纺织学报 ›› 2018, Vol. 39 ›› Issue (04): 19-23.doi: 10.13475/j.fzxb.20170305505

• 纤维材料 • 上一篇    下一篇

应用电子鼻技术的鹅绒与鸭绒区分

  

  • 收稿日期:2017-03-27 修回日期:2018-01-10 出版日期:2018-04-15 发布日期:2018-04-20

Discrimination of goose down and duck down based on electronic-nose

  • Received:2017-03-27 Revised:2018-01-10 Online:2018-04-15 Published:2018-04-20

摘要:

为探索羽绒种类的快速识别方法,研究了采用电子鼻技术区分鹅绒与鸭绒的可行性,并建立了识别羽绒种类的定性预测模型。分别提取鹅绒与鸭绒样品的响应(96~98 s)均值作为特征值,运用主成分分析、线性判别分析等方法对鹅绒与鸭绒进行定性判别,讨论不同模式识别方法区别鹅绒与鸭绒的能力,利用偏最小二乘法建立了羽绒类别预测模型。结果表明:主成分分析法对鹅绒与鸭绒的区分度为89.2%,其累计方差贡献率达到99.9%;线性判定分析法得到第1主成分的区分贡献率为90.63%;最小二乘法的校正集识别率达到97.5%,验证集的识别率达90%。

关键词: 电子鼻技术, 鹅绒, 鸭绒, 识别方法, 线性判别分析, 偏最小二乘法

Abstract:

In order to study a rapid identification methods of feather types, the electronic nose was used in distinguishing the goose down and duck down, thus a qualitative feather types prediction model was established. The average response values (96s-98s) of the feather samples were extracted as characteristic walues, the principal component and linear discriminant method were used for qualitative judgement. The identification power was discussed between different pattern recognition methods. A down types prediction model was established by partial least squares. The results proved that the differentiation on the goose down and duck down is 89.2 % by principal component analysis, and the cumulative variance contribution ratio reaches 99.9 %. For the linear discriminant method, the first principal differentiated component contribution ratio is 90.63 %. For the partial least squares method, the classification success ratio is 97.5 % for the training sets and 90 % for the testing sets.

Key words: electronic nose, goose down, duck down, identification method, linear discriminantanalysis, partial least squares analysis

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