and pattern recognition. The main components are a sample delivery system to introduce vapors to sensors, a detection system consisting of various sensor types, and a computing system to analyze the response patterns. Applications mentioned include environmental monitoring, medical diagnosis, crime prevention, and food quality control.
This presentation provides an overview of electronic noses (e-noses). It discusses how e-noses can detect volatile organic compounds to identify odors, similar to biological noses but with advantages like not tiring, getting sick, or being distracted. The main components of an e-nose include a sample delivery system to introduce odors, a sensor array to transduce chemical interactions into electrical signals, and a computing system to analyze the sensor responses. Common sensor technologies are metal oxide sensors, conducting polymers, quartz crystal microbalances, and MOSFETs. E-noses find applications in medical diagnosis, environmental monitoring, the food industry, and detecting explosives. Future areas of research include miniaturizing e-nose technology and
This document presents information on electronic noses. It discusses that electronic noses are devices that detect odors and flavors using sensor arrays and pattern recognition systems, similarly to how humans sense smells. The three main parts of an electronic nose are a sample delivery system to introduce headspaces, a sensor detection system of sensors like MOS that react to volatile compounds, and a computing system to analyze the sensor responses. Electronic noses are used in various applications like research and development, quality control, and production to analyze samples and odors.
The document discusses electronic noses (e-noses), which are devices that mimic the human sense of smell. It describes the major parts of an e-nose including sample delivery, detection, and computing systems. The document outlines the development of e-noses from first to third generations and provides an example device. Finally, it discusses current and potential future applications of e-noses in areas like medical diagnosis, environmental monitoring, food quality control, and crime prevention.
The document discusses electronic noses (e-noses), which are devices that mimic the human sense of smell. An e-nose consists of sensors that detect volatile organic compounds in odors and a computing system that analyzes the sensor responses. E-noses have various applications in areas like environmental monitoring, quality control, and medical diagnosis. While they have advantages over human smell in speed, accuracy, and ability to detect more substances, challenges remain in fully replicating the human olfactory system. Future improvements to sensors and data analysis may lead to more advanced e-nose technologies.
An electronic nose, or e-nose, is an instrument that mimics the human olfactory system and is designed to identify and classify chemical vapors. It consists of three main parts: a sample delivery system, a detection system with an array of different gas sensors, and a computing system. When a vapor sample is introduced, the sensors produce an electrical response that is sent to the computing system for analysis. E-noses can be used in applications like medical diagnosis, environmental monitoring, food quality control, and more. They provide a cheap and fast alternative to traditional analytical techniques.
This document summarizes a presentation on electronic noses. It begins with an introduction comparing biological noses to electronic noses. The need for electronic noses is that they are faster, more reliable, and can detect hazardous gases that humans cannot. The basic design of an electronic nose involves pulling a gas sample through a sensor array to produce response signals. Common sensor technologies are described along with the sample delivery, detection, and computing systems of electronic noses. Applications discussed include medical diagnosis, environmental monitoring, food quality control, and explosive detection. In conclusion, while electronic noses are cheaper and faster than biological noses, they still cannot match the sensitivity and selectivity of mammalian noses.
This document provides an overview of electronic noses. It defines an electronic nose as a device that detects odors or flavors using an array of sensors that generate electrical signals in response to volatile compounds. The document then discusses the basic design of an electronic nose and how it works, comparing it to the biological nose. It also outlines common sensor technologies, applications such as medical diagnosis and food quality control, and concludes that while not as sensitive as the mammalian nose, the electronic nose is a useful analytical tool.
The document describes the components and functioning of an electronic nose. It discusses how electronic noses mimic the human olfactory system using an array of chemical sensors and pattern recognition. The main components are a sample delivery system to introduce vapors to sensors, a detection system consisting of various sensor types, and a computing system to analyze the response patterns. Applications mentioned include environmental monitoring, medical diagnosis, crime prevention, and food quality control.
This presentation provides an overview of electronic noses (e-noses). It discusses how e-noses can detect volatile organic compounds to identify odors, similar to biological noses but with advantages like not tiring, getting sick, or being distracted. The main components of an e-nose include a sample delivery system to introduce odors, a sensor array to transduce chemical interactions into electrical signals, and a computing system to analyze the sensor responses. Common sensor technologies are metal oxide sensors, conducting polymers, quartz crystal microbalances, and MOSFETs. E-noses find applications in medical diagnosis, environmental monitoring, the food industry, and detecting explosives. Future areas of research include miniaturizing e-nose technology and
This document presents information on electronic noses. It discusses that electronic noses are devices that detect odors and flavors using sensor arrays and pattern recognition systems, similarly to how humans sense smells. The three main parts of an electronic nose are a sample delivery system to introduce headspaces, a sensor detection system of sensors like MOS that react to volatile compounds, and a computing system to analyze the sensor responses. Electronic noses are used in various applications like research and development, quality control, and production to analyze samples and odors.
The document discusses electronic noses (e-noses), which are devices that mimic the human sense of smell. It describes the major parts of an e-nose including sample delivery, detection, and computing systems. The document outlines the development of e-noses from first to third generations and provides an example device. Finally, it discusses current and potential future applications of e-noses in areas like medical diagnosis, environmental monitoring, food quality control, and crime prevention.
The document discusses electronic noses (e-noses), which are devices that mimic the human sense of smell. An e-nose consists of sensors that detect volatile organic compounds in odors and a computing system that analyzes the sensor responses. E-noses have various applications in areas like environmental monitoring, quality control, and medical diagnosis. While they have advantages over human smell in speed, accuracy, and ability to detect more substances, challenges remain in fully replicating the human olfactory system. Future improvements to sensors and data analysis may lead to more advanced e-nose technologies.
An electronic nose, or e-nose, is an instrument that mimics the human olfactory system and is designed to identify and classify chemical vapors. It consists of three main parts: a sample delivery system, a detection system with an array of different gas sensors, and a computing system. When a vapor sample is introduced, the sensors produce an electrical response that is sent to the computing system for analysis. E-noses can be used in applications like medical diagnosis, environmental monitoring, food quality control, and more. They provide a cheap and fast alternative to traditional analytical techniques.
