Search

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 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.

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.

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.