Search

Acetylcholinesterase (AChE), a widely used enzyme for inhibition-based biosensors in pesticide residues detection, lags due to multiple-step operation, time-consuming incubation and reactivation/regeneration steps. Herein, this endeavour reports the development of Organophosphate Hydrolase (OPH), which has functional superiority over the AChE and explored in on-spot biosensing device for organophosphate pesticide residue detection in fruits and vegetables. The organophosphate degrading enzyme OPH is expressed from the ‘opd’ gene through biotechnological tools. The OPH exhibited its best activity at pH 8.0 and subsequently thermal inactivation over 37 °C. The activity of the purified OPH enzyme was found 2.75 U mL-1 at λmax 410 nm. Furthermore, the developed OPH is integrated into 96 well plate format with our previously reported UIISScan 1.1, an advanced imaging array technology based field-portable high-throughput sensory system. The developed biosensor revealed a linear range from 100 ng mL-1 to 0.1 ng mL-1 for detection of organophosphate pesticide residues with a negative slope i.e. y = 235.678x (ng mL-1) – 62.8725 with R2 = 0.99991 and n = 23. Moreover, the applicability of the developed biosensor was tested for market available fruits and vegetables. This is the first-ever reported OPH mediated on-spot biosensing device for pesticide residue detection in fruits and vegetables to the best of our knowledge.

The colorimetric sensing technology has evolved into an essential tool for high-throughput analysis including portability and cost-effectiveness among available biomedical and agricultural screening approach. In this endeavor, the objective of work is to focus on the development of a field-portable instrument based on an Uniform Illumination Imaging System (UIIS), which will facilitate the colorimetric biochemical sensing. The developed field-portable, wavelength independent UIIS has been exploited for (a) rotavirus detection using commercial enzymatic immunoassay based microplate kit; (b) pesticide residue detection and quantification; The proposed system exhibited a good correlation in comparison to another two conventional techniques, i.e., multi-plate reader (r = 0.9991938) and LC-MS/MS (r = 0.998877399) with a short analysis time of 5 min for 95 test samples. Moreover, the feasibility of UIIS system has also been explored as field-portable enzyme-linked immunosorbent assay (ELISA) plate reader. By incorporating the Mahalanobis distance calculation, the advanced algorithm has been investigated and developed to analyze the data. The overall dataset was transformed into a matrix format to give a good correlation with a conventional plate reader, i.e., r = 0.915389612. Internet of things (IoT) enabled decision support system can be exploited by using big data analytics. Finally, test results can be shared with concerned stakeholders and the remote users. Thus, the developed UIIS will help to identify potential public health threats expeditiosly compared to conventional time consuming process of sample submission to the laboratory for analysis.

In this article, wavelet energy feature (WEF) has been extracted from the responses of e-nose and e-tongue for the classification of different grades of Indian black tea. For WEF, different decomposition levels of wavelet packet transform have been tested for both the systems and performance is evaluated with K- Nearest Neighbors classifier. Energy features of the best-suited decomposition level for e-nose and e-tongue have been calculated and fused to get a combined sensor response. Results confirm that the clustering nature (PCA plot) and classification accuracy (10-fold cross-validated based on KNN) have improved (accuracy 99.75%) with the applied method on the combined data. Moreover, this method is compared with some benchmark classification methods namely PLS-DA and Sammon's projection method which exhibits the superiority of the WEF extraction method combining the responses of multi sensory system.

