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Tea industries enjoy a significant position in the socio-economic ladder for any demographics, especially in India who is the largest producer as well as consumer of the agro-product. While tea ranks only next to water in the pedigree of globally consumed beverages, the imperative fermentation stage in the processing of tea leaves is conventionally monitored through olfactory perception of tea tasters. Recent advances in the field of machine olfaction have witnessed the advent of electronic nose prototypes, which provide a scientific validation to the organoleptic estimations disseminated by the tasters. However, fermentation is a continuous process requiring constant monitoring whose successful completion relies heavily on identification of distinct aroma peaks emanated at optimum instants. Since the fermentation process is integral to the final quality, it is deemed beneficial if the optimum fermentation period can be predicted at an earlier stage. Such preemptive information can mitigate constant monitoring requirements and momentary concentration lapses. Recognizing the time series nature of the data generated during the fermentation process with an electronic nose prototype, we have implemented a recurrent Elman network to predict the optimum fermentation period for different black tea samples. The results showed that the prescribed network could predict the optimum period with confidence at the halfway of the process. The minimal error between the predicted and the actual fermentation period at the halfway point suggests that the proposed model can well be integrated with an electronic nose dedicated for monitoring the fermentation process.

The effect of aging on quality of jasmine (Jasminum Sambac) concrete in terms of aroma was studied with the intention of estimating the shelf life of this highly priced export oriented commodity. A handheld electronic nose was developed and customized to find out aroma intensity of fresh and aged commercial concrete samples. The same fresh and aged samples were also analyzed with the help of a gas chromatography–mass spectrometry (GC–MS). Between first and final experiment, the samples were kept in air-tight containers. The electronic nose showed very high sensitivity toward the concrete’s aroma molecules and could detect aroma volatiles from concrete samples at a minimum sample weight of 0.05 g. The e-nose also showed the decrease in aroma intensity in the samples with aging. The GC-MS results established a strong correlation with the e-nose observations. The aroma intensity of concrete was observed to be decreased almost linearly over a period of one year. Electronic nose has been found to be a potent tool for rapid prediction of concrete age.

Biochemical means of tea quality evaluation is quite accurate, but it requires very costly instruments and takes a long time to conduct the experiment. Researches show that thearubigin (TR) and theaflavin (TF) are the two most important biochemical compounds present in tea liquor on which liquor characteristics of black CTC tea depends. Consequently, this fact may be the basis of determining tea quality by assessment of TR and TF via electronic tongue (ET) response. This technique is free from subjective factors. In literature, there are only two research works using this technique so more research work is required to address the problem. This chapter proposes a modeling technique of ET response using sparse decomposition technique to estimate TR and TF content in a given tea sample. For each tea sample, sparse model coefficients obtained from the ET response is considered as a characteristic attribute of it. Experimental results using dissimilar regression models show high prediction accuracies which justify the efficacy of the proposed method.

Physical and biochemical attributes are commonly used for characterization of rice. The physical attributes are related to the quantification of size, shape, colour and texture of the rice grains. Biochemical attributes are assessed from cooking and eating characteristics of rice and are termed like alkali spreading value (ASV), amylose content (AC), gel consistency (GC), grain elongation etc. Estimation of biochemical attributes are often time consuming and require meticulous effort for sample preparation, storage and manual measurement. The gelatinization temperature (GT) is related to Alkali spreading value of rice and is partly associated with the amylose content of the starch. GT has a negative correlation with cooking temperature of rice. In this paper image analysis technique has been proposed for discrimination of rice. A portable flat bed scanner has been used as the imaging device and image analysis software has been developed to measure the rate of dispersion during ASV testing. This machine vision technique is a faster and effective way to determine the ASV. The results obtained are promising towards this new approach for objective estimation of ASV.

