CLC number: TS272.7
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-05-10
Cited: 0
Clicked: 7173
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,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B1600423 @article{title="Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools", %0 Journal Article TY - JOUR
基于机器视觉和非线性的芽形绿茶外形感官品质评价创新点:采用图像特征(色泽和纹理)和AdaBoost改进的ELM(极限学习机)混合算法(Ada-ELM),明确了茶叶外形表象与人的感官感受间的非线性量化解析关系。 方法:基于机器视觉和图像处理技术,提取不同品质茶样的纹理和色泽等图像特征(表1),并与专家感官评分进行关联分析,筛选出10个极显著相关的特征变量(图1)。进而采用偏最小二乘法(PLS)和Ada-ELM,分别建立了针芽形绿茶外形感官品质的线性和非线性预测模型(表2),并进行模型性能比较。 结论:非线性模型能更好地表征图像信息与感官评分间的关联,且AdaBoost集成算法能进一步提升ELM模型的预测精度和泛化性。综合而言,采用计算机图像特征量化评价芽形绿茶的外形品质是可行的,为拓展茶叶感官评审方法和规模化、自动化加工中品质的专家决策技术,提供了一种新的技术途径和思路。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[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 ![]() Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
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