A quartz crystal microbalance (QCM) sensor was developed in this study with the vegetable oil from olive (OLV-QCM) to detect an important volatile organic compound, β-pinene in Indian cardamom. Hydrophobic vegetable oil from olive, which contains oleic acid and omega-9, a monounsaturated fatty acid was found to be suitable for binding β-pinene through non-covalent bonds. The fabricated QCM sensor coating was examined with the field emission scanning electron microscope (FESEM) and Fourier-transform infrared spectroscopy (FTIR) to determine its surface morphology and chemical compositions. The sensitivity, reproducibility, repeatability, and reusability were studied for the developed sensor. Notably, the sensor was observed to be highly selective towards β-pinene as compared to the other volatile components present in cardamom. The limit of detection (LOD) and limit of quantitation (LOQ) parameters were determined as 5.57 mg L−1 and 18.57 mg L−1, respectively. Moreover, the adsorption isotherm models of the sensor were studied to validate the physical adsorption affinity towards β-pinene applying Langmuir, Freundlich, and Langmuir-Freundlich isotherm models. The sensor showed a correlation factor of 0.99 with the peak area percentage of gas chromatography-mass spectrometry (GC-MS) analysis for β-pinene in cardamom samples. The sensor was prepared with natural vegetable oil, unlike health hazard chemicals. In addition to this, the low-cost, easy fabrication process ensured the suitability of the sensor for practical deployment.

Smart introduction of fertilizers in the soil should help to decrease the negative impact of chemicals on the environment without dramatic reduction of crop yield. The amounts of fertilizers have to be carefully tuned according to actual nutrients content in the soil and vegetation period of the plants. Since most of the chemical soil analyses are performed in the laboratory nowadays, fast and simple analytical tools have to be developed for this task. In this study, a potentiometric multisensor system was used for simultaneous determination of the N, P and K in the water extracts of the soils. It was shown that potentimetric sensors with multivariate data processing can be used for assessment of all three macronutrients, along with estimation of pH and conductivity of soil samples. Correlation coefficients between targeted parameters and sensor responses varied from 0.69 to 0.96. The quantification of N was possible with RMSE of 50 mg/kg in the range 60–426 mg/kg. The main benefits of the proposed approach are the short measurement duration – just 8 min for all the parameters, and reagentless procedure.

α-terpinyl acetate (aTA) is an organic volatile compound present in cardamom and it majorly makes this spice gracefully flavoured. Quartz crystal microbalance (QCM) sensor with tailor-made sensitivity provides a wide range of linear responses and is generally used for aroma sensing based on the deviation of the resonant frequency. In this present work, a QCM gas sensor (aTA-MIP-QCM) has been developed by coating the quartz crystal with a molecularly imprinted polymer (MIP) containing polymethacrylic acid (PMAA) to detect α-terpinyl acetate in small cardamom. Extensive experimentation has been carried out for the study of sensitivity, selectivity, repeatability, reproducibility, and stability of the decorated sensor and the results demonstrate that the sensor is exclusively sensitive and selective to aTA. The sensor was exposed to the aroma of cardamom samples and a significant correlation was obtained of the sensor response with the gas chromatography-mass spectrometry (GC–MS) estimations.

When partaking of any food or beverage, we actually perceive its flavour, which is the combined effect of aroma and taste. This parallel processing of signals from our nose and tongue is possible due to the presence of large and complex biological neural networks in our brain. Artificial perception of odor and taste is attempted by the scientists using similar techniques, and is implemented with an array of sensors and an array of electrodes known as electronic nose and tongue, respectively. Fusion of electronic nose and tongue at various levels provides improved results for various applications This review article discusses different techniques of fusing electronic nose and tongue information as well as applications of the combined system in different fields.

The exacting technology of piezoelectric aroma sensor has engendered a great hope for determining the ripening aroma of mango (Mangifera indica L.). 3-Carene is one of the most important ripeness indicating aromas of mango. Detection of 3-Carene content in mango is very important because fruit aroma and flavor characteristics are of key importance in determining consumer acceptance in commercial fruit markets. quartz crystal microbalance (QCM) transduces changes in mass on the crystal surface into measurable shifts in resonant frequency due to its sensitive solution-surface interface measurement capability. In this exploration, poly-dimethylsiloxane (PDMS) coated QCM sensor has been developed to sense the aroma of 3-Carene volatile in Indian mango varieties namely Langda and Chausa cultivars of mango. Empirically the performance of the sensor has been validated by characterizing the sensor through different studies viz. sensitivity, selectivity, repeatability, reproducibility, period of reusability, etc. The sensor was found to be selective to 3-Carene mostly, unlike other dominant aromas present in mango. Validation of the proposed sensor with real mangoes are carried out by correlating the frequency deviation of the sensor with the estimations of 3-Carene obtained from gas chromatography mass spectrometry (GC–MS).