This document summarizes a presentation on electronic noses. It begins with an introduction comparing biological noses to electronic noses. The need for electronic noses is that they are faster, more reliable, and can detect hazardous gases that humans cannot. The basic design of an electronic nose involves pulling a gas sample through a sensor array to produce response signals. Common sensor technologies are described along with the sample delivery, detection, and computing systems of electronic noses. Applications discussed include medical diagnosis, environmental monitoring, food quality control, and explosive detection. In conclusion, while electronic noses are cheaper and faster than biological noses, they still cannot match the sensitivity and selectivity of mammalian noses.
This document provides an overview of electronic noses. It defines an electronic nose as a device that detects odors or flavors using an array of sensors that generate electrical signals in response to volatile compounds. The document then discusses the basic design of an electronic nose and how it works, comparing it to the biological nose. It also outlines common sensor technologies, applications such as medical diagnosis and food quality control, and concludes that while not as sensitive as the mammalian nose, the electronic nose is a useful analytical tool.
The document provides an overview of electronic noses (e-noses). It defines an e-nose as a device that detects odors and flavors using an array of sensors that generate electrical signals in response to volatile compounds. The document discusses the history, working principle, sensor technologies, applications, and conclusion of e-noses. It notes that e-noses were first reported in 1964 and aim to mimic the mammalian olfactory system to allow repeatable identification and classification of aroma mixtures. The working principle involves a sample delivery system to generate headspace, sensors to detect physical changes from compound absorption, and a computing system to analyze the responses.
1. The document describes the human olfactory system and electronic nose (e-nose).
2. It explains that an e-nose aims to mimic the mammalian olfactory system and consists of a sensor array, transducer, and pattern recognition system to detect and identify odors.
3. The e-nose provides advantages over the human nose and gas chromatography-mass spectrometry (GC-MS) as it is portable, inexpensive, reproducible, and can detect hazardous gases.
The document discusses electronic noses (e-noses), which are devices that mimic the human sense of smell. An e-nose uses an array of electronic sensors and pattern recognition systems to detect odors. It works by sampling odors, analyzing them using different sensors, and then using artificial intelligence to identify the odor based on previous samples. E-noses have various applications like environmental monitoring, medical diagnosis, and quality control in food production. While they have advantages over human smell in speed and sensitivity, e-noses still cannot perfectly mimic the complexity of the human olfactory system.
An electronic nose is a device that identifies the specific components of an odor and analyzes its chemical makeup. It consists of sensors for chemical detection and neural networks for pattern recognition. The electronic nose was developed to mimic the human nose and consists of three parts: a sample delivery system, detection system with sensors, and a computing system. Some applications of electronic noses include medical diagnosis, environmental monitoring, food quality control, and space applications. While electronic noses have advantages over biological noses, challenges remain in fully mimicking the human olfactory system. Future developments may lead to more advanced electronic nose technologies.
The document discusses detecting lung cancer at an early stage through analysis of volatile organic compounds (VOCs) in exhaled air using an electronic nose. It describes the two main types of lung cancer and detection methods like CT scans. The electronic nose system collects breath samples from patients and analyzes VOCs using sensor arrays. Certain VOCs like 1-butanol and 3-hydroxy-2-butanone indicate lung cancer. Analysis of peak height, rate to peak height, rate of recovery, and area under the curve found lower values for cancer patients. The electronic nose mimics the mammalian olfactory system and uses sensors like MOSFETs and optical fibers to detect chemical changes from VOCs. This non-invasive, inexpensive
Seminar on Electronic-NOSE (E-NOSE) By- MAYANK SAHUmayank843
The document discusses electronic noses (e-noses), which aim to mimic the human sense of smell. E-noses consist of sensor arrays that detect odors and pattern recognition systems that interpret the sensor readings. They have various applications in areas like environmental monitoring, quality control, and medical diagnosis. While e-noses have advantages over human smell in speed, accuracy, and ability to detect hazardous substances, challenges remain in fully replicating the human olfactory system. Future improvements to sensor arrays and neural network techniques may lead to more advanced electronic noses.
Current and Future Research of E Nose(Electronic Nose)Nadiya Mahjabin
Abstract:-- Over last decade electronic sensing or e-sensing becomes an important
technology from both of technical and commercial point of view which refers to the
capability of reproducing human senses using sensor arrays and pattern recognition
system. An electronic nose is such an instrument which consists of mechanism for
identification of chemical detection such as an array of electronic sensors and a
mechanism of pattern recognition. This paper highlights the significant researches of
Electronic nose which are being performed currently and at the same time how can we
use it in the future more effectively .
This document proposes the development of an "Artificial Electronic Nose" capable of detecting toxic gases, chemical vapors, smoke, and flammable oils through an array of sensors. The nose would not only detect the presence of these elements but also quantify their concentration. It aims to benefit those unable to detect hazards in their environment and would find application in industrial monitoring. The project is proposed to occur in two phases: first enhancing an existing industrial interface and integrating relevant sensors, then exploring a bio-electric interface for impaired patients. The feasibility lies in building upon commercially available gas detection technology and simulations show the design could accommodate 24 sensors with further resources.
The document presents an abstract on an electronic nose (e-nose) device called the Pico-1 that was used to analyze groups of coffees. The e-nose was able to classify the different coffee types with over 90% accuracy and also predict sensory descriptors assessed by coffee judges. E-noses can discriminate between similar food products like different coffee blends and roasting levels, as well as correlate to sensory data from human panels, making them a valid tool for routine food analysis compared to more expensive techniques like gas chromatography–mass spectrometry.
An electronic nose uses an array of chemical sensors and pattern recognition systems to detect odors and flavors. It consists of three main parts: a sample delivery system that introduces odors to sensors, a detection system containing sensors that react and record responses to compounds, and a computing system that analyzes the sensor responses against a database to identify samples. Electronic noses are used in quality control, healthcare, security, environmental monitoring and other industrial applications such as detecting contaminants, monitoring storage conditions, diagnosing diseases from volatile compounds, and detecting explosives.
