近红外光谱稀疏分量分析检测柴油品质参数

周扬; 戴曙光; 葛丁飞

doi:10.3788/OPE.20142202.0296

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近红外光谱稀疏分量分析检测柴油品质参数

现代应用光学 | 更新时间：2020-08-13

- 近红外光谱稀疏分量分析检测柴油品质参数
- Detection of diesel quality parameters by near infrared spectroscopy with sparse component analysis
- 光学精密工程 2014年22卷第2期页码：296-303
- 作者机构：
  
  1. 浙江科技学院信息与电子工程学院,浙江杭州,310023
  2. 上海理工大学光电信息与计算机工程学院上海,200093
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142202.0296
  中图分类号： O657.33;U473.1+2
- 收稿日期：2013-05-06，
  
  纸质出版日期：2014-02-20
- 稿件说明：
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周扬,戴曙光,葛丁飞. 近红外光谱稀疏分量分析检测柴油品质参数[J]. 光学精密工程, 2014,22(2): 296-303

ZHOU Yang, DAI Shu-guang, GE Ding-fei. Detection of diesel quality parameters by near infrared spectroscopy with sparse component analysis[J]. Editorial Office of Optics and Precision Engineering, 2014,22(2): 296-303
周扬,戴曙光,葛丁飞. 近红外光谱稀疏分量分析检测柴油品质参数[J]. 光学精密工程, 2014,22(2): 296-303 DOI： 10.3788/OPE.20142202.0296.

ZHOU Yang, DAI Shu-guang, GE Ding-fei. Detection of diesel quality parameters by near infrared spectroscopy with sparse component analysis[J]. Editorial Office of Optics and Precision Engineering, 2014,22(2): 296-303 DOI： 10.3788/OPE.20142202.0296.

摘要

由于光谱盲源分离中的独立分量分析方法(ICA)在柴油品控参数近红外光谱定量分析时预测效果不理想

稳定性不高

本文提出了一种在稀疏特性下的盲源分离近红外光谱分析思路——近红外光谱稀疏分量分析法

并用该方法预测了柴油沸点、密度、芳烃总量等品控参数。首先利用柴油校正集光谱样本训练冗余字典并完成光谱在该字典下的稀疏变换

接着完成混合矩阵估计

最后用混合矩阵与柴油品控参数建立回归预测模型。针对混合矩阵估计中光谱稀疏度不为一时聚状特征模糊导致无法确定聚类数的问题

提出将AP聚类算法应用于聚类过程。实验表明

近红外光谱稀疏分量分析法对柴油沸点、密度、芳烃总量预测的相关系数(

)、预测均方根误差(RMSEP)分别达到了98.91%

99.68%

99.43%和2.84

0.88×10

-3

0.59

性能优于ICA及全谱偏最小二乘(PLS)等传统方法。该方法可作为一种柴油品控参数检测的有效盲源分离定量分析方法

并可推广于其它光谱检测领域。

Abstract

Independent Component Analysis(ICA) in a blind source separation method can not obtain ideal prediction results and excellent measuring stability in detecting diesel quality parameters by near-infrared spectroscopy. Therefore

a blind source separation method based on sparse characteristics of near-infrared spectroscopy is proposed. The method is named Near Infrared Spectroscopy with Sparse Component Analysis(NIFS SCA) and is used for the prediction of boiling points

density and total aromatics for the diesel. This method firstly trains the redundant dictionary by spectral samples and finishes the sparse transformation for the calibration sample. Then

it estimates the mixing matrix

and establishes the regression model between mixing matrix and diesel quality parameters. As the clustering progress is hard to determine the number of clusters when the sparsity of spectral is not equal to one and the clustering feature is fuzzy

the AP clustering algorithm is applied to the clustering process. The experiments show that the Relative coefficient(

) and the Root Mean Square Error of Prediction (RMSEP) of the NIFS SCA for diesel boiling point

density

total aromatic prediction respectively are 98.91%

99.68%

99.43% and 2.84

0.88×10

-3

0.59

which is better than those of ICA and the full spectrum PLS methods. The proposed method can be an effective blind source separation quantitative analysis method for detecting diesel quality parameters and also can be promoted to other spectral detection applications.

关键词

Keywords

references

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