A novel Kohonen one-class method for quality control of tea coupled with artificial lipid membrane taste sensors

Shi, Yan; Liu, Xiaotong; Yin, Chongbo; Liu, Jingjing; Men, Hong

doi:10.1039/c9ay02408e

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…The KS method can make the sample distribution of the training set wider and help strengthen the generalization ability of a model. (16) Table 2 shows the classification results of the 40 feature subsets in the GWO-SVM algorithm. Overall, as the number of features increased, the classification accuracy increased while the accuracy fluctuated in a small range, indicating that there was a correlation between the features.…”

Section: Feature Selection Based On Vip Scorementioning

confidence: 99%

“…At present, the inspection method of the tea quality mainly relies on artificial sensory evaluation, but the method has the disadvantages of strong subjectivity and poor reproducibility. (16) Meanwhile, gas chromatography, (17) liquid chromatography, (18) near-infrared spectroscopy, (19) and other technologies can qualitatively analyze the chemical composition of tea, but the detection of a single chemical composition cannot characterize the overall tea quality. When people choose tea, the smell information, which is the external manifestation of the internal chemical composition of the tea, directly affects their sensory experience.…”

Section: Introductionmentioning

confidence: 99%

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Feature Reduction Method Coupled with Electronic Nose for Quality Control of Tea

Wang¹,

Yang²,

Wu³

2021

Sensors and Materials

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An effective feature reduction method is a key issue to improve the detection performance of the electronic nose (e-nose). In this study, a feature reduction method coupled with a support vector machine (SVM) was proposed to enhance the detection performance of the e-nose for the quality detection of tea. Firstly, the time-domain features were extracted, which can represent the original gas information of different grades of tea. Secondly, to consider the importance of the relationship between each feature and output category, a subset of multiple features with the best variable importance of projection (VIP) score was generated to obtain the optimized feature set. Finally, kernel principal component analysis (KPCA) and kernel entropy component analysis (KECA) were performed to further reduce the correlation between features to obtain the best feature set. The results indicated that VIP-KECA can obtain the best feature set effectively, and a good classification accuracy of 98% was obtained. This study shows that the feature reduction method is effective for enhancing the detection performance of the e-nose. It also provides an effective technique to monitor the quality of tea.

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Section: Feature Selection Based On Vip Scorementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature Reduction Method Coupled with Electronic Nose for Quality Control of Tea

Wang¹,

Yang²,

Wu³

2021

Sensors and Materials

View full text Add to dashboard Cite

show abstract

“…1 At present, the inspection method of tea quality mainly relies on artificial sensory evaluation, but the method has the disadvantages of strong subjectivity and poor reproducibility. 2 Meanwhile, gas chromatography, 3 liquid chromatography, 4 near-infrared spectroscopy, 5 and other technologies can qualitatively analyze the chemical composition of tea. However, the detection of a single chemical component cannot show the overall quality of the tea.…”

Section: Introductionmentioning

confidence: 99%

A global extended extreme learning machine combined with electronic nose for identifying tea gas information

Wang

Yang

2022

Measurement and Control

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To enhance the detection performance of electronic nose (e-nose), a recognition method of gas feature based on a global extended extreme learning machine (GEELM) is proposed, which combines the expansion factor and global balance coefficient to expand and balance the difference between categories, and improve the classification performance. Then this method is applied to identify the quality of tea. Firstly, the dragging factor and following matrix are introduced to increase the distance between classes. Secondly, the global identification coefficient is introduced further to increase the feature differences among different types of tea, and improve the classification stability. Finally, under different feature sets, the classification performance of multi-pattern recognition methods is compared to prove the effectiveness of GEELM in e-nose gas feature recognition. The results show that GEELM has the best classification accuracy of 98.20%, F1-score of 0.9871, and Kappa coefficient of 0.9775. In conclusion, GEELM can be an effective technique to identify gas features, and it also provides a new method for tea quality measurement.

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“…The measurement process of an instrument includes sensor detection, feature mining and information decisions [1]. As a machine sensing technology, the electronic nose (e-nose) mimics the human olfactory sensing mechanism to comprehensively acquire the cross-information for the sample [2,3].…”

Section: Introductionmentioning

confidence: 99%

Origin traceability of rice based on an electronic nose coupled with a feature reduction strategy

Shi

Jia

Yuan

et al. 2020

Meas. Sci. Technol.

Self Cite

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Effective information processing technology is one of the keys to improving detection accuracy. In this study, a feature reduction strategy is proposed for reducing the dimension of electronic nose (e-nose) sensor features, in combination with multiclassifiers to identify the origin of rice. Firstly, the time domain and time-frequency domain features were extracted from the detection data. Secondly, the kernel principal component analysis and kernel entropy component analysis (KECA) were introduced to reduce the dimension of the fusion features to obtain the kernel principal components (KPCs) and kernel entropy components (KECs). Finally, global discriminant analysis (GDA) was proposed in order to reduce the dimension of the KPCs and KECs to obtain the final features, respectively. The results indicated that the KECA-GDA achieved the dimensionality reduction of fusion features, effectively, the good classification accuracy of 97% and 93.29%, F 1-scores of 0.9697 and 0.9410, and Kappa coefficients of 0.9648 and 0.9210 were obtained by means of the random forest (RF) method in uncooked and cooked rice, respectively. This study shows that KECA-GDA-RF can be used as an effective tool in tracing the origin of rice. Moreover, it can provide a useful processing technique to improve the measurement accuracy of an e-nose.

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A novel Kohonen one-class method for quality control of tea coupled with artificial lipid membrane taste sensors

Abstract: A novel Kohonen one-class method for quality control of tea.

Cited by 5 publications

References 43 publications

Feature Reduction Method Coupled with Electronic Nose for Quality Control of Tea

Feature Reduction Method Coupled with Electronic Nose for Quality Control of Tea

A global extended extreme learning machine combined with electronic nose for identifying tea gas information

Origin traceability of rice based on an electronic nose coupled with a feature reduction strategy

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