Methyl salicylate plays an important role in the constitution of black tea aroma. This paper presents a quartz crystal microbalance (QCM) sensor to detect methyl salicylate from the black tea aroma. The sensor is developed using maltodextrin (MDEX). Methyl salicylate is an organic ester, naturally produced by many species of plants. It is used as a fragrance, in foods and beverages, and in ointments. Extensive experiments have been carried out with MDEX-coated QCM sensor and it is observed that the sensor offers better sensitivity for the methyl salicylate in comparison with other important constituents of black tea like linalool, geraniol, linalool oxide, and trans-2-hexenal. Experiments with 32 Darjeeling orthodox black tea samples have been carried out with the developed sensor, which offers 0.982 correlation between the responses of the developed sensor and the concentrations obtained through the reference instrumental technique, i.e., gas chromatography mass spectrometry.

In black tea manufacturing, the fermentation process plays a significant role to determine the quality of finished tea. During this process, complex chain of biochemical reactions takes place and green tea leaves transform from grassy to sweet floral aroma. Monitoring and detection of optimum fermentation time are necessary, as both under and over fermented tea produce inferior grades of tea. This paper presents a method of real-time monitoring to detect the optimum fermentation time of black tea using an electronic nose consisting of eight quartz crystal microbalance (QCM) sensors. The sensor is coated with glucose derivative coating materials viz. maltose (MAL), maltodextrin (MDEX), β-cyclodextrin (β-CD), d -glucose ( d -GLU) and polyethylene glycols (PEG) with different molecular weights, 200, 1500, 4000, and 6000. Optimum fermentation times were determined for twelve black tea samples, and the results show good agreement with the estimations of the ultra-violet-visible (UV) spectrophotometer based reference method.

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.

A voltammetric electronic tongue (E-tongue) has been used to detect the optimum fermentation time (OFT) of crush tear curl black tea. An E-tongue with an array of five noble metal working electrodes has been developed for this purpose. A suitable large amplitude pulse voltammetric waveform is used and a simple feature extraction technique based on the charge activity of working electrodes is proposed. The principal component analysis reveals a significant correlation between the E-tongue responses and the biochemical changes during various stages of fermentation. Principal component regression models are developed to assess the effectiveness of the features derived from E-tongue response to model the temporal variations of theaflavins, a key quality compound of tea, produced during the fermentation process. The OFTs detected by the E-tongue correlate by a factor of 0.97 with the estimates obtained from an ultraviolet-visible spectrophotometer-based reference method.

Linalool is one of the most important volatile constituent that contributes significantly in the aroma of brewed, dry or extracted tea. Thus it is very essential to sense the linalool content in orthodox black tea as that may lead to rapid quality estimation. Quartz crystal microbalance (QCM) type gas sensors are very sensitive and are increasingly being used for many applications. It can be easily fabricated, has less response time and fast recovery characteristics and can be coated with a variety of materials to obtain different sensitivities and selectivities. In the present work, a QCM sensor has been developed with polyethylene glycol (PEG) to detect linalool gas in black tea. Extensive experiments have been carried out with various concentrations of linalool gas and the sensitivity, repeatability and reproducibility of the developed sensors were determined. The sensors were observed to be sensitive and selective to linalool. The developed sensors were exposed to the orthodox black tea aroma and significant correlation is obtained with gas chromatography mass spectrometry (GCMS) estimations.

Automation for tea quality estimation is a challenging task and with the advent of electronic nose and electronic tongue systems this problem becomes quite addressable by instrumental means. Electronic nose judges tea sample based on aroma of the sample and based on taste tea quality can be classified using electronic tongue. For estimation of flavour of tea rather the overall quality of tea can be estimated if these two sensory responses can be fused. In this work, we have attempted to fuse these two sensory features using fuzzy fusion technique. A general fuzzy rule base is developed from the transient datasets obtained from electronic nose and tongue separately. The fuzzy system model can give accurate prediction with much simpler model than neural network. But both the system has certain advantages. In order to develop better classifier fuzzy neural network (FNN) model is also developed. Moreover the model works with transient responses and no data compression technique is employed. It is found that the combined sensor signature regarding tea quality estimation is quiet improved compared to individual sensor systems for all three classifiers and among these FNN is the best suited model for tea classification.

