傅立叶变换红外光谱法测定聚苯醚-聚苯乙烯共混物中的组分含量

采用傅立叶变换红外光谱对PPO/PS共混合金样品的组成进行了定量分析.以1 305 cm-1峰和699 cm-1峰分别作为聚苯醚和聚苯乙烯的定量吸收峰,并将其峰面积之比作为定量分析的基础.利用郎伯-比耳定律的理论建立了定量工作曲线,可满足PPO(或者PS)在5%～95%之间的PPO/PS共混合金样品组成的定量分析.分别采用红外光谱和核磁共振对样品进行定量分析,结果显示,两种方法定量结果的相对偏差在5%以内.红外定量方法测定结果的相对标准偏差小于2.5%(n=6).该方法可快速、准确地运用于PPO/PS共混合金样品的红外定量分析.     

红外光谱结合角度转换法快速分析乙酰二茂铁

基于红外光谱法建立了乙酰二茂铁含量快速分析模型。通过正交试验合成18组样本，采集了所有样本的红外光谱，并且进行了液相色谱分析。按照样本含量梯度由小到大选择9个作为校正集，以乙酰二茂铁对照品光谱作为参比，通过1阶导数降噪，截取特征波段1 000～1 100 cm^-1、1 610～1 680 cm^-1，得到相对应的角度值。建立角度值与乙酰二茂铁含量的分析模型为y=0.011θ-1.289，相关系数r为0.996。将另外9组验证集样本在同样数据处理条件下，分析各验证集样本中乙酰二茂铁含量，与液相色谱分析结果进行比对，绝对误差为-0.91%～3.84%，相对误差为-4.94%～6.51%。结果表明，红外光谱结合角度转换法可用于样本中乙酰二茂铁含量的快速定量分析，该方法具有准确性高、分析快捷、操作简便等优点。     

废旧涤/棉混纺织物近红外定量分析模型的建立及预测

将中红外光谱筛选出的598个纯涤、纯棉及涤/棉混纺样本采用GB/T 2910.11-2009法测定其涤、棉准确含量,其中校正集样本252个,验证集样本346个。使用便携式近红外光谱仪获取样本的原始近红外光谱(NIRS)。校正集样本依据回归系数的分布趋势和范围选取最佳建模谱区,并采用差分一阶导、S-G平滑和均值中心化相结合的方法对原始光谱进行预处理,利用偏最小二乘法(PLS)建立涤/棉混纺织物中涤含量的近红外(NIR)定量分析模型。同时分析了样本颜色对NIRS的影响,探讨了斜线光谱样本、奇异样本和不同组织结构织物对模型预测效果的影响。结果表明:利用PLS法建立的涤/棉混纺织物定量分析模型最优组合包含1个光谱区间和9个主成分因子,校正集相关系数(RC)为0.998,标准偏差(SEC)为0.908。为验证所建模型的有效性和实用性,对346个未参与建模的涤棉样本进行了预测,并将预测结果与国标法测定值进行方差分析,两种方法结果无显著差异,预测正确率达97%以上。模型的建立为废旧涤/棉混纺织物快速、无损分拣提供了基础数据库。     

无信息变量消除法变量筛选优化烟草中总氮和总糖的定量模型

应用近红外光谱技术对烟草常规化学成分中总氮和总糖进行了测定。无信息变量消除(UVE)剔除光谱矩阵中没有有效信息的数据点,并用偏最小二乘方法(PLS)建立总氮和总糖的定量分析模型,外部检验对模型效果进行了评价。总氮定量模型校正集的决定系数R2为93.35%,标准偏差SEC为0.10;外部检验集的决定系数R2为94.09%,标准偏差SEP为0.11,相对标准偏差RSD为6.12%;总糖的定量模型校正集的决定系数R2为98.20%,标准偏差SEC为0.95;外部检验集样品的决定系数R2为98.01%,标准偏差SEP为0.78,相对标准偏差RSD为2.93%。结果表明:采用UVE建立的总氮与总糖的模型优于用全谱建立的模型,UVE提高了PLS模型的预测能力。     

多组分有机物在玻碳电极上的伏安法测定

对乙酰氨基苯乙醚,氨基比林,乙酰氨基苯酚,氨基苯酚等４种有机物在玻碳电极上的伏安行为进行了研究。在０．１ｍｏｌ／Ｌ氢氧化钠溶液中得到４个灵敏度和分辨率良好的阳极氧化峰。Ｅｐ分别为０．６８,０．５１,０．２２－０．０６Ｖ（ｖｓ．ＡｇＣｌ／Ａｇ）左右,样品不需分离直接测定。     

