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In this paper, we present an efficient technique of electronic tongue (ET) signal processing using the sparse decomposition method for the prediction of tea quality. Sparse model coefficients of an under complete dictionary are considered as characteristic attributes of the ET signals obtained for characterization of tea samples. The effectiveness of the proposed method is established by performing experiment on three different types of pulse voltammetry. The ability of the sparse coefficients, as features of ET signal, is tested by employing three different classifiers. High classification accuracy of all the classifiers on three types of voltammetric measurement data validates the usefulness of this technique. The present model-based the tea quality prediction method is very fast, simple, and straightforward. This tea quality prediction method is able to address the problem more efficiently in comparison to other techniques found in the literature.

We report on the development of a simple and cost-effective potentiometric sensor array that is based on manual “drawing” on the polymeric support with the pencils composed of graphite and different types of zeolites. The sensor array demonstrates distinct sensitivity towards a variety of inorganic ions in aqueous media. This multisensor system has been successfully applied to quantitative analysis of 100 real-life surface waters sampled in Mahananda and Hooghly rivers in the West Bengal state (India). Partial least squares regression has been utilized to relate responses of the sensors to the values of different water quality parameters. It has been found that the developed sensor array, or electronic tongue, is capable of quantifying total hardness, total alkalinity, and calcium content in the samples, with the mean relative errors below 18%.

To fight against the present pandemic scenario of COVID-19 outbreak, medication with drugs and vaccines is extremely essential other than ventilation support. In this paper, we present a list of ligands which are expected to have the highest binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used to make the drug for the novel coronavirus. Here, we implemented an architecture using 1D convolutional networks to predict drug–target interaction (DTI) values. The network was trained on the KIBA (Kinase Inhibitor Bioactivity) dataset. With this network, we predicted the KIBA scores (which gives a measure of binding affinity) of a list of ligands against the S-glycoprotein of 2019-nCoV. Based on these KIBA scores, we are proposing a list of ligands (33 top ligands based on best interactions) which have a high binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used for the formation of drugs.

Background: In recent decades, contaminations with heavy metals ions have adversely affected the environment, food safety, and human health. Heavy metals, leaching to water sources from the industrial effluents, can enter into the aquatic and food chains of humans and animals from a variety of anthropogenic sources. Scope and Approach: Heavy metal detection has been an intensive area of research today. Both laboratory-based analytical instruments and innovative sensor devices like the electronic nose, electronic tongue, and bio/chemical sensors have increasingly emerged to meet the demand for legislative actions on environmental pollution control and early warning. These evolving technology and developments particularly in the area of nanotechnology and sensors have become key contributing factors in heavy metal detection. Key Findings and Conclusions: This endeavor aims at exploring this field in detail to understand the key principles behind this flourishing science and summarize the recent development in heavy metal detection technologies. This article also gives a brief review of commercially available portable devices that has the potential to become the next gold standard instruments in heavy metal detection.

Liquor characteristics of black cut, tear, and curl tea mostly depend on two biochemical components like theaflavin (TF) and thearubigin (TR). Evaluation of tea quality can be done efficiently by estimating the concentration of TF and TR without using biochemical tests as it takes much time, which requires laborious effort for sample preparation, storage, and measurement. Moreover, the required instruments for this test are very costly. In this paper, we have proposed an efficient method of TF and TR prediction in a given tea sample using electronic tongue (ET) signal. Combinations of transformed features, like discrete cosine transform, Stockwell transform (ST), and singular value decomposition, of ET signals are fused to develop regression models to predict the contents of TF, TR, and TR/TF. Three different regression models such as artificial neural network, vector-valued regularized kernel function approximation, and support vector regression are used to evaluate the performance of the proposed method. High prediction accuracy using fusion of features ensures the effectiveness of the proposed method for prediction of TF and TR using ET signals.

