Nabarun Bhattacharyya

Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks

Tea is an extensively consumed beverage worldwide with an expanding market. The major quality attributes of tea are flavor, aroma, color, and strength. Out of these, flavor and aroma are the most important attributes. Human experts called ldquotea tastersrdquo conventionally evaluate tea quality, and they usually assign scores to samples of tea that are under evaluation on a scale of 1 to 10, depending on the flavor, the aroma, and the taste of the sample. This paper presents a study where, first, the selection of appropriate sensors was carried out based on sensitivity with the major aroma-producing chemicals of black tea. Then, this sensor array was exposed to black tea samples that were collected from the tea gardens in India, and the computational model has been developed based on artificial neural network methods to correlate the measurements with the tea tasters scores. With unknown tea samples, encouraging results have been obtained with a more than 90% classification rate.

Application of electronic nose for industrial odors and gaseous emissions measurement and monitoring--An overview.

The present review evaluates the key modules of the electronic nose, a biomimetic system, with specific examples of applications to industrial emissions monitoring and measurement. Regulations concerning the odor control are becoming very strict, due to ever mounting environmental pollution and its subsequent consequences and it is advantageous to employ real time measurement system. In this perspective, systems like the electronic nose are an improved substitute for assessing the complex industrial emissions over other analytical techniques (odorant concentration measurement) and olfactometry (odor concentration measurement). Compared to tools like gas chromatography, electronic nose systems are easy to develop, are non-destructive and useful for both laboratory and on field purposes. Although there has been immense development of more sensitive and selective sensor arrays and advanced data mining techniques, there have been limited reports on the application of electronic nose for the measurement of industrial emissions. The current study sheds light on the practical applicability of electronic nose for the effective industrial odor and gaseous emissions measurement. The applications categorization is based on gaseous pollutants released from the industries. Calibration and calibration transfer methodologies have been discussed to enhance the applicability of electronic nose system. Further, industrial gas grab sampling technique is reviewed. Lastly, the electronic mucosa system, which has the ability to overcome the flaws of electronic nose system, has been examined. The review ends with the concluding remarks describing the pros and cons of artificial olfaction technique for the industrial applications.

Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea.

In an electronic tongue, preprocessing on raw data precedes pattern analysis and choice of the appropriate preprocessing technique is crucial for the performance of the pattern classifier. While attempting to classify different grades of black tea using a voltammetric electronic tongue, different preprocessing techniques have been explored and a comparison of their performances is presented in this paper. The preprocessing techniques are compared first by a quantitative measurement of separability followed by principle component analysis; and then two different supervised pattern recognition models based on neural networks are used to evaluate the performance of the preprocessing techniques.

Towards Versatile Electronic Nose Pattern Classifier for Black Tea Quality Evaluation: An Incremental Fuzzy Approach

Commonly used classification algorithms are not capable of incremental learning. When a new pattern is presented to such a computational model, it can either classify the unknown pattern based on its legacy training or declare the pattern as an outlier if such a provision is built into the associated algorithm. In the case of the pattern being an outlier to the existing training model, it is desirable that the same could be seamlessly included in the training model with appropriate class labels so that a universal computational model may be evolved incrementally. To this end, classifiers having the incremental-learning ability can be of great benefit by automatically including the newly presented patterns in the training data set without affecting class integrity of the previously trained system. In the present treatise, an incremental-learning fuzzy model for classification of black tea using electronic nose measurement is proposed. For application in black tea grade discrimination, an attempt has been made to correlate the multisensor aroma pattern of electronic nose with sensory panel (tea tasters) evaluation. However, this problem is associated with 2-D complexities. On one hand, the aroma of tea depends on the agroclimatic condition of a particular location, the specific season of flush, and the clonal variation for the tea plant. On the other hand, the sensory evaluation is completely human dependent that often suffers from subjectivity and nonrepeatability. In our pursuit of developing a universal computational model capable of objectively assigning tea-taster-like scores to tea samples under test, it has been felt that an incremental approach could be extremely beneficial for electronic-nose-based tea quality estimation. To this end, the proposed incremental-learning fuzzy model promises to be a versatile pattern classification algorithm for black tea grade discrimination using electronic nose. The algorithm has been tested in some tea gardens of northeast India, and encouraging results have been obtained.

Calibration transfer between electronic nose systems for rapid In situ measurement of pulp and paper industry emissions

Electronic nose systems when deployed in network mesh can effectively provide a low budget and onsite solution for the industrial obnoxious gaseous measurement. For accurate and identical prediction capability by all the electronic nose systems, a reliable calibration transfer model needs to be implemented in order to overcome the inherent sensor array variability. In this work, robust regression (RR) is used for calibration transfer between two electronic nose systems using a Box-Behnken (BB) design. Out of the two electronic nose systems, one was trained using industrial gas samples by four artificial neural network models, for the measurement of obnoxious odours emitted from pulp and paper industries. The emissions constitute mainly of hydrogen sulphide (H2S), methyl mercaptan (MM), dimethyl sulphide (DMS) and dimethyl disulphide (DMDS) in different proportions. A Box-Behnken design consisting of 27 experiment sets based on synthetic gas combinations of H2S, MM, DMS and DMDS, were conducted for calibration transfer between two identical electronic nose systems. Identical sensors on both the systems were mapped and the prediction models developed using ANN were then transferred to the second system using BB-RR methodology. The results showed successful transmission of prediction models developed for one system to other system, with the mean absolute error between the actual and predicted concentration of analytes in mg L(-1) after calibration transfer (on second system) being 0.076, 0.1801, 0.0329, 0.427 for DMS, DMDS, MM, H2S respectively.

