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Relevant bibliographies by topics / Tea classification via pattern recognition

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Journal articles

Academic literature on the topic 'Tea classification via pattern recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Tea classification via pattern recognition"

1

Li, Yanjie, Jincan Lei, Junnian Yang, and Renhua Liu. "Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition." Analytical Letters 47, no. 14 (2014): 2361–69. http://dx.doi.org/10.1080/00032719.2014.908381.

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2

Huang, Danyi, Zhuang Bian, Qinli Qiu, Yinmao Wang, Dongmei Fan, and Xiaochang Wang. "Identification of Similar Chinese Congou Black Teas Using an Electronic Tongue Combined with Pattern Recognition." Molecules 24, no. 24 (2019): 4549. http://dx.doi.org/10.3390/molecules24244549.

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It is very difficult for humans to distinguish between two kinds of black tea obtained with similar processing technology. In this paper, an electronic tongue was used to discriminate samples of seven different grades of two types of Chinese Congou black tea. The type of black tea was identified by principal component analysis and discriminant analysis. The latter showed better results. The samples of the two types of black tea distributed on the two sides of the region graph were obtained from discriminant analysis, according to tea type. For grade discrimination, we determined grade predicti

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3

Luo, Aiwen, Sandip Bhattacharya, Sunandan Dutta, et al. "Surface Recognition via Force-Sensory Walking-Pattern Classification for Biped Robot." IEEE Sensors Journal 21, no. 8 (2021): 10061–72. http://dx.doi.org/10.1109/jsen.2021.3059099.

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4

Wang, Yi, W. David Pan, Jian Fu, and Bingyang Wei. "Margin setting algorithm for pattern classification via spheres." Pattern Analysis and Applications 23, no. 4 (2020): 1677–88. http://dx.doi.org/10.1007/s10044-020-00888-3.

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5

Ye, Neng Sheng, Ya Li Xie, Chang Liu, and Jian Li. "Classification of Three Green Tea Varieties by Micellar Electrokinetic Electrophoresis-Laser Induced Fluorescence and Pattern Recognition Methods." Advanced Materials Research 554-556 (July 2012): 1289–92. http://dx.doi.org/10.4028/www.scientific.net/amr.554-556.1289.

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A micellar electrokinetic electrophoresis-laser induced fluorescence detection (MEKC-LIF) method was applied for the profiling of native fluorescence in three different kinds of Chinese green teas. The running buffer was 100 mM sodium borate (pH9.8) containing 20 mM SDS, and tea infusion was injected at the pressure of 0.5 psi for 5 seconds. The applied voltage was set at 25 kV, and the excited wavelength was 488 nm, and the detected wavelength was 520 nm. Ten tea samples were collected and analyzed by the developed method. Eighteen common peaks in the MEKC-LIF profiling of green teas were sel

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6

Mahmood, Noof T., Mahmuod H. Al-Muifraje, Sameer K. Salih, and Thamir R. Saeed. "Pattern Recognition of Composite Motions based on EMG Signal via Machine Learning." Engineering and Technology Journal 39, no. 2A (2021): 295–305. http://dx.doi.org/10.30684/etj.v39i2a.1743.

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In the past few years, physical therapy plays a crucial role during rehabilitation. Numerous efforts are made to demonstrate the effectiveness of medical/ clinical and human-machine interface (HMI) applications. One of the most common control methods is using electromyography (EMG) signals generated by muscle contractions to implement the prosthetic human body parts. This paper presents an EMG signal classification system based on the EMG signal. The data is collected from biceps and triceps muscles for six different motions, i.e., bowing, clapping, handshaking, hugging, jumping, and running u

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7

Chen, Jing, Qi Liu, and Lingwang Gao. "Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model." Symmetry 11, no. 3 (2019): 343. http://dx.doi.org/10.3390/sym11030343.

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The rapid, recent development of image recognition technologies has led to the widespread use of convolutional neural networks (CNNs) in automated image classification and in the recognition of plant diseases. Aims: The aim of the present study was to develop a deep CNNs to identify tea plant disease types from leaf images. Materials: A CNNs model named LeafNet was developed with different sized feature extractor filters that automatically extract the features of tea plant diseases from images. DSIFT (dense scale-invariant feature transform) features are also extracted and used to construct a

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Zhao, Bin, Lulu Hua, Xuelong Li, Xiaoqiang Lu, and Zhigang Wang. "Weather recognition via classification labels and weather-cue maps." Pattern Recognition 95 (November 2019): 272–84. http://dx.doi.org/10.1016/j.patcog.2019.06.017.

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9

Lin, Li, Lu, et al. "Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network." Sensors 19, no. 21 (2019): 4687. http://dx.doi.org/10.3390/s19214687.

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A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a C

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10

Toksöz, Mehmet Altan, and İlkay Ulusoy. "Classification via ensembles of basic thresholding classifiers." IET Computer Vision 10, no. 5 (2016): 433–42. http://dx.doi.org/10.1049/iet-cvi.2015.0077.

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