The paper describes a wide-range practical application of the potentiometric multisensor system (MS) (1) for integral safety evaluation of a variety of natural waters at multiple locations, under various climatic conditions and anthropogenic stress and (2) for close to real consistency evaluation of waste water purification processes at urban water treatment plants. In total, 25 natural surface water samples were collected around St. Petersburg (Russia), analyzed as is, and after ultrasonic treatment. Toxicity of the samples was evaluated using bioassay and MS. Relative errors of toxicity assessment with MS in these samples were below 20%. The system was also applied for fast determination of integral water quality using chemical oxygen demand (COD) values in 20 samples of water from river and ponds in Kolkata (India) and performed with an acceptable precision of 20% to 22% in this task. Furthermore, the MS was applied for fast simultaneous evaluation of COD, biochemical oxygen demand, inorganic phosphorous, ammonia, and nitrate nitrogen at two waste water treatment plants (over 320 samples). Reasonable precision (within 25%) of such analysis is acceptable for rapid water safety evaluation and enables fast control of the purification process. MS proved to be a practicable analytical instrument for various real-world tasks related to water safety monitoring.

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.

A novel incremental algorithm based on fuzzy-c-means (FCM) method is proposed and implemented to effectively cluster data obtained from an electronic nose for black tea quality evaluation. The algorithm segregates data generated with the electronic nose from different batches of black tea into clusters with similar features, without requiring to access previously collected data. This feature of appending information exclusively from fresh data points entitles the algorithm to overcome catastrophic interference phenomenon common to conventional pattern recognition techniques.

Electronic tongue (ET) system is under extensive development for automatic analysis and prediction of quality of different industrial end products. Each sensor in an ET system generates a specific electronic response in presence of different organic or inorganic compounds in the sample. The vital part of the ET system is the discrimination of the complex pattern generated by the sensor array. In this paper, a novel technique of black tea quality estimation is using the ET signals. A moving window is used to extract discrete wavelet transform coefficients from the transient response of ET. The energy in different frequency bands are used as the features of the ET signal for different positions of the window. The prediction of a new sample is performed by the highest score obtained by a particular class by testing all the patterns generated by windowing ET signal. The performance of the proposed technique is verified to estimate black tea quality using two kernel classifiers, namely support vector machine and recently proposed vector valued regularized kernel function approximation method. High prediction accuracy of both the classifiers confirms the effectiveness of the proposed technique of tea quality estimation using ET signals.

The obnoxious odors generated from pulp and paper industries have been the cause of nuisance since the instigation of these industries. The objective of the study was to develop a metal oxide sensor based electronic nose for rapid measurement of odorant concentration and associated odor intensity of major reduced sulfur compounds emitted from different sources of these pulp and paper mills. The gas samples collected from the surroundings of major source points of industry were exposed to sensor array of the electronic nose and the change in voltage was measured and taken to PC through data acquisition cards. The same sets of samples were also tested with gas chromatography. The results of electronic nose and GC-FPD were correlated using response surface methodology to know the odorant concentration. The model fed with unknown industrial samples had more than 95% prediction capability. To determine odor intensity by electronic nose firstly a collective index was generated using SVD based 2-norm method (e-nose index) proportional to the sensors response relative to reference gas, methyl mercaptan. Secondly the e-nose index was associated with human expert evaluations. The training of the electronic nose enabled it to predict odorant concentration found at the industrial site and associated odor intensity in methyl mercaptan equivalent. The overall results of the experiments carried out suggest the potential of electronic nose as a device for on or off line measurement of odorant concentration and odor intensity.

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.

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.

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.

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.

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.

Orthodox black tea quality depends upon the amount of certain organic compounds present and out of these, theaflavins (TF) and thearubigins (TR) are the most important ones While TF is responsible for attractive golden colour, increased brightness and astringency in tea liquor, TR is reddish brown, reduces the brightness of tea liquor and contribute mostly for the ashy taste of the liquor with minor improvement in astringency. The rapid estimation of TF and TR thus may resolve the problem of certain uncertainty or ambiguity that may arise during quality assessment of tea by the tea tasters. In this paper, a new method for rapid measurement of concentration of TF and TR is described using a machine vision system taking images of tea liquor and employing artificial neural networks (ANN). The results show good correlation of estimated values of TF and TR with the actual concentrations obtained using ultraviolet-visible spectrophotometer (UV-VIS)