Application of machine learning algorithms in quality assurance of fermentation process of black tea-- based on electrical properties

Zhu, Hongkai; Liu, Fei; Yang, Ye; Chen, Lin; Liu, Jingyuan; Gui, Anhui; Zhang, Jianqiang; Dong, Chunwang

doi:10.1016/j.jfoodeng.2019.06.009

Cited by 60 publications

(29 citation statements)

References 39 publications

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“…In solving the classification problems, every bootstrap sample grows many classification trees. Each decision tree gives a classification label, and the type with the most classification consequences among the total decision trees is taken as the final result (Zhu et al ., 2019). The process of RF is briefly as follows: (i) employing the bagging method generates T subsets of training data.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion

Ren

Liu

Ning

et al. 2020

Int J of Food Sci Tech

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Summary Food fraud causes significant economic losses for the industry and generates distrust between the consumers and traders. Tea is one of the most valued beverages throughout the world, being vulnerable to economically motivated cheat. The objective of the study was to develop the potential of hyperspectral imaging (HSI) allying multivariate analysis and data fusion to identify the authenticity of Keemun black tea quality categories. Data fusion that integrated of texture characteristics based on grey level co‐occurrence matrix and visible and near‐infrared spectral features via competitive adaptive reweighted sampling (CARS) was as the target data for modelling. Support vector machine (SVM) and random forest (RF) were utilised for the classification of tea samples of seven grades. The RF model using fused data gave the best performance with the correct discriminant rate of 92.70% for the prediction set. This study demonstrated that HSI coupled with RF was effective in identifying tea sample rank.

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Section: Methodsmentioning

confidence: 99%

“…The traditional analysis methods have been applied for detecting the taste‐related quality components of tea, mainly involving chromatography (Zhu et al ., 2019; Ren et al ., 2020b) and spectrophotometry (Dong et al ., 2020; Wang et al ., 2020). However, all of the above chemical analysis techniques are time‐consuming and costly.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion

Ren

Liu

Ning

et al. 2020

Int J of Food Sci Tech

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“…The e-nose system is mainly composed of three parts: a sensor array, signal processing, and pattern recognition. (2) The sensor array obtains the smell information of the sample. The signal processing performs feature extraction and processing to remove redundant information, and the pattern recognition makes the classification decision.…”

Section: Introductionmentioning

confidence: 99%

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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“…The Lewis model could best describe the thin-layer drying characteristics of black tea particles. Zhu et al [9] established a discriminant mode of the degree of fermentation with multi-layer perceptron, random forest, and support vector machine methods and developed a rapid method for detecting the degree of black tea fermentation based on the electrical properties of tea. Hyperspectral imaging technology was also used to predict tea moisture content.…”

Section: Introductionmentioning

confidence: 99%

An Approach for in-Line Control of Moisture Content During Green Tea Processing

Zhao

Chen

et al. 2020

IEEE Access

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During preliminary tea processing, moisture content is an important consideration affecting the tea quality. Traditionally, the moisture content of tea leaves was manually controlled by the joint action of multiple processing units, and maintaining stability was difficult. In this paper, a multi-unit collaborative strategy was proposed for controlling moisture content in preliminary tea processing. Multivariate methods including polynomial regression, radical basis function neural network (RBFNN), and least squares support vector machine (LSSVM) were used to establish models for moisture content prediction in the first fixation, second fixation, and drying units, with minimal root mean square errors (RMSEs) of 1.34%, 0.86%, and 0.13%, respectively. The combination of RBFNN and LSSVM, with a RMSE of 0.03%, was used to model the preliminary processing of whole tea. Rough set data mining technology was used to obtain the optimum ranges of moisture content and critical process parameters. Finally, a Monte Carlo simulation experiment was carried out within the optimum range, and moisture content design spaces for the single unit and the whole processing line were obtained. With the proposed approach, the stability of the final moisture content of tea can be improved, which is of great significance for improving tea quality and accelerating the automation of tea production.

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Application of machine learning algorithms in quality assurance of fermentation process of black tea-- based on electrical properties

Cited by 60 publications

References 39 publications

Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion

Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion

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

An Approach for in-Line Control of Moisture Content During Green Tea Processing

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