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

Wang, Chao; Yang, Jizheng; Wu, Junhui

doi:10.18494/sam.2021.3363

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…The E-nose finding was similar to that of tea practitioners, implying that the E-nose possesses the potential to eventually replace human sensory perception. In another study on tea quality control, Wang et al [72] proposed a feature reduction method coupled with SVM to improve quality detection performance for Shucheng Xiaolanhua tea using E-nose. The grey wolf optimization-support vector machine (GWO-SVM) classification performance for variable importance of projection-kernel entropy component analysis (VIP-KECA) achieved a 98% accuracy rate.…”

Section: Applications Of E-nose In Tea Quality Evaluationmentioning

confidence: 99%

Applications of Electronic Nose Coupled with Statistical and Intelligent Pattern Recognition Techniques for Monitoring Tea Quality: A Review

et al. 2022

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Tea is the most widely consumed non-alcoholic beverage worldwide. In the tea sector, the high demand for tea has led to an increase in the adulteration of superior tea grades. The procedure of evaluating tea quality is difficult to assure the highest degree of tea safety in the context of consumer preferences. In recent years, the advancement in sensor technology has replaced the human olfaction system with an artificial olfaction system, i.e., electronic noses (E-noses) for quality control of teas to differentiate the distinct aromas. Therefore, in this review, the potential applications of E-nose as a monitoring device for different teas have been investigated. The instrumentation, working principles, and different gas sensor types employed for E-nose applications have been introduced. The widely used statistical and intelligent pattern recognition methods, namely, PCA, LDA, PLS-DA, KNN, ANN, CNN, SVM, etc., have been discussed in detail. The challenges and the future trends for E-nose devices have also been highlighted. Overall, this review provides the insight that E-nose combined with an appropriate pattern recognition method is a powerful non-destructive tool for monitoring tea quality. In future, E-noses will undoubtedly reduce their shortcomings with improved detection accuracy and consistency by employing food quality testing.

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Section: Applications Of E-nose In Tea Quality Evaluationmentioning

confidence: 99%

Applications of Electronic Nose Coupled with Statistical and Intelligent Pattern Recognition Techniques for Monitoring Tea Quality: A Review

et al. 2022

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“…These signals are then collected by a signal acquisition circuit. After the collected signals have been analyzed and processed by a signal processing module, it becomes possible to classify and identify different gases or determine the proportions of various components in complex mixed gases [ 17 ]. Tozlu and Okumuş designed a fermentation system equipped with a gas sensor detection system function to detect the aroma during the fermentation process of black tea to enhance the production capacity of tea gardens [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing

Han,

Ahmad,

Rong

et al. 2024

Foods

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The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3−4−1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process.

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“…The electronic nose is an instrument that imitates the human olfactory system to detect complex odors in the headspace of a sample. This special detection method has been widely used in the food industry, [9][10][11] such as distinguishing tea, 12 Perilla, 13 fruit juice varieties, 14,15 the origin of Panax notoginseng, 16 and quality evaluation of Panax notoginseng powder, 17 milk, 18 cherry, 19,20 etc. Chen et al combined electronic nose with GC-MS to classify Dianhong black tea granules through the Fisher Discriminant Analysis (FDA) algorithm, and the detection method achieved an accuracy rate of 95.2%.…”

mentioning

confidence: 99%

Identification of Panax Notoginseng Powder in Different Parts Based on the Electronic Nose and Time-Domain Feature Extraction

Lin

Zhang

et al. 2022

J. Electrochem. Soc.

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To realize the non-destructive identification of Panax notoginseng powder in different parts, we propose a non-destructive identification method based on the electronic nose and time-domain feature extraction. First, The electronic nose technology combined with statistical analysis method was used to collect and extract nine time-domain characteristics of the response information of Panax notoginseng whole root powder, tap root powder, rhizome powder, and fibrous powder, including the data at 110s, the mean value between 101-120s, the maximum value, minimum value, integral value, differential value, skewness factor, kurtosis factor, and standard deviation between 0-120s. Next, three classical feature selection method were used to reduce the data dimension. Subsequently, the classification models of support vector machine (SVM), least-square support vector machine (LSSVM), and extreme learning machine (ELM) were established based on original data, multi-feature data, and feature selection data. Finally, the Grey Wolf Optimization (GWO) algorithms were introduced to optimize the parameters of the classification model. The results show that the GWO-CARS-LSSVM achieved the best modeling effect, and the classification accuracy on the test set was 97.92%.Therefore, This study provides a theoretical basis and technical support for rapid identification of adulteration of Panax notoginseng powder.

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

Cited by 4 publications

References 25 publications

Applications of Electronic Nose Coupled with Statistical and Intelligent Pattern Recognition Techniques for Monitoring Tea Quality: A Review

Applications of Electronic Nose Coupled with Statistical and Intelligent Pattern Recognition Techniques for Monitoring Tea Quality: A Review

A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing

Identification of Panax Notoginseng Powder in Different Parts Based on the Electronic Nose and Time-Domain Feature Extraction

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