This paper presents a neuro-fuzzy classification technique using electronic nose, electronic tongue and the fused response of electronic nose and electronic tongue for the evaluation of black tea quality. In the tea industries an automated, neutral and low cost instrumental system to determine the overall tea quality is in great requirement. A general fuzzy rule based and neural network model can produces accurate predictions. But both models have some weakness. In this pursuit, Pseudo outer-product based fuzzy neural, a kind of fuzzy neural network classifying system has been attempted to classify tea grades. Results show that above model can classify in a better way compared to other models.

In the tea industry, experienced tea tasters are employed for evaluation of tea quality and gradation of tea is done on the basis of their scores. This subjective method of assessment has numerous problems like inaccuracy and non-repeatability. Electronic nose and electronic tongue systems are recently being used for measurement of odor and taste of tea samples. As the senses of smell and taste are not independent, and both are interacting, the measured data from the individual systems are combined in this paper for improved estimation of black tea quality. It is found that for the combined system, both the clustering and classification rates improve when compared to the individual systems. With radial basis function neural network, the classification rate increases up to 93%, whereas with the independent systems, the classification rate obtained is 83–84% with electronic nose and 85–86% with electronic tongue.

Biochemical components like theaflavins (TF) play very important role in the quality of finished CTC (cut, torn, and curled) variety of tea. TF are known to provide characteristic astringency to the taste of finished CTC tea. The quality indicators like brightness, briskness, strength, color and overall quality of tea liquor are also due to the amount of TF present. A positive correlation is normally observed between the amount of TF and the quality scores of finished tea. Biochemical tests that yield the percentage of TF are often time consuming, require meticulous effort of sample preparation, storage and measurement. This paper proposes an alternative approach of quality evaluation of CTC tea by predicting the amount of TF that may be present in a given tea sample, using a voltammetric electronic tongue.

Potential of an “electronic tongue” multisensor system in identification of various tea samples and in quantitative analysis of separate tea components was studied. As a reference method for quantitative analysis of antioxidants of the polyphenolic type served micellar electrokinetic chromatography with a UV detector. The contents of epicatechin gallate, epigallocatechin gallate, epicatechin, epigallocatechin, gallic acid, gallocatechin gallate, and caffeine alkaloid in various tea samples were quantitatively estimated.

Tea quality assessment is a difficult task because of the presence of innumerable compounds and their diverse contribution to tea quality. As a result, instrumental evaluation of tea quality is not practiced in the industry, and tea samples are assessed by experienced tea tasters. There had been a very few reports where an electronic tongue has been used for the discrimination of taste of tea samples. In this paper, a voltammetric electronic tongue instrument is described, which can declare tea-taster-like scores for black tea. The electronic tongue is based on the principle of pulse voltammetry and consists of an array of five working electrodes along with a counter and a reference electrode. The five working electrodes are of gold, iridium, palladium, platinum, and rhodium. The voltage equivalent of the output current from between the working electrode and the counterelectrode generated out of the tea liquor when excited with pulse voltage between the working electrode and the reference electrode has been considered for data analysis. First, the sampled data have been compressed using discrete wavelet transform (DWT) and are then processed using principal component analysis (PCA) and linear discriminant analysis (LDA) for visualization of underlying clusters. Finally, different pattern recognition models based on neural networks are investigated to carry out a correlation study with the tea tasters' score of five different grades of black tea samples obtained from a tea garden in India. The efficacy of the classifier has been established using tenfold cross-validation methods.

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.

