近红外光谱在复合固体推进剂快速分析中的应用研究
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摘要
本文应用近红外光谱分析技术研究了复合固体推进剂中三种主要原材料组分和部分杂质的测定方法,讨论了化学计量学方法在近红外光谱预处理、波长选择、模型建立、模型传递以及奇异点判别等工作中的应用效果。
第一部分研究了近红外光谱标准加入法测定三-1-(2-甲基氮丙啶)氧化膦(MAPO)中水分含量的方法,分别采用单元线性回归、多元线性回归、主成分回归、偏最小二乘法、人工神经网络等建立校正模型,比较了不同模型对预测结果的影响。实验结果表明采用单元线性回归测定MAPO中水分具有较高的准确度和精密度,实验回收率为95%～108%,相对标准偏差为1.6%;采用本方法对实际样品的测定结果与国军标方法测定结果无显著差别。
第二部分研究了近红外光谱测定NEPE胶中硝化甘油、丁三醇三硝酸酯、聚乙二醇含量的方法,采用相关系数、显变分析、间隔偏最小二乘法、遗传算法等方法选择波长,对模型传递方法进行了研究。实验结果表明数据平移处理可以有效改善样品吸光度与浓度之间的相关性;遗传算法选择波长优于相关系数法和间隔偏最小二乘法,显变分析不适合于本项研究;采用DS算法时,可以用NEPE胶标样在不同仪器上实现模型传递,不能用普通标样传递模型;近红外光谱法快速测定NEPE胶组分含量的结果与标准分析方法的测定结果一致,两者相差小于1%;水分含量的变化、正常加热操作对NEPE胶组分含量测定的影响不明显。
第三部分研究了近红外光谱测定NEPEB胶中硝化甘油、二缩三乙二醇二硝酸酯、端羟基环氧乙烷—四氢呋喃共聚醚含量的方法,采用马氏距离、杠杆值、主成分相关图、三维主成分相关图、聚类分析等方法判别奇异点,建立稳健的校正模型。实验结果表明不同判别方法得到的奇异点不完全一致,不同判别方法对模型的影响也不一致,其中马氏距离和聚类分析较适合于本体系;各判别方法对不同预测项目的影响不一致,对改进PET的预测效果最好;删除奇异点后用近红外光谱快速测定NEPEB胶组分含量的预测误差小于2%。
The determination methods of the components and impurities in three principalmaterials of composite solid propellants were investigated by means of near infrared(NIR) technique. The effect of chemometrics methods which were applied in spectraldata pretreatment, wavelength selecting, calibration model establishing, model transferand outlier detecting was also discussed.
In partⅠ, A NIR method was investigated to determine the content of water in MAPO by standard addition method which includes various calibration models such as single wavelength regression, multiple linear regression, principal component regression, partial least square regression and artificial neural network. The influences of these different models on predictive results were compared. The experiment results indicate that a more accurate result can be obtained with the single wavelength calibration model. The recovery of the standard additions method was 95%～108% and the relative standard deviation (RSD) was 1.6%. There was no system error in the determination results between standard additions method and national military standard method.
In partⅡ, The method to determine the contents of NG, BTTN and PEG in NEPE binder was studied by NIR, which used many methods including the correlation coefficient, salience variety analysis, interval PLS and genetic algorithm to select wavelength. The model transfer methods were studied as well. The experiment results show that spectral data shift transform can improve the correlation between absorbance and concentration of samples. The GA was better than both of the correlation coefficient method and the iPLS method for selecting wavelength, and the salience variety analysis was not suitable for this purpose. The model could be transfered between different apparatus with NEPE standard samples, but not with general standard samples when D/S algorithm was applied. The results of component content in NEPE binder obtained by NIR method was in accord with that by standard method. The difference between these two methods is less than 1%. The water contents and general heating process have no obvious effect on determination results.
In partⅢ, The method to determine the contents of NG,TEGDN and PET in NEPEB binder was studies by NIR, which included outlier detection and establishment of robust calibration model. The outlier was detected by various methods such as the Mahalanobis distance, leverage, PC correlation plot, PC correlation three dimension plot and clusters dendrogram. The experiment results suggest that all of the outliers obtained by different detecting methods were not identical and the effect of these different methods on model was different. The Mahalanobis distance and the clusters dendrogram were more applicable for this system. The effect of detecting methods was different for each predicting object, which was best to improve predictive results of PET. It was less than 2% that the predictive residual error using NIR method to determine NEPEB binder component content after outliers eliminated.
