CLC number: TS272.7
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-05-10
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Chun-wang Dong, Hong-kai Zhu, Jie-wen Zhao, Yong-wen Jiang, Hai-bo Yuan, Quan-sheng Chen. Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools[J]. Journal of Zhejiang University Science B, 2017, 18(6): 544-548.
@article{title="Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools",
author="Chun-wang Dong, Hong-kai Zhu, Jie-wen Zhao, Yong-wen Jiang, Hai-bo Yuan, Quan-sheng Chen",
journal="Journal of Zhejiang University Science B",
volume="18",
number="6",
pages="544-548",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B1600423"
}
%0 Journal Article
%T Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools
%A Chun-wang Dong
%A Hong-kai Zhu
%A Jie-wen Zhao
%A Yong-wen Jiang
%A Hai-bo Yuan
%A Quan-sheng Chen
%J Journal of Zhejiang University SCIENCE B
%V 18
%N 6
%P 544-548
%@ 1673-1581
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1600423
TY - JOUR
T1 - Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools
A1 - Chun-wang Dong
A1 - Hong-kai Zhu
A1 - Jie-wen Zhao
A1 - Yong-wen Jiang
A1 - Hai-bo Yuan
A1 - Quan-sheng Chen
J0 - Journal of Zhejiang University Science B
VL - 18
IS - 6
SP - 544
EP - 548
%@ 1673-1581
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B1600423
Abstract: Tea is one of the three greatest beverages in the world. In China, green tea has the largest consumption, and needle-shaped green tea, such as Maofeng tea and Sparrow Tongue tea, accounts for more than 40% of green tea (Zhu et al., 2017). The appearance of green tea is one of the important indexes during the evaluation of green tea quality. Especially in market transactions, the price of tea is usually determined by its appearance (Zhou et al., 2012). Human sensory evaluation is usually conducted by experts, and is also easily affected by various factors such as light, experience, psychological and visual factors. In the meantime, people may distinguish the slight differences between similar colors or textures, but the specific levels of the tea are hard to determine (Chen et al., 2008). As human description of color and texture is qualitative, it is hard to evaluate the sensory quality accurately, in a standard manner, and objectively. Color is an important visual property of a computer image (Xie et al., 2014; Khulal et al., 2016); texture is a visual performance of image grayscale and color changing with spatial positions, which can be used to describe the roughness and directivity of the surface of an object (Sanaeifar et al., 2016). There are already researchers who have used computer visual image technologies to identify the varieties, levels, and origins of tea (Chen et al., 2008; Xie et al., 2014; Zhu et al., 2017). Most of their research targets are crush, tear, and curl (CTC) red (green) broken tea, curly green tea (Bilochun tea), and flat-typed green tea (West Lake Dragon-well green tea) as the information sources. However, the target of the above research is to establish a qualitative evaluation method on tea quality (Fu et al., 2013). There is little literature on the sensory evaluation of the appearance quality of needle-shaped green tea, especially research on a quantitative evaluation model (Zhou et al., 2012; Zhu et al., 2017).
[1]Chen, Q., Zhao, J., Cai, J., 2008. Identification of tea varieties using computer vision. Trans. ASABE, 51(2):623-628.
[2]Chia, K.S., Rahim, H.A., Rahim, R.A., 2012. Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison. J. Zhejiang Univ.-Sci. B (Biomed. & Biotechnol.), 13(2): 145-151.
[3]Fu, X., Xu, L., Yu, X., et al., 2013. Robust and automated internal quality grading of a Chinese green tea (Longjing) by near-infrared spectroscopy and chemometrics. Spectroscopy, 2013(1):367-383.
[4]AQSIQ (General Administration of Quality Supervision, Inspection and Quarantine), SAC (the Standardization Administration of China), 2009. Methodology of Sensory Evaluation of Tea, GB/T 23776-2009. China Standard Publishing House, Beijing (in Chinese).
[5]Huang, G.B., Zhou, H., Ding, X., et al., 2012. Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.), 42(2): 513-529.
[6]Huang, L., Zhao, J., Chen, Q., et al., 2014. Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem., 145(7):228-236.
[7]Khulal, U., Zhao, J., Hu, W., et al., 2016. Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chem., 197(Pt B):1191-1199.
[8]Luo, Y., Li, W.L., Huang, W.H., et al., 2014. Rapid quantification of multi-components in alcohol precipitation liquid of Codonopsis Radix using near infrared spectroscopy (NIRS). J. Zhejiang Univ.-Sci. B (Biomed. & Biotechnol.), 18(5):383-392.
[9]Mir-Marqués, A., Elvirasaez, C., Cervera, M.L., et al., 2016. Authentication of protected designation of origin artichokes by spectroscopy methods. Food Control, 59(1-3): 74-81.
[10]Sanaeifar, A., Bakhshipour, A., La Guardia, M.D., 2016. Prediction of banana quality indices from color features using support vector regression. Talanta, 148:54-61.
[11]Tian, H., Mao, Z., 2010. An ensemble ELM based on modified AdaBoost.RT algorithm for predicting the temperature of molten steel in ladle furnace. IEEE Trans. Automat. Sci. Eng., 7(1):73-80.
[12]Yu, Y.L., Li, W., Sheng, D.R., et al., 2016. A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network. J. Zhejiang Univ.-Sci. A (Appl. Phys. & Eng.), 17(2):101-114.
[13]Xie, C., Li, X., Shao, Y., et al., 2014. Color measurement of tea leaves at different drying periods using hyperspectral imaging technique. PLoS ONE, 9(12):e113422.
[14]Zhou, X., Ye, Y., Zhou, Z., et al., 2012. Study on quality evaluation of Dafo Longjing tea based on near infrared spectroscopy. Spectrosc. Spect. Anal., 32(11):2971 (in Chinese).
[15]Zhu, H., Ye, Y., He, H., et al., 2017. Evaluation of green tea sensory quality via process characteristics and image information. Food Bioprod. Proc., 102(03):116-122. https://doi.org/10.1016/j.fbp.2016.12.004
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