The document discusses using artificial neural networks for electronic noses. It begins with an abstract that provides background on neural networks and their use in pattern recognition. The document then discusses how electronic noses work, using an array of chemical sensors and neural networks to identify chemicals. It provides details on the components of electronic noses, including different types of sensors, and how neural networks are trained and used for identification. Applications discussed include using electronic noses for medical diagnosis by analyzing odors from the body. The document concludes that further work involves comparing neural network analysis to other techniques and evolving electronic nose prototypes into field systems.
Application of electronic nose for rapid diagnosis of tuberculosisAnkit Kumar Singh
1) The document proposes using an electronic nose to rapidly diagnose tuberculosis through analysis of volatile organic compounds (VOCs) in exhaled breath.
2) The electronic nose contains an array of gas sensors that detect patterns in the VOCs, and principal component analysis is used to analyze the sensor signals.
3) Comparison of the VOC patterns with a reference library can diagnose tuberculosis with high sensitivity and determine the degree of similarity to reference samples.
The document discusses electronic noses, or e-noses. It defines an e-nose as a multi-sensor system that can detect and identify chemicals through sensor fusion and data analysis. E-nose sensors fall into four main categories: conductivity sensors, piezoelectric sensors, MOSFET sensors, and optical sensors. Conductivity sensors include polymer sensors and metal oxide sensors. Piezoelectric sensors include quartz crystal microbalance sensors and surface acoustic wave sensors. The document provides examples of how different sensor types work and concludes that preparing the presentation helped the authors learn more about e-noses.
The document discusses electronic noses, which are devices that mimic the human sense of smell. It describes electronic noses as consisting of an array of electronic sensors and a neural network for pattern recognition. The document then compares the biological nose to an electronic nose and discusses their similarities. It explains the need for electronic noses due to limitations of human sniffers. The working, principle, and applications of electronic noses are outlined, including uses in food, medical, environmental, and other industries. The document concludes that while not as sensitive as biological noses, electronic noses can perform analyses faster and at lower cost than human sniffers.
The document discusses the history and components of artificial noses. It describes how artificial noses were first conceptualized in 1982 and developed in 2008. The key components are a sample delivery system, detection systems using sensors like metal oxide semiconductors, and a computing system. Different types of sensors are explained, along with applications in quality control and future prospects like medical diagnostics and security screening. In conclusion, the artificial nose is presented as a useful analytical tool that can perform repetitive detection tasks better than humans.
The document discusses fibre optic sensors for measuring pH. It describes how fibre optic sensors work by modulating light properties like intensity, phase or wavelength. They have advantages for biomedical applications like in vivo monitoring. An ideal fibre optic sensor for biomedicine would be reliable, easy for operators to use, and low cost. The document discusses measuring blood pH, gastric/esophageal pH, and tissue pH using fibre optic sensors. It describes different sensor designs and challenges like sensitivity to light propagation. Fibre optic sensors eliminate drawbacks of traditional glass pH electrodes.
The document provides an overview of electronic noses (e-noses). It discusses what an e-nose is, its main components including sensing and pattern recognition systems, and the types of sensors used. The key components are sensors that detect volatile organic compounds and a computing system that analyzes the sensor responses. It also outlines how e-noses work by exposing sensors to samples, measuring responses, and using the response pattern to identify chemicals. Common sensor types are metal oxide and polymer sensors, with each responding slightly differently to compounds. E-noses have applications in research, quality control, and production monitoring.
The document discusses electronic noses, which are devices that mimic the human sense of smell. It describes electronic noses as consisting of an array of electronic sensors and a neural network for pattern recognition. The document then compares the biological nose to an electronic nose and discusses their similarities. It explains the need for electronic noses due to limitations of human sniffers. The working, principle, and applications of electronic noses are outlined, including uses in food, medical, environmental, and other industries. The document concludes that while not as sensitive as biological noses, electronic noses can perform analyses faster and at lower cost than human sniffers.
The document provides an overview of electronic noses (e-noses). It defines an e-nose as a device that detects odors and flavors using an array of sensors that generate electrical signals in response to volatile compounds. The document discusses the history, working principle, sensor technologies, applications, and conclusion of e-noses. It notes that e-noses were first reported in 1964 and aim to mimic the mammalian olfactory system to allow repeatable identification and classification of aroma mixtures. The working principle involves a sample delivery system to generate headspace, sensors to detect physical changes from compound absorption, and a computing system to analyze the responses.
Computer Aided Sensory Evaluation of Food And BeveragesJagriti Bhasin
This document discusses computer-aided sensory evaluation tools for beverages. It describes electronic nose (e-nose) and electronic tongue (e-tongue) systems that can mimic and measure some human sensory functions like smell and taste. The e-nose uses sensor arrays and pattern recognition to detect volatile compounds. The e-tongue uses sensor arrays and electrochemical cells to analyze soluble compounds. Both tools generate data that requires multivariate analysis. The document also discusses the Compusense Five sensory evaluation software that facilitates planning, conducting, and analyzing sensory tests.
The document provides an overview of electronic noses (e-noses). It defines an e-nose as a device that detects odors and flavors using an array of sensors that generate electrical signals in response to volatile compounds. The document discusses the history, working principle, sensor technologies, applications, and conclusion of e-noses. It notes that e-noses were first reported in 1964 and aim to mimic the mammalian olfactory system to allow repeatable identification and classification of aroma mixtures. The working principle involves a sample delivery system to generate headspace, sensors to detect physical changes from compound absorption, and a computing system to analyze the responses.
1. The document describes the human olfactory system and electronic nose (e-nose).
2. It explains that an e-nose aims to mimic the mammalian olfactory system and consists of a sensor array, transducer, and pattern recognition system to detect and identify odors.
3. The e-nose provides advantages over the human nose and gas chromatography-mass spectrometry (GC-MS) as it is portable, inexpensive, reproducible, and can detect hazardous gases.
The document discusses electronic noses (e-noses), which are devices that mimic the human sense of smell. An e-nose uses an array of electronic sensors and pattern recognition systems to detect odors. It works by sampling odors, analyzing them using different sensors, and then using artificial intelligence to identify the odor based on previous samples. E-noses have various applications like environmental monitoring, medical diagnosis, and quality control in food production. While they have advantages over human smell in speed and sensitivity, e-noses still cannot perfectly mimic the complexity of the human olfactory system.
