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$\beta $ -myrcene is one of the important terpene hydrocarbons emitted in substantial amounts from mango. In this regard, an ethyl cellulose modified quartz crystal microbalance (QCM) sensor has developed to detect $\beta $ -myrcene in mango. The developed sensor exhibits a linear sensing rage of 10–1000 ppm with a sensitivity factor of 0.1 Hz/ppm. The limit of detection of the sensor was 1.68 ppm. Furthermore, the sensor is found to be selective toward the target $\beta $ -myrcene analyte with respect to other volatile organic compounds present in mango with selectivity factor of 42.98% at 500 ppm. The response of the sensor was further correlated with the response obtained from the gas chromatography-mass spectrometry (GC-MS) study. The response of the prepared sensor was further validated by real mango samples. A sensor array constituting the developed sensor and the developed sensors was explored to validate the necessity of using multiple sensors for VOC detection, unlike a single sensor.

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.

The extract of Jasmine called concrete is an important floricultural export commodity of India. The major quality attribute of both jasmine and concrete is fragrance. The fragrance profile of jasmine flower has been studied using a custom built handheld e-Nose (HEN). Three species of jasmine, J. Sambac , J. Grandiflorum , and J. Auriculatum were applied to e-Nose one by one. The principal component analysis of the responses showed that distinct clusters were formed for different species. Another study was made on the mostly cultivated J. Sambac species to find out a relationship between concrete quality with various flowering stages as well as with seasonal variation. Experiments were carried out over 200 samples collected from various gardens of South India. Multivariate sensor data were computed in a single dimensional intensity value termed as fragrance index (FI). Flowering stages were classified by Industry experts in terms of bud opening indicator (BOI). HEN was trained to formulate a scale of BOI in the range of 0–2 based on the FI. Best quality of concrete is found to be obtained from jasmine flower with BOI level of 1.5. Jasmine concrete, produced at laboratory from flowers harvested at different time-of-the-day, i.e., with different BOI levels were analysed using GC-MS to check the volatile emission patterns. Results showed that concrete produced with BOI level 1.5 emitted maximum aroma generating volatiles. These observations showed that e-Nose can be a potential gadget for fragrance profiling as well as selection of best quality flowers for concrete extraction.

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 this paper, a novel method to model the responses of electronic tongue (ET) sensors using autoregressive (AR) and AR moving average techniques is presented. The transient response of each electrode present in the sensor array of an ET is characterized with tea samples of different qualities. Models coefficients are used as the characteristics features of the ET response corresponding to the tea samples. Three different classifiers, namely, artificial neural network, vector valued regularized kernel function approximation, and one-versus-one support vector machine, are employed to evaluate the performance of these features to discriminate the quality of black tea. Experimental results on three types of voltammetric measurement data show that the proposed method may be very useful for prediction of tea quality. The present model-based classification method is very straightforward and provides better or similar performance compared with some other methods proposed in the literature for ET signal classification.

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.

In many applications of electronic nose, the instrument is trained with data generated by human experts prior to its deployment in the fields. Quite often, these data are conflicting and inaccurate and thus the performance of an electronic nose is degraded. Moreover, degradation of its performance may also be due to the presence of redundant features or sensors in the array. While deploying an electronic nose for a specific application, it is observed that some of the sensors may not be required and only a subset of the sensor array contributes to the decision, which implies that optimization of the sensor array is also very important. To obtain a consistent and precise data set, both the conflicting data and irrelevant features must be removed. The rough set theory is capable of dealing with such an imprecise, inconsistent data set and in this paper, the rough-set based algorithm has been applied to remove the conflicting training patterns and optimize the sensor array in an electronic nose instrument used for sensing aroma of black tea samples.