Black Tea is conventionally tested by human sensory panel called “Tea Tasters”, who assign quality scores to different teas. Electronic nose-based studies aimed at co-relation of sensor array output signature with Tea Tasters’ scores have already been accomplished by the authors with good accuracy [N. Bhattacharyya, R. Bandyopadhyay, M. Bhuyan, B. Tudu, D. Ghosh, A. Jana, Electronic nose for black tea classification and correlation of measurements with “Tea Taster” marks, IEEE Trans. Instrum. Meas. 57 (2008) 1313–1321], where a number of conventional neural network topologies have been explored. One of the principal problems encountered in the above studies is collection of tea samples as tea industries in India are spread over dispersed locations and quality of tea varies considerably on agro-climatic condition, type of plantation, season of flush and method of manufacturing. As a result, the entire dataset is not available at a time and training the conventional neural network models becomes difficult. In this regard, 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 system. In the presented paper, the radial basis function (RBF) neural network with the incremental learning feature is used in the pattern recognition algorithm for black tea aroma classification with electronic nose.

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 taster's scores. With unknown tea samples, encouraging results have been obtained with a more than 90% classification rate.

Quality assessment of tea is an extremely difficult task and until now has been carried out by experienced tasters. There are many important quality attributes of tea—aroma, taste, color, and size and, out of these attributes, aroma plays a significant role in determining the price of tea. Here we will focus only on this important attribute of tea—the aroma and the applicability of an electronic nose for its assessment. There have been several attempts for instrumental assessment of tea aroma, but being a natural product with several hundred compounds contributing to the pleasant aroma, there are several challenges to be overcome for the widespread use of such instruments. This chapter presents the research reports on experimentation with using electronic nose to determine tea aroma and a case study.

Black tea fermentation is essentially an oxidation process. After the plucked tea leaves are treated by series of processes called withering (removal of moisture by air flow), pre-conditioning and CTC (essentially maceration and cutting of leaves), the leaves are subjected to the process of fermentation by exposing them to air by laying the cut tea leaves on floor, trough or moving conveyor under controlled temperature, humidity and air-flow conditions. During this process, the leaves change colour from green to coppery brown and the grassy smell gets transformed to floral smell. It is critical that the leaves be allowed to ferment only up to the desired limit and both under and over fermentation result in deteriorated quality of black tea. Out of the two detectable parameters (colour and smell), smell is very important since a strong, very specific fragrance emanates from the leaves once leaves are optimally fermented. A new electronic nose-based approach for monitoring of tea aroma during fermentation is proposed. Two methods namely the 2-Norm method (2NM) and the Mahalanobis distance method (MDM) were tested and the results were correlated with the results of colorimetric tests and human expert evaluation.

Fermentation is an extremely crucial process in black tea manufacturing and is primarily responsible in deciding the final quality of finished tea. During this process, green tea changes its colour from green to coppery brown and grassy smell gets transformed to floral smell. A complex chain of biochemical reactions takes place during the fermentation process and once such changes reach their optimum point, the process should be stopped. The quality of the final product hinges to a large extent on the decision to end fermentation at right time, and either under-fermentation or over-fermentation leads to deterioration of finished tea quality. This paper presents a study on electronic nose-based monitoring of volatile emission pattern during black tea fermentation process and detection of the optimum fermentation time on the basis of peaks in the sensor outputs.

Electronic nose has wide application in discriminations among food and beverage samples. Electronic nose is an array of gas sensor classifies samples based on their aroma profile. In this work this artificial sensory system is used to classify black tea using the features extracted from sensor response. Gaussian windowing function called `kernel' are used to extract information from the transient response and those are optimized by GA. The number of features being considered for classification was reduced considerably as well as classification performance is much improved than classification by directly using the sensor responses.

As of today, aroma of rice is measured by an expert sensory panel and they assign scores like ‘+’, ‘++’, ‘+++’ and ‘NA’ for mild, medium, strong and non aromatic varieties of rice respectively. This method of human panel testing is very subjective with numerous problems like inaccuracy, non-repeatability and it is laborious and time consuming also. On the other hand, the analytical instruments, which are used for this purpose are prohibitively expensive and are available in the laboratories only. It is in this pursuit, an electronic nose with an array of gas sensors has been developed for aroma measurement of rice. This user friendly and low cost electronic nose may be extremely useful for rice scientists, researchers and exporters to determine the aroma of aromatic rice. In this paper, we describe the experimental setup and the regression model for classification of rice samples. With unknown rice samples, aroma based classification accuracy by multi-sensor electronic nose using the regression model, has been found to be more than 80%.