An electronic nose is a device that identifies the specific components of an odor and analyzes its chemical makeup. It consists of sensors for chemical detection and neural networks for pattern recognition. The electronic nose was developed to mimic the human nose and consists of three parts: a sample delivery system, detection system with sensors, and a computing system. Some applications of electronic noses include medical diagnosis, environmental monitoring, food quality control, and space applications. While electronic noses have advantages over biological noses, challenges remain in fully mimicking the human olfactory system. Future developments may lead to more advanced electronic nose technologies.
The document discusses detecting lung cancer at an early stage through analysis of volatile organic compounds (VOCs) in exhaled air using an electronic nose. It describes the two main types of lung cancer and detection methods like CT scans. The electronic nose system collects breath samples from patients and analyzes VOCs using sensor arrays. Certain VOCs like 1-butanol and 3-hydroxy-2-butanone indicate lung cancer. Analysis of peak height, rate to peak height, rate of recovery, and area under the curve found lower values for cancer patients. The electronic nose mimics the mammalian olfactory system and uses sensors like MOSFETs and optical fibers to detect chemical changes from VOCs. This non-invasive, inexpensive
Seminar on Electronic-NOSE (E-NOSE) By- MAYANK SAHUmayank843
The document discusses electronic noses (e-noses), which aim to mimic the human sense of smell. E-noses consist of sensor arrays that detect odors and pattern recognition systems that interpret the sensor readings. They have various applications in areas like environmental monitoring, quality control, and medical diagnosis. While e-noses have advantages over human smell in speed, accuracy, and ability to detect hazardous substances, challenges remain in fully replicating the human olfactory system. Future improvements to sensor arrays and neural network techniques may lead to more advanced electronic noses.
Current and Future Research of E Nose(Electronic Nose)Nadiya Mahjabin
Abstract:-- Over last decade electronic sensing or e-sensing becomes an important
technology from both of technical and commercial point of view which refers to the
capability of reproducing human senses using sensor arrays and pattern recognition
system. An electronic nose is such an instrument which consists of mechanism for
identification of chemical detection such as an array of electronic sensors and a
mechanism of pattern recognition. This paper highlights the significant researches of
Electronic nose which are being performed currently and at the same time how can we
use it in the future more effectively .
This document proposes the development of an "Artificial Electronic Nose" capable of detecting toxic gases, chemical vapors, smoke, and flammable oils through an array of sensors. The nose would not only detect the presence of these elements but also quantify their concentration. It aims to benefit those unable to detect hazards in their environment and would find application in industrial monitoring. The project is proposed to occur in two phases: first enhancing an existing industrial interface and integrating relevant sensors, then exploring a bio-electric interface for impaired patients. The feasibility lies in building upon commercially available gas detection technology and simulations show the design could accommodate 24 sensors with further resources.
The document presents an abstract on an electronic nose (e-nose) device called the Pico-1 that was used to analyze groups of coffees. The e-nose was able to classify the different coffee types with over 90% accuracy and also predict sensory descriptors assessed by coffee judges. E-noses can discriminate between similar food products like different coffee blends and roasting levels, as well as correlate to sensory data from human panels, making them a valid tool for routine food analysis compared to more expensive techniques like gas chromatography–mass spectrometry.
An electronic nose uses an array of chemical sensors and pattern recognition systems to detect odors and flavors. It consists of three main parts: a sample delivery system that introduces odors to sensors, a detection system containing sensors that react and record responses to compounds, and a computing system that analyzes the sensor responses against a database to identify samples. Electronic noses are used in quality control, healthcare, security, environmental monitoring and other industrial applications such as detecting contaminants, monitoring storage conditions, diagnosing diseases from volatile compounds, and detecting explosives.
The document discusses using artificial neural networks for electronic noses. It begins with an abstract that provides background on neural networks and their use in pattern recognition. The document then discusses how electronic noses work, using an array of chemical sensors and neural networks to identify chemicals. It provides details on the components of electronic noses, including different types of sensors, and how neural networks are trained and used for identification. Applications discussed include using electronic noses for medical diagnosis by analyzing odors from the body. The document concludes that further work involves comparing neural network analysis to other techniques and evolving electronic nose prototypes into field systems.
Application of electronic nose for rapid diagnosis of tuberculosisAnkit Kumar Singh
1) The document proposes using an electronic nose to rapidly diagnose tuberculosis through analysis of volatile organic compounds (VOCs) in exhaled breath.
2) The electronic nose contains an array of gas sensors that detect patterns in the VOCs, and principal component analysis is used to analyze the sensor signals.
3) Comparison of the VOC patterns with a reference library can diagnose tuberculosis with high sensitivity and determine the degree of similarity to reference samples.
The document discusses electronic noses, or e-noses. It defines an e-nose as a multi-sensor system that can detect and identify chemicals through sensor fusion and data analysis. E-nose sensors fall into four main categories: conductivity sensors, piezoelectric sensors, MOSFET sensors, and optical sensors. Conductivity sensors include polymer sensors and metal oxide sensors. Piezoelectric sensors include quartz crystal microbalance sensors and surface acoustic wave sensors. The document provides examples of how different sensor types work and concludes that preparing the presentation helped the authors learn more about e-noses.
The document discusses electronic noses, which are devices that mimic the human sense of smell. It describes electronic noses as consisting of an array of electronic sensors and a neural network for pattern recognition. The document then compares the biological nose to an electronic nose and discusses their similarities. It explains the need for electronic noses due to limitations of human sniffers. The working, principle, and applications of electronic noses are outlined, including uses in food, medical, environmental, and other industries. The document concludes that while not as sensitive as biological noses, electronic noses can perform analyses faster and at lower cost than human sniffers.
The document discusses the history and components of artificial noses. It describes how artificial noses were first conceptualized in 1982 and developed in 2008. The key components are a sample delivery system, detection systems using sensors like metal oxide semiconductors, and a computing system. Different types of sensors are explained, along with applications in quality control and future prospects like medical diagnostics and security screening. In conclusion, the artificial nose is presented as a useful analytical tool that can perform repetitive detection tasks better than humans.