第一部分研究了近红外光谱标准加入法测定三-1-(2-甲基氮丙啶)氧化膦(MAPO)中水分含量的方法,分别采用单元线性回归、多元线性回归、主成分回归、偏最小二乘法、人工神经网络等建立校正模型,比较了不同模型对预测结果的影响。实验结果表明采用单元线性回归测定MAPO中水分具有较高的准确度和精密度,实验回收率为95%～108%,相对标准偏差为1.6%;采用本方法对实际样品的测定结果与国军标方法测定结果无显著差别。
第二部分研究了近红外光谱测定NEPE胶中硝化甘油、丁三醇三硝酸酯、聚乙二醇含量的方法,采用相关系数、显变分析、间隔偏最小二乘法、遗传算法等方法选择波长,对模型传递方法进行了研究。实验结果表明数据平移处理可以有效改善样品吸光度与浓度之间的相关性;遗传算法选择波长优于相关系数法和间隔偏最小二乘法,显变分析不适合于本项研究;采用DS算法时,可以用NEPE胶标样在不同仪器上实现模型传递,不能用普通标样传递模型;近红外光谱法快速测定NEPE胶组分含量的结果与标准分析方法的测定结果一致,两者相差小于1%;水分含量的变化、正常加热操作对NEPE胶组分含量测定的影响不明显。
第三部分研究了近红外光谱测定NEPEB胶中硝化甘油、二缩三乙二醇二硝酸酯、端羟基环氧乙烷—四氢呋喃共聚醚含量的方法,采用马氏距离、杠杆值、主成分相关图、三维主成分相关图、聚类分析等方法判别奇异点,建立稳健的校正模型。实验结果表明不同判别方法得到的奇异点不完全一致,不同判别方法对模型的影响也不一致,其中马氏距离和聚类分析较适合于本体系;各判别方法对不同预测项目的影响不一致,对改进PET的预测效果最好;删除奇异点后用近红外光谱快速测定NEPEB胶组分含量的预测误差小于2%。
The determination methods of the components and impurities in three principalmaterials of composite solid propellants were investigated by means of near infrared(NIR) technique. The effect of chemometrics methods which were applied in spectraldata pretreatment, wavelength selecting, calibration model establishing, model transferand outlier detecting was also discussed.
In partⅠ, A NIR method was investigated to determine the content of water in MAPO by standard addition method which includes various calibration models such as single wavelength regression, multiple linear regression, principal component regression, partial least square regression and artificial neural network. The influences of these different models on predictive results were compared. The experiment results indicate that a more accurate result can be obtained with the single wavelength calibration model. The recovery of the standard additions method was 95%～108% and the relative standard deviation (RSD) was 1.6%. There was no system error in the determination results between standard additions method and national military standard method.
In partⅡ, The method to determine the contents of NG, BTTN and PEG in NEPE binder was studied by NIR, which used many methods including the correlation coefficient, salience variety analysis, interval PLS and genetic algorithm to select wavelength. The model transfer methods were studied as well. The experiment results show that spectral data shift transform can improve the correlation between absorbance and concentration of samples. The GA was better than both of the correlation coefficient method and the iPLS method for selecting wavelength, and the salience variety analysis was not suitable for this purpose. The model could be transfered between different apparatus with NEPE standard samples, but not with general standard samples when D/S algorithm was applied. The results of component content in NEPE binder obtained by NIR method was in accord with that by standard method. The difference between these two methods is less than 1%. The water contents and general heating process have no obvious effect on determination results.
In partⅢ, The method to determine the contents of NG,TEGDN and PET in NEPEB binder was studies by NIR, which included outlier detection and establishment of robust calibration model. The outlier was detected by various methods such as the Mahalanobis distance, leverage, PC correlation plot, PC correlation three dimension plot and clusters dendrogram. The experiment results suggest that all of the outliers obtained by different detecting methods were not identical and the effect of these different methods on model was different. The Mahalanobis distance and the clusters dendrogram were more applicable for this system. The effect of detecting methods was different for each predicting object, which was best to improve predictive results of PET. It was less than 2% that the predictive residual error using NIR method to determine NEPEB binder component content after outliers eliminated.
引文
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[3]Bittner A,Marbach R,Heise H M.Multivariate Calibration for Protein,Cholesterol and Triglycerides in Human Plasma Using Short-Wave Near Infrared Spectrometry.Journal of Molecular Structure,1995,349:341-344.
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[5]Sumaporn K,Du Y P,Koichi M,et al.Simultaneous determination of human serum albumin,lambda-globulin and glucosein a phosphate buffer solution by near-infrared spectroscopy with moving window partialleast squares regression.The Analyst,2003,128(12):1471-1477.
[6]Kasemsumran S and Du Y P.Near-infrared spectroscopic determination of human serum albumin,γ-globulin and glucose in a control serum solution with searching combination moving window partial least squares.Analytica Chimica Acta,2004,512(2):223-230.
[7]Clark C J,McGlone V A,Requejo C,et al.Dry matter determination in 'Hass'avocado by NIR spectroscopy.Postharvest Biology and Technology,2003,29(3):300-307.
[8]Lafrance D and Lands L C.In vivo lactate measurement in human tissue by near infrared diffuse reflectance spectroscopy.Vibrational Spectroscopy,2004,36(2):195-320.
[9]Macho S and Larrechi M S.Near-infrared spectroscopy and multivariate calibration for the quantitative determination of certain properties in the petrochemical industry,rends in analytical chemistry,2002,21(12):799-806.
[10]Aske N and Kallevik H.Determination of Saturate,Aromatic,Resin,and sphaltenic(SARA) Components in Crude Oils by Means of Infrared and Near-Infrared Spectroscopy.Energy & Fuels,2001,15(5):1304-1312.
[11]Hannisdal A and Hemmingsen P V.Group-Type Analysis of Heavy Crude Oils Using Vibrational Spectroscopy in Combination with Multivariate Analysis. Ind. Eng. Chem. Res., 2005, 44(5): 1349-1357.
[12] Kim M J, Lee Y H, Han C H. Real-time classification of petroleum products using near-infrared spectra. Computers and Chemical Engineering, 2000, 24 (2) : 513-517.
[13]Narve A, Harald K, Johan S. Water-in-crude oil emulsion stability studied by critical electric field measurements, Correlation to physico-chemical parameters and near-infrared spectroscopy. Journal of Petroleum Science and Engineering, 2002, 36(1): 1-17.
[14] Malley D F, Williams P C, Stainton M P. Rapid measurement of suspended C, N , and P from precambrian shield lakes using near-infrared reflectance spectroscopy. Water Research, 1996, 30(6): 1325-1332.
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