Optimization of Sensor Array in Electronic Nose: A Rough Set-Based Approach

In an electronic nose, the most important component is the sensor array and the classification accuracy of an electronic nose that depends significantly upon the choice of the sensors in the array. While deploying an electronic nose for a specific application, it is observed that some of the sensors in the array may not be required and only a subset of the sensor array contributes to the decision. Thus, the number of sensors used in the electronic nose may be minimized for a particular application without affecting the classification accuracy. In many cases, the sensor array produces an imprecise, incomplete, redundant, and inconsistent dataset and thus the classification accuracy degrades due to these redundant sensors. The rough set theory is a mathematical tool capable of selecting the most relevant and nonredundant feature from such datasets. In this paper, the notion of rough set theory is utilized for pattern classification in an electronic nose with black tea samples and at the same time optimization of the sensor set is carried out.

Incremental PNN classifier for a versatile electronic nose

Due to robustness of the probabilistic neural network (PNN) architecture, it has been widely used for pattern classification tasks. Commonly used PNN algorithms are not capable of incremental learning. The classifiers having the incremental learning ability can be of great benefit by automatically including the newly presented patterns in the training dataset without affecting class integrity of the previously trained classifier. This signifies that, the incremental classifiers have the ability to accommodate new classes and new knowledge within an already trained model. Under the present study, an electronic nose anchored aroma characterization model based on PNN classification strategy has been developed whereby the sensor array outputs of the electronic nose can be co-related to the sensory panel (tea tasters) quality scores for black tea. The whole study has been done in few tea gardens in north-east India. In pursuit of development of optimal strategy for data collection from dispersed locations followed by dynamically augmenting the training data corpus of the already trained PNN model, the incremental leaning mechanism has bee suitably grafted to the PNN model to have efficient co-relation of electronic nose signature with tea tasterspsila scores. The incremental PNN classifier promises to be a versatile pattern classification algorithm for black tea grade discrimination using electronic nose system.

Monitoring the Fermentation Process and Detection of Optimum Fermentation Time of Black Tea Using an Electronic Tongue

This paper presents a new methodology to monitor the fermentation process and detect the optimum fermentation time of crush tear curl black tea with a voltammetric electronic tongue. An electronic tongue with an array of five noble metal working electrodes has been developed for this purpose. A suitable large amplitude pulse voltammetric waveform has been employed for probing the chemical changes in tea samples under fermentation. Good correlation between the electronic tongue responses and the biochemical changes has been obtained by principal component analysis (PCA) during various stages of fermentation. The electronic tongue fermentation profile has been derived from PCA analysis and it is observed that such a profile enables detection of optimum fermentation times. Finally, a model based on partial least squares regression technique has been developed for real-time indication of fermentation level. The optimum fermentation times observed from the electronic tongue fermentation profiles derived from PCA and partial least squares regression correlate by a factor of 0.97 and 0.96, respectively, with the reference values obtained from an ultraviolet-visible spectrophotometer-based instrumental analysis.

Aroma characterization of orthodox black tea with electronic nose

Black tea quality is a very complex phenomenon. There are almost two hundred varieties of bio-chemical compounds, both volatile and nonvolatile present in tea and each of these compounds contribute to tea quality (B. Banerjee, 1996), The major quality attributes of tea are flavour, aroma, colour and strength. Acceptance by consumers and price realized depend on these attributes (S.Y. Dheodhar et al.,). Out of these, aroma is the most important of the attributes and in common parlance, aroma means smell of the tea. Characterization of aroma of tea has been a challenge for tea scientists for long. Efforts have been made towards this through chemical analysis and instrumental studies through gas chromatography (GC) and high profile liquid chromatography (HPLC) techniques. Research and studies have been reported with success for quality characterization of food and beverages using electronic nose (T.C. Pearce et al., 2003). This paper reports a study and results on applicability of electronic nose for aroma characterization of orthodox black tea. Six varieties of orthodox tea samples were tested using Alpha MOS 2000 Electronic Nose and data obtained from the experimental setup have been successfully classified using principal component analysis (PCA) and back-propagation multilayer perceptron model.

Electronic Nose and Electronic Tongue

Human beings have five senses, namely, vision, hearing, touch, smell and taste. The sensors for vision, hearing and touch have been developed for several years. The need for sensors capable of mimicking the senses of smell and taste have been felt only recently in food industry, environmental monitoring and several industrial applications. In the ever-widening horizon of frontier research in the field of electronics and advanced computing, emergence of electronic nose (E-Nose) and electronic tongue (E-Tongue) have been drawing attention of scientists and technologists for more than a decade. By intelligent integration of multitudes of technologies like chemometrics, microelectronics and advanced soft computing, human olfaction has been successfully mimicked by such new techniques called machine olfaction (Pearce et al. 2002). But the very essence of such research and development efforts has centered on development of customized electronic nose and electronic tongue solutions specific to individual applications. In fact, research trends as of date clearly points to the fact that a machine olfaction system as versatile, universal and broadband as human nose and human tongue may not be feasible in the decades to come. But application specific solutions may definitely be demonstrated and commercialized by modulation in sensor design and fine-tuning the soft computing solutions. This chapter deals with theory, developments of E-Nose and E-Tongue technology and their applications. Also a succinct account of future trends of R&D efforts in this field with an objective of establishing co-relation between machine olfaction and human perception has been included.