The document discusses fibre optic sensors for measuring pH. It describes how fibre optic sensors work by modulating light properties like intensity, phase or wavelength. They have advantages for biomedical applications like in vivo monitoring. An ideal fibre optic sensor for biomedicine would be reliable, easy for operators to use, and low cost. The document discusses measuring blood pH, gastric/esophageal pH, and tissue pH using fibre optic sensors. It describes different sensor designs and challenges like sensitivity to light propagation. Fibre optic sensors eliminate drawbacks of traditional glass pH electrodes.
The document provides an overview of electronic noses (e-noses). It discusses what an e-nose is, its main components including sensing and pattern recognition systems, and the types of sensors used. The key components are sensors that detect volatile organic compounds and a computing system that analyzes the sensor responses. It also outlines how e-noses work by exposing sensors to samples, measuring responses, and using the response pattern to identify chemicals. Common sensor types are metal oxide and polymer sensors, with each responding slightly differently to compounds. E-noses have applications in research, quality control, and production monitoring.
The document discusses electronic noses, which are devices that mimic the human sense of smell. It describes electronic noses as consisting of an array of electronic sensors and a neural network for pattern recognition. The document then compares the biological nose to an electronic nose and discusses their similarities. It explains the need for electronic noses due to limitations of human sniffers. The working, principle, and applications of electronic noses are outlined, including uses in food, medical, environmental, and other industries. The document concludes that while not as sensitive as biological noses, electronic noses can perform analyses faster and at lower cost than human sniffers.
The document provides an overview of electronic noses (e-noses). It defines an e-nose as a device that detects odors and flavors using an array of sensors that generate electrical signals in response to volatile compounds. The document discusses the history, working principle, sensor technologies, applications, and conclusion of e-noses. It notes that e-noses were first reported in 1964 and aim to mimic the mammalian olfactory system to allow repeatable identification and classification of aroma mixtures. The working principle involves a sample delivery system to generate headspace, sensors to detect physical changes from compound absorption, and a computing system to analyze the responses.
Computer Aided Sensory Evaluation of Food And BeveragesJagriti Bhasin
This document discusses computer-aided sensory evaluation tools for beverages. It describes electronic nose (e-nose) and electronic tongue (e-tongue) systems that can mimic and measure some human sensory functions like smell and taste. The e-nose uses sensor arrays and pattern recognition to detect volatile compounds. The e-tongue uses sensor arrays and electrochemical cells to analyze soluble compounds. Both tools generate data that requires multivariate analysis. The document also discusses the Compusense Five sensory evaluation software that facilitates planning, conducting, and analyzing sensory tests.
Neuro identification of some commonly used volatile organic compounds using e...Alexander Decker
This document describes a study that used an electronic nose system with three gas sensors and an artificial neural network to identify three volatile organic compounds (formaldehyde, acetone, and chloroform). Sensor responses were analyzed and 60% of data was used to train the neural network, which successfully identified the compounds. The system has potential for applications in environmental monitoring and industrial process control.
The high demand of meat causes the seller mix the fresh and not-fresh meat. Electronic nose was used to detect the quality of the meat quickly and accurately. This research is proposed to test and analyze the sensitivity of MOS sensor in the electronic nose and simulate it using Matlab to identify meat classification using neural network. Test parameters based on Indonesian National Standard (SNI 3932-2008) requirement on the quality of carcass and meat. In this simulation, the number of neurons in the hidden layer was varied to find the most accurate identification. The sensitivity analysis of the MOS sensor was conducted by testing the meat sample aroma, calculate the sensitivity, identify the formation of input, hidden layer, outputs, and simulate the result of the varied formation. Then, found the number of the most optimal neurons. The result of the data training will be applied to the real instrument.
A low-cost sensor array system for banana ripeness monitoring is presented. The sensors are constructed by employing a graphite line-patterning technique (LPT) to print interdigitated graphite electrodes on tracing paper and then coating the printed area with a thin film of polyaniline (PANI) by in-situ polymerization as the gas-sensitive layer. The PANI layers were used for the detection of volatile organic compounds (VOCs), including ethylene, emitted during ripening. The influence of the various acid dopants, hydrochloric acid (HCl), methanesulfonic acid (MSA), p-toluenesulfonic acid (TSA) and camphorsulfonic acid (CSA), on the electrical properties of the thin film of PANI adsorbed on the electrodes was also studied. The extent of doping of the films was investigated by UV-Vis absorption spectroscopy and tests showed that the type of dopant plays an important role in the performance of these low-cost sensors. The array of three sensors, without the PANI-HCl sensor, was able to produce a distinct pattern of signals, taken as a signature (fingerprint) that can be used to characterize bananas ripeness.
An artificial nose (e-nose) uses sensor arrays and pattern recognition systems to identify and analyze odors, similar to a biological nose. The document describes the development of an e-nose called the Wi-Nose to monitor wine fermentation. It would use sensors to detect ethanol and carbon dioxide levels characteristic of different fermentation stages, and a neural network trained on sample data to classify the stage. The Wi-Nose design incorporates sensors well-suited for detecting these compounds, and the document outlines its testing and training using a neural network tool in Excel.
Crimson Publishers -A Sensor Multiplatform for Non Invasive Diagnosis of Pros...CrimsonPublishers-SBB
Crimson Publishers -A Sensor Multiplatform for Non Invasive Diagnosis of Prostate Cancer By A D'Amico in Significances of Bioengineering & Biosciences
This work underlines the utility of a multiplatform based on the use of both artificial lactation and taste systems that show the ability to perform both gas and bio liquid chemical imaging. Preliminary results related to the investigation of prostate cancer suggest that high performance levels with diagnostic and screening purposes can be reached with non invasive experimental procedures.
The use of agrochemicals has increased considerably in recent years, and consequently, there has been increased exposure of ecosystems and human populations to these highly toxic compounds. The study and development of methodologies to detect these substances with greater sensitivity has become extremely relevant. This article describes, for the first time, the use of atomic force spectroscopy (AFS) in the detection of enzyme-inhibiting herbicides. A nanobiosensor based on an atomic force microscopy (AFM) tip functionalised with the acetolactate synthase (ALS) enzyme was developed and characterised. The herbicide metsulfuron-methyl, an ALS inhibitor, was successfully detected through the acquisition of force curves using this biosensor. The adhesion force values were considerably higher when the biosensor was used. An increase of ~250% was achieved relative to the adhesion force using an unfunctionalised AFM tip. This considerable increase was the result of a specific interaction between the enzyme and the herbicide, which was primarily responsible for the efficiency of the nanobiosensor. These results indicate that this methodology is promising for the detection of herbicides, pesticides, and other environmental contaminants.