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.

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.

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.

The human perception process related to quality evaluation of food or beverages can be broadly divided into two processes – sensation and perception. While the process of sensation is responsible for collection of huge amount of data by the different sensory organs, the perception process interprets the data with a fusion process in the brain. In this paper, we describe a fusion model to combine the senses of smell and taste for quality assessment of black tea using two instruments – electronic nose and electronic tongue. We propose an artificial perception model based on multi sensor data fusion to analyze the sensory information for assessing tea quality and to correlate the same with human perception. Bayesian technique is employed for multi sensor data fusion and is tested on the combined data obtained using electronic nose and tongue. Experimental results show that the artificial perception improves when two sensory systems are fused together (Classification error 8%) compared with individual system (Classification error 30%).

Development of a methodology for rapid assessment of shelf-life of fried potato wedges were carried out using electronic noses (e-noses) with metal oxide gas sensors. Fuzzy logic analysis was applied for the first time for screening the sensors and it was found that four sensors were more specific for detecting volatile organic compounds from fried potato wedges. Data obtained from these screened sensors concluded that fried potato wedges had shelf-life of 3 days when stored in inert atmosphere of nitrogen in Ziploc pouches at 23 ± 2 °C. Mahalanobis distance method was adopted for quantifying the extent of spoilage and was correlated to peroxide values and free fatty acid content obtained by biochemical assays. Since, this methodology accurately and rapidly predicted the shelf-life of fried potato wedges; it could be also applied for fast and reliable estimation of shelf-life of various fried food products.

The paper is devoted to the application of several simple mathematical methods that can be used for analysis of multisensor system data. RV and modified RV′ matrix correlation coefficients, Tucker's congruency coefficients and canonical correlation analysis can be effectively employed to reveal the similarity degree shared between two different data sets obtained for the same samples. This approach can be particularly useful when the number of available samples is too small to construct and validate quantitative regression models, but still the researcher is interested in getting some numerical estimate of the data quality to judge on electronic tongue applicability for particular analytical tasks. As a case study for this work we analyzed the data from potentiometric and voltammetric electronic tongues acquired during the analysis of black tea samples. These samples were also assessed with standard physicochemical methods and professional sensory panel. The relationships between these four data sets were studied with various matrix correlation techniques.

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.

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.

The two most important chemical groups that decide the liquor characteristics of black CTC (cut, torn and curled) tea are theaflavins (TF) and thearubigins (TR). Hence, a quick estimation of concentration of these compounds can significantly contribute to the evaluation process for the quality of finished tea in an objective manner. In this paper, a scheme for rapid measurement of concentration of TF and TR is described using a voltammetric electronic tongue with five working electrodes made of noble metals. The results indicate good correlation of electronic tongue predictions with the actual concentrations obtained using ultraviolet–visible spectrophotometer.

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.

Tea quality evaluation is a complex task and is carried out qualitatively in the industry by experienced tea tasters. But, the unpredictable and inconsistent nature of human panel tasting demands instrumental methods to assess the quality of black tea in an objective manner. For discrimination between different black tea samples and instrumental evaluation of their quality, a new method employing the principle of cyclic voltammetry is proposed in this paper. The technique has been investigated using platinum and glassy carbon as working electrodes and the resultant current from the potentiostat has been considered for data analysis. First, principal component analysis (PCA) and linear discriminant analysis (LDA) has been performed for visualization of underlying clusters and finally, a neural network model has been used to classify the data. The performance of the classifier has been established using 10-fold cross-validation method.

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.

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.