A novel fuzzy based signal analysis technique in electronic nose and electronic tongue for black tea quality analysis

Modak, Angiras; Roy, Runu Banerjee; Tudu, Bipan; Bandyopadhyay, Rajib; Bhattacharyya, Nabarun

doi:10.1109/cmi.2016.7413755

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…Recently, the possibility of merging different electronic devices (E-nose, E-tongue and/or E-eye), have been studied aiming to improve the overall classification performances of the single devices [3,[34][35][36][37][38]. Similarly, E-nose devices, comprising metal oxide semiconductor (MOS) gas sensors have been successfully applied for assessing black tea flavor grade [39] and quality level [1,2,40]. E-Nose have advantages that rapid non-destructive analysis, adequate sensitivity and relatively low cost [41,42] but E-Nose with MOS sensors have disadvantages that sensor drift, susceptible to poisoning, high power consumption, humidity-dependent signal [41].…”

Section: Introductionmentioning

confidence: 99%

The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

et al. 2019

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An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors, was used in situ for real-time classification of black tea according to its quality level. Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set value preprocessing, F1; area under curve preprocessing, F2; and maximum value preprocessing, F3), allowed grouping the samples from seven brands according to the quality level. The E-nose performance was further checked using multivariate supervised statistical methods, namely, the linear and quadratic discriminant analysis, support vector machine together with linear or radial kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset was split into two subsets, one used for model training and internal validation using a repeated K-fold cross-validation procedure (containing the samples collected during the first three days of tea production); and the other, for external validation purpose (i.e., test dataset, containing the samples collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear model together with the F3 signal preprocessing method was the most accurate, allowing 100% of correct predictive classifications (external-validation data subset) of the samples according to their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the harsh industrial environment, requiring a minimum and simple sample preparation. The proposed approach is a cost-effective and fast, green procedure that could be implemented in the near future by the tea industry. Author Contributions: Conceptualization, K.T. and A.M.P.; Data curation, I.F., D.L., and N.N.; Formal analysis, S.N.H., K.T., A.C.A.V. and A.M.P.; Funding acquisition, K.T.; Investigation, I.F.; Methodology, K.T., T.J., A.C.A.V.; Project administration, T.J.; Resources, Y.Y. and N.N.; Software, S.N.H., A.C.A.V. and A.M.P.; Supervision, K.T. and N.N.; Validation, K.T., T.J., Y.Y.; Visualization, D.L. and A.M.P.; Writing-original draft, S.N.H., D.L.; Writing-review & editing, K.T., A.C.A.V., A.M.P.

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Section: Introductionmentioning

confidence: 99%

The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

et al. 2019

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“…The research indicates that the use of three parameters of a volatile compound instead of only one parameter can allow precise determination of substances. In Modak, Roy, Tudu, Bandyopadhyay, and Bhattacharyya (2016) study, the priori choice of suitable features (like peak, mean value, etc.) was not considered, but a new linguistic classification method named Fuzzy‐based Response of Signal with Time (FRST) is proposed.…”

Section: Gas Sensing Technologymentioning

confidence: 99%

Gas sensing technology for meat quality assessment: A review

Wang

Chen

2022

J Food Process Engineering

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Meat is rich in many nutritional elements and is one of the most basic foods for human beings. The demand for meat is increasing year by year, but at the same time, meat is highly susceptible to spoilage during storage and transportation, and consumption of such meat can be hazardous to human health. Gas sensing technology provides a nondestructive, less time‐consuming, and convenient detection method compared with traditional meat quality testing techniques. In this review, we briefly describe the working principle, the common sensors, signal processing, and pattern recognition for gas sensing technology. Then, we discuss the most relevant contributions of gas sensing technology to meat freshness evaluations and authenticity assessments using different data processing methods in recent years. Finally, the problems, prospects, and likely future development of gas sensing technology are highlighted. Practical applications This article analyzes each key technology of gas sensing, points out some existing defects and future trends, reviews the practical applications of gas sensing technology in meat quality inspection in recent years, and puts forward feasible technology enhancement suggestions.

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“…The e-nose has developed rapidly in the past 20 years and has been used for detecting and classifying the perfumes, black tea, wine, fruits, bacteria, coffee, etc. Laref et al [ 3 ], Modak et al [ 4 ], Paknahad et al [ 5 ] and Chen et al [ 6 ] used e-noses to monitor gas concentration, classify black tea based on aroma profile, analyze the wine quality, and classify fruit maturity. Liang et al [ 7 ] used an e-nose to detect bacteria in the wound infection.…”

Section: Introductionmentioning

confidence: 99%

Domain Correction Based on Kernel Transformation for Drift Compensation in the E-Nose System

Tao

Liang

et al. 2018

Sensors

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This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less generalized. In the actual application scenario, the data collected previously and the data collected later may have different data distributions due to the sensor drift. If the dataset without sensor drift is treated as a source domain and the dataset with sensor drift as a target domain, a domain correction based on kernel transformation (DCKT) method is proposed to compensate the sensor drift. The proposed method makes the distribution consistency of two domains greatly improved through mapping to a high-dimensional reproducing kernel space and reducing the domain distance. A public benchmark sensor drift dataset is used to verify the effectiveness and efficiency of the proposed DCKT method. The experimental result shows that the proposed method yields the highest average accuracies compared to other considered methods.

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A novel fuzzy based signal analysis technique in electronic nose and electronic tongue for black tea quality analysis

Cited by 9 publications

References 10 publications

The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

Gas sensing technology for meat quality assessment: A review

Domain Correction Based on Kernel Transformation for Drift Compensation in the E-Nose System

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