采样误差分布分析法建立氯丙醇含量的稳健近红外光谱分析模型

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英文篇名：Robust Near Infrared Spectroscopy Model for Determination of Chloropropanol Concentration with Sampling Error Profile Analysis 作者：陈万超 ; 陶鑫 ; 范长春 ; 张飞宇 ; 杜一平 英文作者：CHEN Wan-Chao;TAO Xin;FAN Chang-Chun;ZHANG Fei-Yu;DU Yi-Ping;Shanghai Key Laboratory of Functional Materials Chemistry,School of Chemistry & Molecular Engineering,East China University of Science and Technology;Department of Quality Assurance,Jiangsu Yangnong Chemical Group Co.; 关键词：采样误差分布分析 ; 近红外光谱 ; 建模 ; 稳健 英文关键词：Sampling error profile analysis;;Near infrared ray,Modeling,Robust 中文刊名：FXHX 英文刊名：Chinese Journal of Analytical Chemistry 机构：华东理工大学上海市功能材料重点实验室化学与分子工程学院;江苏扬农化工集团; 出版日期：2019-02-15 出版单位：分析化学 年：2019 期：v.47 基金：上海市科委科研计划项目(No17142201100)资助~~ 语种：中文; 页：FXHX201902022 页数：7 CN：02 ISSN：22-1125/O6 分类号：156-162

摘要

利用近红外光谱技术结合采样误差分布分析(SEPA)方法建立了二氯丙醇产品生产过程的氯化液杂质3-氯-1,2-丙二醇浓度的分析模型。对样本数据进行1000次随机划分,建立1000个子模型,获得多个潜变量数下的交互检验误差,进行统计分析。绘制了误差分布图,计算其中位数、标准偏差、偏斜度和分布峰度等统计指标,通过这些指标的综合分析对近红外光谱分析模型进行条件优化、建模和模型评价等。4种光谱处理方法显示出比较理想的模型性能,作为候选与不同波长区域的选择相结合,继续运用SEPA运算,进一步优化模型。最终优化的建模条件为:一阶导数结合标准正态变换; 6931～6017 vcm~(-1)波数区间;使用5个偏最小二乘潜变量。校正、交互检验和独立验证误差分别为0.881%、1.282%和1.167%。所选择的波长具有可解释性,模型的各项统计参数合理、可信。研究结果表明,SEPA能全面、合理地考察多项统计指标,可以建立实用、稳健的近红外光谱分析模型。
        The analytical near infrared (NIR) spectroscopy model of 1,3-dichloro-2-propanol in the production process of 1,3-dichloro-2-propanol was established with sampling error profile analysis (SEPA)method. In SEPA,with a number of randomly sampling(1000 sampling in this work),1000 sub-models were built and a series of cross validation errors under several PLS factors were obtained. With the errors,an error profile was plotted and some statistic parameters,including median,standard deviation,skewness and kurtosis were calculated. Condition optimization, modeling and evaluation of the model were carried out by comprehensively investigating the statistic performances. It was found that four spectral pretreatment methods showed good performance,and they were used to the following process of wavenumber selection to further improve the model with SEPA. The optimization results showed that the method was 1~(st) derivative coupled with standard normal variate,the wavenumber region selected here was 6931-6017 cm~(-1),and five PLS factors were employed. Under the optimal conditions,the root mean squared error of calibration,the cross validation and the prediction were 0.881%,1. 282% and 1. 167%,respectively. The selected wavenumber region was explainable,and the built model was reasonable and believable in view of statistics,which indicated that SEPA could supply a useful and robust NIR model.

引文

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