- The document presents a case study on using an electronic nose to evaluate the quality of three types of grapes (green, red, black) over 10 days of storage.
- Principal component analysis was used to analyze the sensor response data and distinguish the grapes at different stages of ripening and quality degradation.
- The results showed the electronic nose could clearly identify and differentiate the grapes based on storage time, with samples further from the origin on the PCA plots indicating poorer quality. This demonstrated the potential of electronic noses for non-destructive quality evaluation of fruits.
The document summarizes advances in electronic nose technologies. It discusses the historical development of electronic noses from the 1950s to present. It describes the four main sensor types used in electronic noses: metal oxide semiconductors, metal oxide semiconductor field-effect transistors, conducting polymer sensors, and piezoelectric acoustic sensors. Each sensor type is explained in terms of its sensitive material, detection principle, and factors that influence selectivity and sensitivity. The document provides an overview of electronic nose technologies and the sensor types commonly used.
Reliable e-nose for air toxicity monitoring by filter diagonalization methodIJECEIAES
This document describes an electronic nose (e-nose) device designed to detect toxic air compounds that could threaten human health. The e-nose uses an array of 6 metal oxide semiconductor sensors to detect gases like carbon monoxide, combustible gas, hydrogen, methane, and smoke. It includes an electronic module for data acquisition and software for signal processing and visualization. This study proposes using the filter diagonalization method to analyze the spectral content of the sensor signals. Preliminary results found the prototype to be functional and the filter diagonalization method suitable for later classification. Potential applications of the e-nose include indoor and hazardous environment monitoring.
Performance of electronic nose based on gas sensor-partition column for synth...TELKOMNIKA JOURNAL
Electronic nose (e-nose) has been developed and implemented in a wide area, included in food industries. This study was conducted to investigate the performance of an e-nose that utilizes a packed gas chromatography column and a gas sensor for classification of synthetic flavor products. There were six aroma variants of synthetic flavor evaluated, namely durian, jackfruit, ambonese banana, melon, orange and lemon. The e-nose was designed with four main parts, namely aroma provider, column and detector room, microcontroller, and data acquisition system. The device was operated automatically at a stable temperature of 60 °C. Collected data consisted of ten data of each sample was preprocessed by baseline equalization and normalization, extracted its distinctive feature and then were analyzed through pattern recognition analysis. There were two kinds of methods used to analyzed the patterns of the data, namely a fuzzy c-means clustering and an artificial neural network (ANN). With the fuzzy c-means clustering,
the result was six data clusters with an unbalanced number of members, indicated that this analysis could not classify samples properly. Meanwhile, analysis with the ANN could classify properly the samples with the level of accuracy of 70%.
This document summarizes a presentation on developing a MEMS-based gas sensor for early detection of lung cancer. It introduces lung cancer and its symptoms. It then describes a gas sensor that could detect volatile organic compounds in exhaled breath to identify biomarkers for lung cancer. The presentation outlines fabricating surface acoustic wave sensors and reviewing literature on gas sensors to detect aromatic hydrocarbons and graphene-based sensors for lung cancer detection.
The document discusses several applications of biosensors and nanomaterials in biosensors. It describes how researchers at the Institute of Bioengineering and Nanotechnology developed a simple method to organize cells and their microenvironments in hydrogel fibers, providing a template for assembling complex tissues. It also discusses how researchers at NYU-Poly used a nano-enhanced biosensor to detect a single cancer marker protein smaller than any known virus or molecule, setting a new limit of detection.
Nanotechnology has potential applications in the food industry including increasing nutritional value, improving packaging and detecting food contaminants. Some key areas discussed are nanoencapsulation to protect nutrients, nanosensors to detect pathogens and chemicals, and active nano-enabled food packaging with antimicrobial properties. While nanotechnology offers benefits, safety research is still needed to address concerns around potential health effects of ingesting nanoparticles. Stakeholder education is important as nanotechnology in food is still an emerging field.
Design of Electronic Nose System Using Gas Chromatography Principle and Surfa...TELKOMNIKA JOURNAL
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.
Biosensors are the analytical device that are used to measure the concentration of analye , these type of biosensors are made with conjugation of enzymes as a biological eliment to quantify a (bio)chemical substance / analyte are reffered to as Enzyme-probe Biosensors .
Biosensors are of many types but focusing on Enzyme biosensors there are 4 main types which are briefly described in this power point presentation .
2. TNAU- Tamil Nadu Agricultural University
Department of Food and Agrl. Process Engineering
Seminar on
“ELECTRONIC NOSE (E-nose)”
Presented by:
Nisha. R
Ph.D Scholar
2016804101
2
6/21/2017Nisha.R
3. Electronic noses are engineered to mimic the
mammalian olfactory system.
Instrument designed to allow repeatable identifications
and classifications of aroma mixtures.
Determines the various characteristics properties of the
odour while eliminating operator fatigue.
Hundreds of different prototypes of artificial-nose
devices have been developed to discriminate complex
vapor mixtures containing many different types of volatile
organic compounds (VOCs)
e-sensing
Refers to the capability of reproducing human senses
using sensor arrays and pattern recognition systems.
6/21/2017 3Nisha. R
4. An electronic nose is an array of non-specific chemical
sensors, controlled and analyzed electronically, which
mimics the action of the mammalian nose by recognizing
patterns of response to vapors.
The sensors will conduct chemical sensors which
change resistance when exposed to vapors, and are not
specific to any one vapor it is in the use of an array of
sensors, each with a different sensing medium.
6/21/2017 4Nisha. R
5. Of all the five senses, olfaction uses the largest part
of the brain and is an essential part of our daily lives.
Quantifying smells are useful in gas
chromatography
Human nose is very sensitive.