核磁共振波谱法对氟苯尼考原粉定性定量分析的综合实验*

酰胺醇类抗生素氟苯尼考具有抗菌谱广、用量低、细菌耐药性低及毒性小等优点,已被广泛应用于兽医实践。采用对硝基甲苯为内标,以单峰甲基信号为定量信号峰,建立了定量分析氟苯尼考和对乙酰氨基苯酚的核磁共振氢谱定量方法。该方法方便快捷,适用于氟苯尼考的质量控制分析。通过本实验使学生对于核磁共振波谱有了更深的认识,培养了学生学以致用、理论联系实际和解决实际问题的能力。     

基于交互式自模型混合物分析的近红外光谱波长变量优选方法

为了提高近红外光谱定量分析的预测精度和建模效率,提出了一种基于交互式自模型的混合物分析的波长优选方法,根据光谱各波长变量的纯度值和标准差值,选择含有用信息的波长变量,并引入相关权函数解决变量间共线性问题.通过依次迭代选择的变量建立定量校正模型,由交互验证均方根预测误差(RMSECV)确定最佳波长变量个数.应用该波长变量优选方法对具有不同葡萄糖含量的两组(四成分葡萄糖水溶液实验和人体血浆实验)近红外光谱数据进行分析,两组数据中分别只选择了全部变量的0.3%建立定量校正模型,其验证集葡萄糖浓度的均方根预测误差(RMSEP)分别减少为669和15 mg/L.与全谱范围及优选波段建立的定量校正模型比较,本方法能够通过波长变量优选最小化冗余信息、提高预测精度及建模效率.     

超高效液相色谱-串联质谱法测定染发剂中7种酚类化合物

提出了应用超高效液相色谱-串联质谱法同时测定染发剂中4-氨基-2-硝基苯酚、3-二乙氨基酚、2-氨基-4-氯苯酚、2-氨基-5-硝基苯酚、2-氨基-3-硝基苯酚、1,7-二羟基萘酚和2,3-二羟基萘酚等7种酚类化合物的方法。采用甲醇萃取染发剂中酚类成分,经WatersAcquityUPLCTMBEHC18色谱柱分离,外标法定量,多反应监测模式采集质谱数据。7种酚类化合物的检出限(3S/N)均低于50.0μg.L-1。在10,20,50μg.g-1三个添加水平下,7种酚类化合物的回收率在68.8%～112.5%之间,相对标准偏差(n=6)在1.58%～12.61%之间。     

红外光谱非线性建模定量分析方法

公开号:CN101368905公开日:2009.02.18申请人:淮阴工学院摘要本发明公开了红外光谱非线性建模定量分析方法,该方法依据分子振动原理,利用Matlab软件编程,Statistica软件数据处理,建立非线性模型,由模型获得最终定量结果。本发明通过建立非线性模型实现了定量分析,减小了测量误差,提高了测量的准确性、可行性。红外光谱非线性建模定量分析方法     

激光诱导击穿光谱结合CARS变量选择方法定量检测倍硫磷含量

利用双脉冲激光诱导击穿光谱(LIBS)技术对溶液中的倍硫磷含量进行定量检测。采用二通道高精度光谱仪采集不同浓度倍硫磷样品在206.28~481.77 nm波段的LIBS光谱,并对光谱进行多元散射校正(MSC)、标准正态变量变换(SNV)及3点平滑预处理,根据偏最小二乘(PLS)建模确定最优的预处理方法。在此基础上,利用竞争性自适应重加权算法(CARS)筛选与倍硫磷相关的重要变量,然后应用PLS回归建立溶液中倍硫磷含量的定量分析模型,并与单变量定量分析模型及未变量选择的PLS定量分析模型进行比较。结果表明,相比单变量定量分析模型及原始光谱PLS定量分析模型,CARS-PLS定量分析模型的性能更优,其模型的校正集和预测集的决定系数及平均相对误差分别为0.969 4、15.537%和0.995 9、5.016%。此外,与原始光谱PLS模型相比,CARS-PLS模型仅使用其中1.9%的波长变量,但预测集平均误差却由9.829%下降为5.016%。由此可见,LIBS技术检测溶液中的倍硫磷含量具有一定的可行性,且CARS方法能简化定量分析模型,提高模型的预测精度。     

