A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment

Zhi, Ruicong; Zhao, Lei; Zhang, Dezheng

doi:10.3390/s17051007

Cited by 74 publications

(39 citation statements)

References 36 publications

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“…The satisfactory results clearly demonstrated the capability of using, in-situ on a real-time basis, an E-nose with MOS gas sensors, together with the SVM-linear model coupled with the maximum value signal preprocessing method (F3 technique), as a practical and potential routine analytical tool for correctly assessing tea quality level (Q1, Q2 or Q3 grades), for the seven tea brands studied (BOP, BOPF, PF, FF, PFF, BOHEA, and PLUFF). Furthermore, it should be remarked that, the satisfactory E-nose performance is in line with the discrimination performances previously reported by other researchers (sensitivities ranging from 80 to 100%) using E-nose devices for tea quality classification [1,3,24,26,27,29,[32][33][34]39].…”

Section: Supervised Multivariate Classification Methodssupporting

confidence: 87%

“…Tea (Camellia sinensis) is the most consumed non-alcoholic beverage in the world, after water [1][2][3][4][5][6][7][8][9][10]. Tea can be made from leaf and bud of Camellia sinensis through a series of production, fixation, withering, rolling, fermentation, polling and drying processes [4,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Also, electronic noses (E-nose), that utilize an array of gas sensors to give a fingerprint response to a given odor [17][18][19][20][21], combined with different multivariate statistical tools have been used for tea quality assessment and tea aroma evaluation of green or black teas from different geographical origins [1,2,[22][23][24][25][26][27][28][29][30][31][32][33]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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: Supervised Multivariate Classification Methodssupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Furthermore, Ruicong Zhi et al proposed a framework for a multi-level fusion strategy of electronic nose and electronic tongue. The time-domain based feature (mean value and max value of sensor response) and frequency-domain based feature (the energy of DWT) were fused for classification [23]. Runu Banerjee et al combined electronic tongue data with electronic nose data and then fused DWT with Bayesian statistical analysis to evaluate the artificial flavor of black tea [24].…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Zhong

Zhang

et al. 2019

Applied Sciences

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Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are "hand-crafted", which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency.

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“…With the rapid development of sensors, the wide application of electronic nose (E-nose), electronic tongue, and near-infrared technology [15][16][17][18][19][20][21][22] has made tea quality estimation easier. Especially, electronic nose technology has the convenience and objectivity of detecting food taste, which has been successfully applied to many aspects of tea research by simulating the human olfactory system, including in the tea fermentation process [23,24], tea classification [25][26][27][28], tea storage [29], and tea components [30].…”

Section: Introductionmentioning

confidence: 99%

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Yang

Wang

et al. 2019

Sensors

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Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.

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A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment

Cited by 74 publications

References 36 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

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

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