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CLC number: TS272.7

On-line Access: 2017-06-05

Received: 2016-10-01

Revision Accepted: 2016-12-16

Crosschecked: 2017-05-10

Cited: 0

Clicked: 2009

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Chun-wang Dong

http://orcid.org/0000-0001-8140-1022

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Journal of Zhejiang University SCIENCE B 2017 Vol.18 No.6 P.544-548

http://doi.org/10.1631/jzus.B1600423


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


Author(s):  Chun-wang Dong, Hong-kai Zhu, Jie-wen Zhao, Yong-wen Jiang, Hai-bo Yuan, Quan-sheng Chen

Affiliation(s):  School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; more

Corresponding email(s):   8517809@qq.com, q.s.chen@hotmail.com

Key Words:  Needle-shaped green tea, Appearance quality, Image feature, Nonlinear tools, Extreme learning machine (ELM)


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.

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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).

基于机器视觉和非线性的芽形绿茶外形感官品质评价

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。

关键词:芽形绿茶;外形品质;图像特征;非线性建模;极限学习机(ELM)

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Reference

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