Skip to main content
SpringerLink
Log in

Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools

基于机器视觉和非线性的芽形绿茶外形感官品质评价

  • Correspondence
  • Published:
Journal of Zhejiang University-SCIENCE B Aims and scope Submit manuscript

摘要

目的

针对传统人工感官评价缺陷,建立客观、量化、 有效和无损的芽形绿茶外形品质表征方法。

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。

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Chen, Q., Zhao, J., Cai, J., 2008. Identification of tea varieties using computer vision. Trans. ASABE, 51(2):623–628. http://dx.doi.org/10.13031/2013.24363

    Article  Google Scholar 

  • 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. http://dx.doi.org/10.1631/jzus.B11c0150

    Article  Google Scholar 

  • 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. http://dx.doi.org/10.1155/2013/139347

    Google Scholar 

  • 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).

  • 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. http://dx.doi.org/10.1109/TSMCB.2011.2168604

    Article  Google Scholar 

  • 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. http://dx.doi.org/10.1016/j.foodchem.2013.06.073

    Article  CAS  PubMed  Google Scholar 

  • 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. http://dx.doi.org/10.1016/j.foodchem.2015.11.084

    Article  CAS  PubMed  Google Scholar 

  • 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. http://dx.doi.org/10.1631/jzus.B1600141

    Article  Google Scholar 

  • 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. http://dx.doi.org/10.1016/j.foodcont.2015.05.004

    Article  Google Scholar 

  • 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. http://dx.doi.org/10.1016/j.talanta.2015.10.073

    Article  CAS  PubMed  Google Scholar 

  • 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. http://dx.doi.org/10.1109/TASE.2008.2005640

    Article  Google Scholar 

  • 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. http://dx.doi.org/10.1631/jzus.A1500156

    Article  Google Scholar 

  • 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. http://dx.doi.org/10.1371/journal.pone.0113422

    Article  PubMed  PubMed Central  Google Scholar 

  • 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). http://dx.doi.org/10.3964/j.issn.1000-0593(2012)11-2971-05

    CAS  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, China

    Chun-wang Dong, Jie-wen Zhao, Yong-wen Jiang & Quan-sheng Chen

  2. Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, 310008, China

    Chun-wang Dong, Hong-kai Zhu, Yong-wen Jiang & Hai-bo Yuan

Authors
  1. Chun-wang Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong-kai Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie-wen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong-wen Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hai-bo Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Quan-sheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hai-bo Yuan or Quan-sheng Chen.

Additional information

Project supported by the National Natural Science Foundation of China (No. 31271875), the Natural Science Foundation of Zhejiang Province (No. Y16C160009), and the Key Research Projects of Zhejiang (No. 2515C02001), China

ORCID: Chun-wang DONG, http://orcid.org/0000-0001-8140-1022

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, Cw., Zhu, Hk., Zhao, Jw. et al. Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools. J. Zhejiang Univ. Sci. B 18, 544–548 (2017). https://doi.org/10.1631/jzus.B1600423

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/jzus.B1600423

关键词

Search

Navigation