Near infrared spectroscopic model for determinating total catechins in tea polyphenol powder based on radical basis function network

CHEN Hua-cai; LU Jin; CHEN Xing-dan; LIU Hui-jun

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Near infrared spectroscopic model for determinating total catechins in tea polyphenol powder based on radical basis function network

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- Near infrared spectroscopic model for determinating total catechins in tea polyphenol powder based on radical basis function network
- Optics and Precision Engineering Vol. 14, Issue 1, Pages: 58-62(2006)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所应用光学国家重点实验室,吉林长春,130033
  2. 中国计量学院,浙江杭州 310018
- 作者简介：
- 基金信息：
- DOI：
  CLC： O657.33
- Received：22 September 2005，
  
  Revised：18 November 2005，
  
  Published Online：20 February 2006，
  
  Published：20 February 2006
- 稿件说明：
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CHEN Hua-cai, LU Jin, CHEN Xing-dan, et al. Near infrared spectroscopic model for determinating total catechins in tea polyphenol powder based on radical basis function network[J]. Optics and precision engineering, 2006, 14(1): 58-62.
DOI：

CHEN Hua-cai, LU Jin, CHEN Xing-dan, et al. Near infrared spectroscopic model for determinating total catechins in tea polyphenol powder based on radical basis function network[J]. Optics and precision engineering, 2006, 14(1): 58-62. DOI：

摘要

基于径向基函数神经网络(RBFN)建立了茶多酚总儿茶素含量的近红外光谱分析模型。茶多酚光谱采用小波压缩、标准化处理后

进行主成分分解

以主成分光谱作为RBFN的隐层输入函数

并通过改变主成分数对模型进行优化。当主成分数为7时得到了RBFN优化模型

该模型对定标样品集、全样品集和预测样品集的预测值与实际值回归系数

分别为0.999

0.999和0.992

预测均方误RMSEP分别为1.08%

2.06%和3.68%。

Abstract

A near infrared spectroscopic models for determinating the total catechins in tea polyphenol powder were presented based on radical basis function network. The spectra of tea polyphenol powder samples were pretreated with wavelet transform (sym6)

standard normalization and principle components analysis (PCA)

the eigenvectors of PCA were used as the input functions of RBFN. The models were optimized by changing the number of PCA and evaluated by the correlation coefficients (

) and root mean square error of prediction (RMSEP) of calibration set and the prediction set. The optimum correlation coefficient (

) of the calibration set

all sample set and prediction set were 0.999

0.999 and 0.992

respectively

and RMSEP of them were 1.08%

2.06% and 3.68%

respectively.

关键词

Keywords

references

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