Electronic nose applications in environmental monitoring are nowadays of great interest, because of the instruments' proven capability of recognizing and discriminating between a variety of different gases and odors using just a small number of sensors. Such applications in the environmental field include analysis of parameters relating to environmental quality, process control, and verification of efficiency of odor control systems. This article reviews the findings of recent scientific studies in this field, with particular focus on the abovementioned applications. In general, these studies prove that electronic noses are mostly suitable for the different applications reported, especially if the instruments are specifically developed and fine-tuned. As a general rule, literature studies also discuss the critical aspects connected with the different possible uses, as well as research regarding the development of effective solutions. However, currently the main limit to the diffusion of electronic noses as environmental monitoring tools is their complexity and the lack of specific regulation for their standardization, as their use entails a large number of degrees of freedom, regarding for instance the training and the data processing procedures.

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Electronic Noses for Environmental Monitoring Applications

Instrumental testing of tea by combining the responses of electronic nose and tongue

Consumers expect water supply companies to deliver safe drinking water that meets both health quality standards and aesthetic requirements such as colour, turbidity, taste and odour. Current water quality assessment methods of these parameters, which form the basis for sound water resources management, are mainly laboratory based, require fresh supply of chemicals, trained staff and are time consuming. Real-time water quality monitoring is essential for National and International Health and Safety, as it can significantly reduce the level of damage and also the cost to remedy the problem. This paper critically analyses both commercially available and state-of-the-art research methods and devices suitable for real-time wastewater quality monitoring and suggests further developments in this area. In particular, the focus is made on the monitoring of nitrates and phosphates in wastewater and a novel microwave based method for instantaneous water quality assessment is suggested.

/pdf/monitoring-of-nitrates-and-phosphates-in-wastewater-current-1v58v7yuiw.pdf

Monitoring of nitrates and phosphates in wastewater: current technologies and further challenges

Black tea classification employing feature fusion of E-Nose and E-Tongue responses

Discrimination of mineral waters by electronic tongue, sensory evaluation and chemical analysis

This paper proposes the development of a new approach for water sample authentication, in real life, using a pulse-voltametry-method-based electronic tongue instrumentation system. The system is developed as a parallel combination of several neural network classifiers, each dedicated to authenticate a specific category of water sample, and can be extended for more categories of water sample authentication. The system employs a slantlet-transform (ST)-based feature extraction module and two popular variants of neural networks for classification. The proposed system hybridizes ST with two variants of backpropagation-neural-network-based binary classifiers to develop an automated authentication tool. ST is regarded as an improved version of orthogonal discrete wavelet transform that can provide improved time localization with simultaneous achievement of shorter supports for the filters. This proposed system, implemented in a laboratory environment for various water samples available in India, showed encouraging average authentication percentage accuracy, on the order of over 80% for most water categories and even producing accuracy results exceeding 90%, for several categories.

Electronic Tongue System for Water Sample Authentication: A Slantlet-Transform-Based Approach

This paper proposes the development of water sample classification and authentication, in real life which is based on machine learning algorithms. The proposed techniques used experimental measurements from a pulse voltametry method which is based on an electronic tongue (E-tongue) instrumentation system with silver and platinum electrodes. E-tongue include arrays of solid state ion sensors, transducers even of different types, data collectors and data analysis tools, all oriented to the classification of liquid samples and authentication of unknown liquid samples. The time series signal and the corresponding raw data represent the measurement from a multi-sensor system. The E-tongue system, implemented in a laboratory environment for 6 numbers of different ISI (Bureau of Indian standard) certified water samples (Aquafina, Bisleri, Kingfisher, Oasis, Dolphin, and McDowell) was the data source for developing two types of machine learning algorithms like classification and regression. A water data set consisting of 6 numbers of sample classes containing 4402 numbers of features were considered. A PCA (principal component analysis) based classification and authentication tool was developed in this study as the machine learning component of the E-tongue system. A proposed partial least squares (PLS) based classifier, which was dedicated as well; to authenticate a specific category of water sample evolved out as an integral part of the E-tongue instrumentation system. The developed PCA and PLS based E-tongue system emancipated an overall encouraging authentication percentage accuracy with their excellent performances for the aforesaid categories of water samples.

Classification and authentication of unknown water samples using machine learning algorithms.

This paper describes the use of voltammetric electronic tongue and pattern recognition techniques used as a biomimetic system for classifying eight varieties of Indian juices. The responses from the voltammetric electronic tongue were recorded for all the samples of eight varieties of juices. In order to classify the juices into different classes, different pattern recognition techniques were used. The data being large as is the case with voltammetric signals is analyzed using Principal Component Analysis to transform the data into a low dimensional feature vector space involving Principal components thereby realizing dimensionality reduction of the responses followed by the selection of relevant principal components. Later, these relevant principal components are used as inputs to a distance based classifier for classifying the juices and the classification accuracy attained is 100%. The results demonstrate the feasibility of voltammetric electronic tongue combined with pattern recognition techniques for discriminating and classifying the juice samples.

http://ieeexplore.ieee.org/iel5/6428838/6461638/06461740.pdf

Biomimetic classification of juices

This chapter deals with the design, fabrication, packaging, and instrumentation of ISFET and EGFET devices as pH-sensing platforms for chemical and biological sensors. The ISFETs have been developed in two geometrical layouts: a twin-FET configuration on a single chip comprising ISFET and a reference field-effect transistor (REFET); and a single ISFET with linear gate geometry. Packaging challenges have been successfully overcome by thick film alumina and low-temperature cofired ceramic (LTCC) technologies. Characterization studies have been performed on ISFETs. An ISFET-based pH meter, consisting of a sensor probe, a signal conditioning circuit, microcontroller, and display has been developed. The study has been carried out on a pH-sensitive extended gate electrode with a Ta2O5 sensing film by connecting it to a standard MOSFET gate to develop an EGFET device.

Design and Development of Ion-Sensitive Field-Effect Transistor and Extended-Gate Field-Effect Transistor Platforms for Chemical and Biological Sensors

This paper deals with a data reduction technique for pulse voltammetric signals of electronic tongue for discrimination of different types of liquids. The electronic tongue is a system which can determine the fingerprint of a liquid sample by an array of non-specific/semi-specific electrodes/sensors coupled to a pattern recognition system. The data acquired from electronic tongue is very large in size, in particular, when multiple electrodes are used. A new method or technique for reducing the size of the variables in a voltammetric signal is proposed. The method is based on segmentation of the voltammetric signal into significant and insignificant segments based on the statistical measure, variance, in the samples under investigation for classifying into different classes. The method is simple, fast and good enough to reduce the size of the signal without much loss of significant information needed for classifying the samples. The proposed method is described in the present paper with its application to two different sets of data obtained from the electronic tongue where in data reduction is achieved to a greater extent. Further, principal component analysis (PCA) was applied on the data before and after reduction. The results obtained validate the technique proposed in the present paper as a relatively good option for reducing the dimension or size of the data matrix for voltammetric signals of electronic tongue.

P. C. Panchariya

Papers

Electronic Tongue System for Water Sample Authentication: A Slantlet-Transform-Based Approach

Classification and authentication of unknown water samples using machine learning algorithms.

Biomimetic classification of juices

Design and Development of Ion-Sensitive Field-Effect Transistor and Extended-Gate Field-Effect Transistor Platforms for Chemical and Biological Sensors

New data reduction algorithm for voltammetric signals of electronic tongue for discrimination of liquids