Subject to fatigue, inconsistencies, adaptation etc.
Smelling toxic gases may involve risk
6/21/2017 5Nisha. R
6. Bio-nose Electronic nose
It uses the lungs to bring the odor
to epithelium layer
It employs a pump to smell the odor
It has mucus, membrane, and hair to
act as filter
It has an inlet sampling system that
provides filtration
The human nose contains the olfactory
epithelium, which contains millions of
Sensing cells that interact with
odorants in unique
Electronic nose has a variety of sensors
that interact differently with a group of
odorous molecules
Convert the chemical response to
electronic nerve impulses whose
unique patterns are propagated by
neurons.
Electronic nose react with the sample
and produce electrical signals.
6/21/2017 6Nisha. R
8. Sample handling system
Sensing system
Data acquisition system
Signal processing & pattern
recognition
sample injection stage
Result recorder6/21/2017 8Nisha. R
9. Eliminates all undesirable factors that could affect sensor response
and ensures a stable and repeatable headspace of gas sampling
environment.
Temperature and humidity are constantly monitored both in the
sample chamber and in the sensor chamber, while the analysis is
running.
After headspace sampling, ambient air is applied to both chambers
to prevent potential contamination (residue from previous sample
and environment).
The sample chamber must be made from non-adsorbing and inert
materials.
6/21/2017 9Nisha. R
10. It is comprised by a group of several sensors measuring
different flavor properties with various selectivity or by
a single detecting device (e.g., a mass spectrometer)
carrying out a series of measurements of a given aroma
profile, or a combination of these two types.
The type and number of sensors play a key role in
determining the applicability of the e-nose instrument.
6/21/2017 10Nisha. R
11. The signal is processed, and a distinguishing feature of
this system is the recording method of the sensor
response signal.
Some aromas change their profiles over time,
depending on whether they are static or dynamic.
A data series formed by averaging signals from a
sensor is more suitable.
It can provide more information on aromas and an
identification process for flavors is more simple and
reliable.
6/21/2017 11Nisha. R
12. It identifies the odour profile by comparison with known
profiles from the database.
It associates each pattern to one of many possible reference
classes. Odors are characterized on the basis of greater or
lesser similarity of given features and they are assigned to a
given class.
6/21/2017 12
Primed by passing a reference gas or pure dry air through
the sample and sensor chambers.
Removes residues and impurities remaining from a
previously analyzed sample
Nisha. R
13. 6/21/2017 13
Rmin is the
baseline resistance
Rmax the
resistance in the
odor
I – flow of
reference gas
II – measuring
sample headspace
III –recovery step
Arshak, et.al, 2004
Fig. 4. : Characteristic of the response of e-nose
sensor to an odorant.
Nisha. R
14. 6/21/2017 14
The sensor array is clearly the key element. It forms the
primary step in the detection or identification of an
odorant.
The sensors on an electronic nose system should be
selectively sensitive to odors which may be present in a
given kind of tested sample.
The most commonly used sensors in electronic nose are:
sensors
Metal oxide
sensors
(MOS)
Conducting
polymers
Optical
sensors
Piezoelectric
sensors
Nisha. R
15. Metal oxide sensors (MOS)
Commonly and most utilized sensor systems for the
development of electronic noses to detect gaseous
molecules.
Major principle: adsorption or desorption of gaseous
molecules on the surface of a metal oxide changes the
conductivity of material.
When oxides are exposed to volatile organic compounds
(VOCs), they are involved in a redox reaction on the
surface of the MOS or act as oxidizing agents and, thereby,
cause a shift in the resistance of the MOS
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16. 6/21/2017 Nisha. R 16
Conducting polymer sensors
Here the active material is a conducting polymer from
such families as the Polypyrroles, thiophenes, indoles
or furans.
CP sensor arrays often consist of unique polymers with
different reversible physicochemical properties and
sensitivity to groups of volatile.
These organic vapors attach to and interact with the
polymer surface, changing the resistance under
ambient temperature conditions
17. All of the polymer films on a set of electrodes (sensors) start out
at a measured resistance, their baseline resistance. If there has
been no change in the composition of the air, the films stay at the
baseline resistance and the percent change is zero.
e- e- e- e- e- e-
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18. Each polymer changes its size, and therefore its resistance, by a different
amount, making a pattern of the change
e- e- e-
e- e-e-
e-
e-
e-
e-
e- e-
6/21/2017 18Nisha. R
If a different compound had caused the polymer to change, the
pattern of the polymer films' change would have been different.
19. PIEZOELECTRIC SENSORS
A piezoelectric sensor is a device that uses the
piezoelectric effect to measure any physical change
like pressure, strain etc. Here,the gas adsorption leads
to change in mass of the sensor .
Quartz crystal microbalance (QCM) and surface
acoustic wave (SAW) sensors are two of the most
useful piezoelectric sensors applied in electronic noses.
6/21/2017 19Nisha. R
20. OPTICAL SENSORS
These utilize glass fibers with a chemically active
material coating on their sides or ends.
A light source is used to interrogate the active
material which responds with the change in color
to the presence of VOCs.
The active material contains chemically active
fluorescent dyes. As the VOCs interact with it, the
color of the fluorescent dye changes, hence lead to
detection.
6/21/2017 20Nisha. R
22. The Cyranose 320 is a
handheld “electronic
nose” developed by
Cyrano Sciences of
Pasadena, California in
2000.
Applications researched
using the Cyranose 320
includes the detection of
COPD, and other medical
conditions as well as
industrial applications
generally related to
quality control or
contamination detection.