Tea is one of the most widely consumed beverages all over the world due to its delicate taste. It is, therefore, essential to establish a simple and reliable analytical tool for determination (or, discrimination) of tea quality. We have taken an attempt to discriminate the black tea of different qualities/grades by means of polymer membrane electrode based potentiometric taste sensor. Different types of phosphorylated and crosslinked polymer membranes were used to discriminate different grades of black tea by measuring the tastes in terms of membrane potential.

An electronic nose with an array of quartz crystal microbalance (QCM) sensors has been developed to discriminate among different samples of Indian black tea varieties. The important volatile organic components (VOC) responsible for aroma of tea have been considered and the corresponding sensing materials have been identified. Five AT-cut 10MHz Quartz crystal blanks coated with different sensing materials have been used to differentiate the aroma of orthodox and CTC (cut-tearcurl) tea samples. The developed sensors can distinguish not only between the orthodox and CTC tea but distinct clusters are also obtained for the four different tea-samples, as visualized through principal component analysis (PCA). A radial basis function network (RBF) classifier has been used along with 10-fold cross validation technique for classification of data.

The quality evaluation of black tea is based on organoleptic methods of “Tea- tasters”. However the present quality estimation involving Tea-tasters is not a very reliable method as professional acumen, mood and other personal factors affect the judgment of tea quality. Electronic nose consists of an array of gas sensors with overlapping selectivity patterns, signal handling and classifier modules. An array of quartz crystal microbalance (QCM) sensors has been developed to differentiate among different tea samples. The important volatile components responsible for aroma of tea have been considered and the corresponding sensing materials have been identified. Five AT-cut 10 MHz Quartz crystal blanks coated with different sensing materials have been used to differentiate the aroma of orthodox and cut-tear-curl (CTC) tea samples. The developed sensors can distinguish not only between the orthodox and CTC tea but distinct clusters are also obtained for the four different tea-samples, as visualized through principal component analysis (PCA).

Aroma is a major factor for quality evaluation of finished tea. Professional tea tasters distinguish the aroma of finished tea and decide the valuation of tea. Since tea tasters' being humans their evaluations could be subjective. Instruments like spectrophotometer, high-performance liquid chromatography (HPLC) and gas liquid chromatography(GLC) measure the chemical/volatile compounds polyphenols, catechins and flavour profile of tea[b]. But these instruments are costly, time consuming, take long time to prepare the sample and also needs expert manpower to operate. Besides Electronic Nose (E-Nose) is also used by different tea factories to supplement the work of a tea taster for predicting the tea taster like score of finished tea. E-Nose can give tea taster like score within one and half minute and easy to operate. This paper describes the estimation of tea compounds responsible for tea aroma by E-Nose.

Electronic tongue has already been used to estimate the quality of tea in terms of tea-tasters scores that are subjective and limited by human sensory organs. It is known that, chemical constituents play significant role to determine the quality of tea. Thus the perception of quality as understood from an electronic tongue can be improved if it can be trained to estimate the amount of major chemicals responsible for quality of tea. An alternate method of rapid quality evaluation of tea is proposed using a voltammetric electronic tongue to determine two major taste descriptors in black tea. The correlation model is developed between the electronic tongue signatures and theaflavins/thearubigins contents of tea using multi-layer perceptrons. The perception of taste is further improved using scaled conjugate gradient as a weight optimization algorithm.

A voltammetric Electronic Tongue consisting of novel metal base electrode array for classification of Amylose present in rice samples was developed. The electrode array consisting of Gold, Palladium, Platinum, Rhodium and Iridium as working electrode has been used for transforming information of Amylose content in rice into electric signal. The electrodes output shows different pattern of electrical signal for rice samples that have different Amylose percentage. Experiments were carried out on four different rice samples having Amylose content in low, medium and high level. Principal Component Analysis (PCA) was used for visual inspection of data set. Classification was performed by statistical method. A fair discrimination was obtained and this rapid, low-cost method may be useful for prediction of Amylose percentage in rice for breeding and quality perspective.

