Classification of Black Tea Taste and Correlation With Tea Taster's Mark Using Voltammetric Electronic Tongue

Palit, Mousumi; Tudu, Bipan; Dutta, Paramartha; Dutta, Ankur; Jana, Arun; Roy, Jayanta; Bhattacharyya, Nabarun; Bandyopadhyay, Rajib; Chatterjee, Amit

doi:10.1109/tim.2009.2032883

Cited by 109 publications

(48 citation statements)

References 26 publications

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“…The entire transient response for each sensor is considered for analysis. The electronic nose and tongue sensors used in this work are described in detail in [9,10].The experimental conditions used for electronic nose and tongue is given in [4].…”

Section: Experimentationmentioning

confidence: 99%

Fusion of Electronic Nose and Tongue Response Using Fuzzy based Approach for Black Tea Classification

Banerjee

Modak

Mondal

et al. 2013

Procedia Technology

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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.

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Section: Experimentationmentioning

confidence: 99%

Fusion of Electronic Nose and Tongue Response Using Fuzzy based Approach for Black Tea Classification

Banerjee

Modak

Mondal

et al. 2013

Procedia Technology

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“…Wide applications, such as honey identification [3], rice discrimination [4], and beverage classification [5,6], have been a concern in recent years. In beverage classification, scholars mainly focus their attention on the substances with specific aromatic flavors such as tea and liquor [7,8] since the e-tongue identifications are more objective and reproducible than human judgments [9].…”

Section: Introductionmentioning

confidence: 99%

An Active Feature Selection Strategy for DWT in Artificial Taste

Liu

Chen

et al. 2018

Journal of Sensors

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A discrete wavelet transform (DWT) extracts meaningful information in a time-frequency domain and is a favorable feature extraction approach from pulse-like responses in large pulse voltammetry (LAPV) electronic tongues (e-tongue). A regular DWT generates lots of coefficients to describe signal details and approximations at different scales. Thus, coefficient selection is necessary to reduce the feature size. However, the common DWT-based feature selection follows a passive mode: manipulation through human experience or exhaustive trials. It is subjective, time consuming, and barely works in nonlaboratory conditions. In this paper, we present an active feature selection strategy consisting of a dispersion ratio computation and optimal searching search. To evaluate the performance of the proposed method, we prepared several beverage samples and performed experiments with a LAPV e-tongue. Meanwhile, the features of raw response, peak-inflection point, referenced DWT method, and our proposed method were presented to indicate the effects of the refined features of the proposed method. Furthermore, we utilized several classifiers such as the k-nearest neighbor (k-NN), support vector machine (SVM), and random forest (RF) to evaluate the improvement of recognition by the refined features. Compared with other regular feature extraction methods, the proposed method can automatically explore high-quality features with an acceptable feature size. Moreover, the highest average accuracy was achieved by the proposed method for each classifier. It is an alternative feature extraction approach for a LAPV e-tongue without any manipulation in real applications.

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“…They were also constructed on the base of optical and mass sensors [8]. Such devices have been mainly used in the field of food analysis: for classification of wine [9], beer [10], tea and herbal products [11], tomato samples [12], coffee [13], and milk [14]. An electronic tongue was also applied in the analysis of industrial samples (fermentation samples [15]) and in environment monitoring (water quality analysis [16], identification on toxic substances like heavy metals [17] and plant samples [18]).…”

Section: Introductionmentioning

confidence: 99%

Electronic tongue for the detection of taste-masking microencapsulation of active pharmaceutical substances

Jańczyk

Kutyła

Sollohub

et al. 2010

Bioelectrochemistry

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Classification of Black Tea Taste and Correlation With Tea Taster's Mark Using Voltammetric Electronic Tongue

Cited by 109 publications

References 26 publications

Fusion of Electronic Nose and Tongue Response Using Fuzzy based Approach for Black Tea Classification

Fusion of Electronic Nose and Tongue Response Using Fuzzy based Approach for Black Tea Classification

An Active Feature Selection Strategy for DWT in Artificial Taste

Electronic tongue for the detection of taste-masking microencapsulation of active pharmaceutical substances

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