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

Zhong, Yuanhong; Zhang, Shun; He, Rong-Bu; Zhang, Jingyi; Zhou, Zhaokun; Cheng, Xinyu; Huang, Guan

doi:10.3390/app9122518

Cited by 24 publications

(10 citation statements)

References 41 publications

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“…Yuan et al proposed an automatic feature extraction strategy based on a convolutional neural network in an electronic tongue system for tea classification [ 96 ]. Since each sensor provides a large number of current responses for the analyte, it is challenging to extract effective features from the large number of responses that affect the accuracy of pattern recognition.…”

Section: Chemometric Tools In Biosensingmentioning

confidence: 99%

Application and Progress of Chemometrics in Voltammetric Biosensing

Liu

et al. 2022

Biosensors

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The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.

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Section: Chemometric Tools In Biosensingmentioning

confidence: 99%

Application and Progress of Chemometrics in Voltammetric Biosensing

Liu

et al. 2022

Biosensors

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“…So, the method advances the norm using the fusion technique. Authors in 83 suggested the use of the CNN-based auto features extraction (CNN-AFE) method to enhance classification functions. Features extracted using CNN were further classified using a shallow CNN architecture.…”

Section: Related Workmentioning

confidence: 99%

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

Oyelade

Ezugwu

2022

Sci Rep

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Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature extraction and classification has been widely reported in the literature to have achieved outstanding performance and acceptance in the disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by the convolutional operations are passed on to the classifier, to avoid bottlenecking the classification operation. Unfortunately, since this has been left unaddressed, a subtle performance impairment has resulted from this omission. Therefore, this study is devoted to addressing these drawbacks using a metaheuristic algorithm to optimize the number of features extracted by the CNN, so that suggestive features are applied for the classification process. To achieve this, a new variant of the Ebola-based optimization algorithm is proposed, based on the population immunity concept and the use of a chaos mapping initialization strategy. The resulting algorithm, called the immunity-based Ebola optimization search algorithm (IEOSA), is applied to the optimization problem addressed in the study. The optimized features represent the output from the IEOSA, which receives the noisy and unfiltered detected features from the convolutional process as input. An exhaustive evaluation of the IEOSA was carried out using classical and IEEE CEC benchmarked functions. A comparative analysis of the performance of IEOSA is presented, with some recent optimization algorithms. The experimental result showed that IEOSA performed well on all the tested benchmark functions. Furthermore, IEOSA was then applied to solve the feature enhancement and selection problem in CNN for better prediction of breast cancer in digital mammography. The classification accuracy returned by the IEOSA method showed that the new approach improved the classification process on detected features when using CNN models.

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“…They have reported promising results, but they are yet to be deployed in real tea-processing environments. Additionally, CNNs have been adopted to detect diseases and pest-infected leaves (Zhou et al, 2021;Chen et al, 2019;Hu et al, 2019;Karmokar et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

Kimutai

Ngenzi

Ngoga

et al. 2021

J. Sens. Sens. Syst.

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Abstract. Tea (Camellia sinensis) is one of the most consumed drinks across the world. Based on processing techniques, there are more than 15 000 categories of tea, but the main categories include yellow tea, Oolong tea, Illex tea, black tea, matcha tea, green tea, and sencha tea, among others. Black tea is the most popular among the categories worldwide. During black tea processing, the following stages occur: plucking, withering, cutting, tearing, curling, fermentation, drying, and sorting. Although all these stages affect the quality of the processed tea, fermentation is the most vital as it directly defines the quality. Fermentation is a time-bound process, and its optimum is currently manually detected by tea tasters monitoring colour change, smelling the tea, and tasting the tea as fermentation progresses. This paper explores the use of the internet of things (IoT), deep convolutional neural networks, and image processing with majority voting techniques in detecting the optimum fermentation of black tea. The prototype was made up of Raspberry Pi 3 models with a Pi camera to take real-time images of tea as fermentation progresses. We deployed the prototype in the Sisibo Tea Factory for training, validation, and evaluation. When the deep learner was evaluated on offline images, it had a perfect precision and accuracy of 1.0 each. The deep learner recorded the highest precision and accuracy of 0.9589 and 0.8646, respectively, when evaluated on real-time images. Additionally, the deep learner recorded an average precision and accuracy of 0.9737 and 0.8953, respectively, when a majority voting technique was applied in decision-making. From the results, it is evident that the prototype can be used to monitor the fermentation of various categories of tea that undergo fermentation, including Oolong and black tea, among others. Additionally, the prototype can also be scaled up by retraining it for use in monitoring the fermentation of other crops, including coffee and cocoa.

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

Cited by 24 publications

References 41 publications

Application and Progress of Chemometrics in Voltammetric Biosensing

Application and Progress of Chemometrics in Voltammetric Biosensing

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

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