Methanol Content Determination in Methanol Diesel with Mid Infrared Spectroscopy Analysis Using Least Square Support Vector Machine

OUYANG Ai-guo; TANG Tian-yi; ZHOU Xin; LIU Yan-de

doi:10.3788/fgxb20163710.1253

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Methanol Content Determination in Methanol Diesel with Mid Infrared Spectroscopy Analysis Using Least Square Support Vector Machine

更新时间：2020-08-12

- Methanol Content Determination in Methanol Diesel with Mid Infrared Spectroscopy Analysis Using Least Square Support Vector Machine
- Chinese Journal of Luminescence Vol. 37, Issue 10, Pages: 1253-1258(2016)
- 作者机构：
  
  华东交通大学光机电技术及应用研究所,江西南昌,330013
- 作者简介：
- 基金信息：
- DOI：10.3788/fgxb20163710.1253
  CLC： O657.33;TE626.9
- Received：08 April 2016，
  
  Revised：22 May 2016，
  
  Published：05 October 2016
- 稿件说明：
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欧阳爱国, 唐天义, 周鑫等. 最小二乘支持向量机结合中红外光谱测定甲醇柴油甲醇含量[J]. 发光学报, 2016,37(10): 1253-1258

OUYANG Ai-guo, TANG Tian-yi, ZHOU Xin etc. Methanol Content Determination in Methanol Diesel with Mid Infrared Spectroscopy Analysis Using Least Square Support Vector Machine[J]. Chinese Journal of Luminescence, 2016,37(10): 1253-1258
欧阳爱国, 唐天义, 周鑫等. 最小二乘支持向量机结合中红外光谱测定甲醇柴油甲醇含量[J]. 发光学报, 2016,37(10): 1253-1258 DOI： 10.3788/fgxb20163710.1253.

OUYANG Ai-guo, TANG Tian-yi, ZHOU Xin etc. Methanol Content Determination in Methanol Diesel with Mid Infrared Spectroscopy Analysis Using Least Square Support Vector Machine[J]. Chinese Journal of Luminescence, 2016,37(10): 1253-1258 DOI： 10.3788/fgxb20163710.1253.

摘要

采用中红外光谱法对甲醇柴油的甲醇含量进行检测分析。首先，对采集到的原始光谱进行预处理（标准正则变换、多元散射校正、一阶微分、二阶微分、Savitzky-Goly平滑），采用偏最小二乘法和最小二乘支持向量机建立了甲醇柴油的甲醇含量预测模型，并比较了不同预处理方法对模型预测能力的影响。实验结果表明，LSSVM的建模效果最佳，其预测集相关系数

为0.981 8，预测均方误差RMSEP为1.3917%（体积比）。因此，中红外光谱技术可用于甲醇柴油中甲醇含量的快速检测，且可以达到很好的效果。

Abstract

The mid-infrared spectroscopy was used to detect the content of methanol in the methanol diesel. Firstly

the collected original spectra were pre-processed by the standard normal transformation(SNV)

multiple scatter correction (MSC)

first derivative

second derivative and Savitzky-Goly smoothing methods. The quantitative model of methanol was established by partial least square method and least square support vector machine (SVM). The influence of different pre-processing methods on the model prediction capability was also investigated. The results show that LSSVM modeling is the optimal forecasting model with the prediction set correlation coefficient

of 0.981 8 and the prediction mean square error (RMSEP) of 1.3917%(volume ratio). So

the mid infrared spectrum technology can be used for the rapid detection of methanol in the methanol diesel and achieve very good results.

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references

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