Longjing tea quality classification by fusion of features collected from E-nose

Dai, Yang; Zhi, Ruicong; Zhao, Lei; Gao, Haiyan; Shi, Bolin; Wang, Houyin

doi:10.1016/j.chemolab.2015.03.010

Cited by 53 publications

(18 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feature-level fusion is conducted by connecting the time-domain based features and frequency-based features in series, i.e., the four different features of each sensor are individually arrayed as (

M V_{i}, A V_{i}, M E_{i}, A E_{i}

), and the fused features of each sensor are concatenated in order of the E-nose and E-tongue, respectively [36]. The fused feature is standardized for further analysis.…”

Section: Methodsmentioning

confidence: 99%

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

Zhi

Zhao

Zhang

2017

Sensors

Self Cite

View full text Add to dashboard Cite

Electronic nose (E-nose) and electronic tongue (E-tongue) can mimic the sensory perception of human smell and taste, and they are widely applied in tea quality evaluation by utilizing the fingerprints of response signals representing the overall information of tea samples. The intrinsic part of human perception is the fusion of sensors, as more information is provided comparing to the information from a single sensory organ. In this study, a framework for a multi-level fusion strategy of electronic nose and electronic tongue was proposed to enhance the tea quality prediction accuracies, by simultaneously modeling feature fusion and decision fusion. The procedure included feature-level fusion (fuse the time-domain based feature and frequency-domain based feature) and decision-level fusion (D-S evidence to combine the classification results from multiple classifiers). The experiments were conducted on tea samples collected from various tea providers with four grades. The large quantity made the quality assessment task very difficult, and the experimental results showed much better classification ability for the multi-level fusion system. The proposed algorithm could better represent the overall characteristics of tea samples for both odor and taste.

show abstract

M V_{i}, A V_{i}, M E_{i}, A E_{i}

), and the fused features of each sensor are concatenated in order of the E-nose and E-tongue, respectively [36]. The fused feature is standardized for further analysis.…”

Section: Methodsmentioning

confidence: 99%

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

Zhi

Zhao

Zhang

2017

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Brenet et al used surface plasmon resonance imaging for sensing VOCs that has become a high-selective sensing device [16]. 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].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

“…It is specifically used to sense odorant molecules in analogy to the human nose. However, the architecture of an E-nose also applies in gas sensing for the detection of individual components or mixtures of gases/vapours [ 1 ], which is playing an increasing role in general purpose detection of gases in many applications such as odor analysis [ 2 , 3 , 4 ], quality control of food industry [ 5 , 6 , 7 , 8 , 9 , 10 ], environment protection [ 11 , 12 , 13 ], public health [ 14 , 15 , 16 , 17 , 18 , 19 ], explosives detection [ 20 ] and spaceflight applications [ 21 ]. The main hardware component of an E-nose is an array of non-specific gas sensors, i.e.…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose Feature Extraction Methods: A Review

Jia

Guo

Duan

et al. 2015

Sensors

225

132

View full text Add to dashboard Cite

Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.

show abstract

Longjing tea quality classification by fusion of features collected from E-nose

Cited by 53 publications

References 45 publications

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

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

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

Electronic Nose Feature Extraction Methods: A Review

Contact Info

Product

Resources

About