6/21/2017 22Nisha. R
23. Nisha. R 23
Industry
sector
Application area Specific use types and
examples
Agriculture crop protection
harvest timing & storage
meat, seafood, & fish
products
plant production
pre- & post-harvest
diseases
safe food supply, crop ripeness,
preservation treatments, freshness,
contamination, spoilage, cultivar
selection, variety characteristics,
plant disease diagnoses, pest
identification
Environmental air & water quality
monitoring, indoor air
quality control
pollution detection, effluents, toxic
spills, toxic/hazardous gases
Food & beverage quality control
assessments, ripeness, food
contamination, taste, smell
characteristics
ingredient confirmation, content
standards, marketable condition,
spoilage, shelf life
Manufacturing processing controls,
product uniformity, safety,
security, work conditions
product characteristics &
consistency, aroma and flavor
characteristics, fire alarms, toxic
gas leak detection
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26. Kriengkri et al ., 2016
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Evaluation of bacterial population on chicken
meats using a briefcase electronic nose
Objectives
Performance of novel portable electronic nose (E-nose)
based on eight metal oxide sensors was used for
evaluation of chicken meat freshness and bacterial
population on chicken meat stored at 4.0 C and 30.0 C
for up to 5 days
27. Sliced chicken breast meat samples were obtained from a
local market.
The samples were then cut into pieces of the same weight
(10 g ± 1 g) and stored under different temperatures (4.0o
C and 30.0o C) for 5 days in temperature controller
incubators.
These storage temperatures were selected to mimic the
sample storage in a refrigeration system (4.0o C) and room
temperature (30.0o C).
Chicken freshness evaluation was performed everyday by
using E-nose measurement.
6/21/2017 27Nisha. R
28. Fig. 1: Briefcase E-nose.
The portable E-nose system, called
briefcase Enose consists of four
parts
(I) valves and air
pump with mass flow controller
(II) sampling system
(III)sensor array and
(IV) data acquisition (DAQ) with a
computer
6/21/2017 28Nisha. R
29. List of gas sensors with sensing types and
detection ranges.
Sensor name Sensing type Typical detection
ranges (ppm)
TGS 821 Hydrogen 10-10,000
TGS 822 Organic solvent vapours 50-5000
TGS 825 Hydrogen sulfide 5-100
TGS 826 Ammonia & alcohols 30-300
TGS 2600 Air contaminants 1-100
TGS 2602 VOCs and odorous gases 1-30
TGS 2610 LP gas 300-10,000
TGS 2620 Solvent vapours 50-5000
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30. Fig. 2: Schematic diagram of the briefcase E-nose.
(Timsorn et.al, 2016)
6/21/2017 30Nisha. R
31. The changing of sensor
resistance results from the
adsorption/ desorption
reactions between the
volatile organic
compounds (VOCs) from
sliced chicken breast and
the adsorbed oxygen ion
(O) on the crystal surface
of MOS.
Fig. 3: A signal of the sensor recorded as
resistance values versus time.
6/21/2017 31Nisha. R
32. 6/21/2017 32Nisha. R
Fig. 6: Three dimension plots of average sensor responses to sample
odours when samples were stored (a) 30.0o C and (b) 4.0o C for three
and five storage days, respectively.
34. A portable E-nose based on eight MOS sensors with specially
designed system and sensor chamber has been successfully
constructed and used for identification of chicken breast freshness.
The E-nose exhibits fast response to chicken breast odours within
20e30 s. Storage temperature strongly affects the rate of spoilage
bacterial development in chicken meat.
Most VOCs emitted from chicken breast malodour are reducing
gases.
With PCA analysis, the E-nose can well classify the chicken breast
odours corresponding to different storage days (0-5 days) and
temperatures.
It is a portable, rapid, low cost, nondestructive technique, which can
achieve high accuracy and can be used without environmental
controls (no need for nitrogen or air zero as a carrier gas)..0 C and
4.0 C).
6/21/2017 34Nisha. R
35. E-nose to trace tomato-juice quality
XuezhenHonget.al,2015
6/21/2017 35Nisha. R
36. E-nose and sampling procedure.
Each sample (10 mL of cherry tomato juice) was placed
in a 500 mL airtight glass vial that was sealed with
plastic wrap for 10 min.
The headspace gaseous compounds were pumped into
the sensor arrays through Teflon tubing connected to a
needle in the plastic wrap, causing the ratio of
onductance G/GO
(G and G0 are conductance of the sensors exposed to sample gas
and zero gas, respectively)
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37. 6/21/2017 37
Fig. 1. Typical e-nose responses to juices squeezed from
youbei cherry tomatoes stored for (a) 7 and (b) 8 days.Nisha. R
39. 6/21/2017 39
Fig. 3. Visualization of underlying data structure of juices squeezed
from tomatoes with different storage time by PCA.
Nisha. R
40. 6/21/2017 Nisha. R 40
This work successfully employed an e-nose combined with
chemometrics to trace quality indices (storage time, pH,
SSC, VC and firmness) of cherry tomatoes that were
squeezed for juice consumption. The proposed semi-
supervised classifier – Cluster-then- Label based on
spectral clustering and majority voting – was found more
reliable and robust than the supervised approaches, and it
outperformed the four supervised approaches
42. Buratti, S., Benedetti, S., Scampicchio, M., Pangerod, E., 2004.
Characterization and classification of Italian Barbera wines
by using an electronic nose and an amperometric electronic
tongue. Anal. Chim. Acta 525 (1), 133–139.
Cerrato Oliveros, M.C., Perez Pavon, J.L., Garcı´a Pinto, C.,
Fernandez Laespada, M.E.,
Moreno Cordero, B., Forina, M., 2002. Electronic nose based
on metal oxide semiconductor sensors as a fast alternative
for the detection of adulteration of virgin olive oils. Anal.
Chim. Acta 459 (2), 219–228.
Chang, C.-C., Lin, C.-J., 2011. LIBSVM: a library for support
vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2
(3), 27.
K. Arshak, E. Moore, G. M. Lyons, J. Harris, and S. Clifford,
Sens. Rev. 24, 181 (2004).
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43. Pardo, M., Sberveglieri, G., 2008. Random forests and
nearest shrunken centroids for the classification of
sensor array data. Sens. Actuat. B: Chem. 131 (1), 93–99.
Reinhard, H., Sager, F., Zoller, O., 2008. Citrus juice
classification by SPME-GC-MS and electronic nose
measurements. LWT-Food Sci. Technol. 41 (10), 1906–
1912.
Scott, S.M., James, D., Ali, Z., 2006. Data analysis for
electronic nose systems Microchim. Acta 156 (3–4),
183–207.
• Dymerski, Chmiel, and Wardencki W., 2011 Invited
Review Article: An odor-sensing system powerful
technique for foodstuff studies.
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