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%.

The process of evaluating the quality of black tea by human sensory panel known as “Tea Tasters” is very subjective and its accuracy is questionable. As a result various instrumental setups have been investigated in the past for quality determination of black tea. The electronic nose is one such instrument, consisting of sensor arrays, odor detection and data acquisition system. Many clustering algorithms are available that can be used to differentiate between tea with various scores as given by the “Tea Tasters”, the data for which is obtained by an electronic nose. Recently, a heuristic algorithm called the Artificial Bee Colony (ABC) has been devised by Karaboga mainly for solving optimization problems but which has also been applied for data clustering. In this paper, the algorithm devised by Karaboga has been used on the data obtained by an electronic nose to distinguish among the various scores of black tea. Also the results obtained by ABC have been compared with the well known Fuzzy C-Means (FCM) algorithm. The paper is concluded by some observations made by the authors.

The present work deals with the development of electronic tongue (E-Tongue) and its application in determining the briskness of the black tea liquor. The parameters that determine the qualities of black tea are briskness of tea liquor, flavor, aroma and color. Human experts called "tea taster" usually evaluate the tea quality, and conventionally assign scores on a scale of 1 to 10, depending on the major quality attributes of the tea samples. A virtual instrumentation based E-Tongue has been described using voltammetry technique for correlating the briskness of tea liquor with the "tea taster" score. In this work an array of selected electrodes were immersed into black tea liquor-samples for analyzing the tea samples and computational model has been developed to correlate the measurements with the tea taster's briskness scores. With unknown tea samples, encouraging results have been obtained with more than 85% classification rate.

Black tea is an extensively consumed beverage worldwide with an expanding market. The final quality of black tea depends upon number of chemical compounds present in the tea. Out of these compounds, theaflavins (TF), which is responsible for astringency in black tea, plays an important role in determining the final taste of the finished black tea. The present paper reports our effort to correlate the theaflavins contents with the voltammetric and potentiometric electronic tongue (e‐tongue) data. Noble metal‐based electrode array has been used for collecting data though voltammetric electronic tongue where as liquid filled membrane based electrodes have been used for potentiometric electronic tongue. Black tea samples with tea taster score and biochemical results have been collected from Tea Research Association, Tocklai, India for the analysis purpose. In this paper, voltammetric and potentiometric e‐tongue responses are combined to demonstrate improvement of cluster formation among tea samples with different ranges of TF values.

Assessment of black tea quality is a difficult task due to the presence of a large number of chemical compounds. The present day practice in the tea industry for this purpose is to employ the tea-tasters, who evaluate the quality based on their experience and professional acumen. There is a dire need in the industry to assess the tea quality objectively using instrumental methods. In this pursuit, an electronic nose instrument with five gas sensors has been developed and deployed for declaring tea-taster like scores. It has been observed that pre-processing of gas sensor data improves the classification accuracy and in this paper, a comparative study of different normalization techniques is presented for black tea application using electronic nose. For this study black tea samples were collected from different tea gardens in India. At first Principal Component Analysis (PCA) is used to investigate the presence of clusters in the sensors responses in multidimensional space. Then different normalization techniques were applied on electronic nose data. Finally the comparison of classification accuracy is presented with different normalization techniques using back-propagation multilayer perceptron (BP-MLP) neural network.

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.

An appropriate normalization technique selection is one of the key issues for increasing the accuracy of correct classification for pattern recognition in an electronic nose system. This paper presents a comparative study of different normalization techniques for enhancing pattern classification of black tea using electronic nose. For this study black tea samples were collected from different tea gardens in India. At first principal component analysis (PCA) was used to investigate presence of clusters in the sensors responses in multidimensional space. Then different normalization techniques were used on the black tea data. Finally classification performances were done using BP-MLP. BP-MLP algorithm for black tea classifications using normalized data marginally enhances the pattern recognition accuracy of electronic nose system.

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

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.