Arun Jana

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.

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.

Classification of aromatic and non-aromatic rice using electronic nose and artificial neural network

Classification of rice is carried out by human experts in the industry and apart from other attributes like grain size, elongation ratio, aroma plays a significant role in the classification process. On the basis of aroma, the rice samples are manually categorized as strongly aromatic, moderately aromatic, slightly aromatic and non aromatic. Instrumental evaluation of aroma of rice is much needed in the industry and in this paper, we describe an electronic nose instrument, that has been developed for aroma characterization of rice. Artificial neural network is used for the pattern classification on data obtained from the sensor array of the electronic nose. With unknown rice samples, aroma based classification accuracy has been observed to be more than 80%.

Smell Peak Prediction During Black Tea Fermentation Process Using Time-Delay Neural Network on Electronic Nose Data

Fermentation process in black tea manufacturing plays the key role in determining the quality of finished tea. During this process, a complex chain of biochemical changes occurs and the process should be terminated once the optimum fermentation point is reached. Present day practice for detection of optimum fermentation point is purely subjective, and is carried out by experienced industry personnel. Even though chemical methods are available, but they are expensive, time-consuming and offline. A study has been made on real time smell monitoring of black tea during fermentation process using electronic nose and is reported in this paper. Time-delay neural network (TDNN) architecture has been used on time series data obtained from electronic nose for smell peak prediction during the fermentation process. The online predicted result using TDNN seems very promising to detect the optimum fermentation time for black tea manufacturing process

FRAGRANCE MEASUREMENT OF SCENTED RICE USING ELECTRONIC NOSE

This article describes about an instrument and method for aroma based quality detection of Basmati and other aromatic rice varieties. It comprises few modules such as odour delivery module, sniffing module, water bath module and computing module. Odour handling module helps to deliver odour to the sensor array; a sniffing unit comprising a sensor array module that includes a eight number of metal oxide semiconductor sensors assembled on a printed circuit board, said printed circuit board fitted into a sensor chamber; a water bath module for preparing rice sample, said water bath module including a heater attachment to facilitate cooking; a computing module to quantify the aroma data acquired by sensors; data acquisition module etc. Principal Component Analysis (PCA) implemented for clustering the data sets acquired from sensor array. Also data generated from sensor array was fed to Probabilistic Neural Network (PNN), Back-propagation Multilayer Perceptron (BPMLP) and Linear Discriminant Analysis (LDA) for identification of different rice varieties. Finally, for aroma quantifying, pure-quadratic response surface methodology model used with mean square error (MSE) 0.0028. Index term: Aromatic rice, Sensor, Principal Component Analysis, Probabilistic Neural Network, Back-propagation Multilayer Perceptron, Linear Discriminant Analysis, response surface methodology. Arun Jana, Nabarun Bhattacharyya, Rajib Bandyopadhyay, Bipan Tudu, Subhankar Mukherjee, Devdulal Ghosh, Jayanta Kumar Roy, FRAGRANCE MEASUREMENT OF SCENTED RICE USING ELECTRONIC NOSE 1731

P2.9.11 Monitoring of Obnoxious Odorants Generated from Pulp and Paper Industry using Electronic Nose

The Odorous emissions generated from pulp and paper industry has been the cause of nuisance since the inception of the industry. Reduced Sulphur Compounds generated from these mills have several health implications and the ever increasing population accompanied by other socio-economic factors leading to the development of habitant in the proximity of these mills is making it mandatory to monitor the emissions generated. Present day available analytical techniques do not depict clear picture of odor perceived, is time consuming and expensive. The objective of the study was to develop an electronic nose for monitoring major reduced sulphurous compounds emitted during pulping process from paper mills. The gas samples collected from industry were tested with the e-nose and an odor index was generated corresponding to varying concentration of compounds.