白酒基酒中酮类物质的近红外光谱检测方法

应用近红外光谱技术建立了白酒基酒中2,3-丁二酮和3-羟基-2-丁酮的快速检测模型。从洛阳杜康酒厂选取182个白酒基酒样品为材料,运用气相色谱法测得两种物质的化学值,同时采集其在12 000~4 000 cm-1范围内的光谱数据,采用偏最小二乘法(PLS)结合内部交叉验证建立校正模型。通过对比不同光谱预处理下PLS模型效果对其进行优化,确定2,3-丁二酮和3-羟基-2 丁酮的最佳预处理方法分别为一阶导数+多元散射校正和二阶导数,最佳光谱区间分别为9 403.2~7 497.9 cm-1和9 403.2~7 497.9 cm-1+6 101.7~5 449.8 cm-1。优化后2,3-丁二酮和3 羟基-2-丁酮校正集样品的化学值和近红外预测值的决定系数(R2)分别为0.960 2和0.963 2,交叉验证均方根误差(RMSECV)分别为0.39、0.22 mg/100 mL;通过外部检验,验证集样品的R2分别为0.957 6和0.957 8,预测均方根误差(RMSEP)分别为0.40、0.24 mg/100 mL。结果表明,应用近红外光谱技术结合化学计量学方法所建立的模型有较高的准确度,能够满足白酒生产中酮类物质的快速检测需要。     

近红外光谱法快速测定涂料固化剂中游离甲苯二异氰酸酯含量

用气相色谱分析值为参照,采用近红外透射光谱（NIR）技术采集相应样品的NIR光谱,研究了涂料固化剂中游离甲苯二异氰酸酯（TDI）含量的快速测定分析方法。 并从120个固化剂样品中挑选出109个代表性的样品建模,选择7320~7250 cm-1和8485~8370 cm-1波段区间,用偏最小二乘法(PLS)和完全交互验证方式建立TDI含量的预测模型。 结果表明,固化剂中游离甲苯二异氰酸酯含量和近红外光谱之间存在较好的相关性,其预测模型的校正集均方差（RMSEC）为0.0815,验证集均方差（RMSEP）为0.0715,模型性能良好。 近红外光谱法可快速准确测定游离甲苯二异氰酸酯（TDI）含量,用于固化剂样品快速分析。     

近红外光谱结合膜富集技术测定饮料中微量邻苯二甲酸二异辛酯的含量

提出了一种基于在线膜富集的近红外漫反射光谱技术,对饮料中的微量塑化剂邻苯二甲酸二异辛酯(DEHP)进行快速检测。采用聚醚砜膜对饮料中的DEHP进行富集,将富集DEHP的膜直接进行近红外漫反射检测。参考DEHP的透射近红外光谱,对波数进行选择,以4 420～4 060、4 700～4 540、6 040～5 600cm-1作为建模的波数区间。通过比较原始光谱、多元散射校正、一阶求导、二阶求导及其组合,考察了光谱预处理方法对模型的影响,用去一交互验证法建立了偏最小二乘(PLS)模型,并用所建立的校正模型对校正集样品进行了预测。结果表明,在选定的波数区间,当用一阶求导对校正集光谱进行预处理时,所建立的模型对校正集的预测效果最佳,在隐变量数为7时,对校正集所有样品的校正均方根误差(RMSEC)为0.188 7mg/L。用此模型对预测集样品进行预测时,DEHP的质量浓度在0.5～5.0 mg/L范围内,预测均方根误差(RMSEP)为0.232 4 mg/L,平均相对预测误差为6.29%。     

Quantification of paracetamol in intact tablets using near-infrared transmittance spectroscopy.

Production batch samples of paracetamol tablets and specially prepared out-of-specification batches covering the range 90-110% of the stated amount (500 mg) were analysed by the BP official UV assay and by NIR transmittance spectroscopy. NIR measurements were made on 20 intact tablets from each batch, scanned five times each (10 min measurement time per batch) over the spectral range 6000-11,520 cm-1. An average spectrum was calculated for each batch. Partial least squares (PLS) regression models were set up using a calibration set (20 batches) between the NIR response and the reference tablet paracetamol content (UV). Various pre-treatments of the spectra were examined; the smallest relative standard error of prediction (0.73%) was obtained using the first derivative of the absorbance over the full spectrum. Only two principal components were required for the PLS model to give a good relationship between the spectral information and paracetamol content. Applying this model to the validation set (15 batches) gave a mean bias of -0.08% and a mean accuracy of 0.59% with relative standard deviations of 0.75 and 0.44%, respectively. The proposed method is non-destructive and therefore lends itself to on-line/at-line production control purposes. The method is easy to use and does not require a knowledge of the mass of the tablets.     

NIR spectroscopy of jarosites

Near-infrared (NIR) spectroscopy has been used to analyse a suite of synthesised jarosites of formula Mn(Fe3+)6(SO4)4(OH)12 where M is K, Na, Ag, Pb, NH4+ and H3O+. Whilst the spectra of the jarosites show a common pattern, differences in the spectra are observed which enable the minerals to be distinguished. The NIR bands in the 6300-7000 cm-1 region are attributed to the first fundamental overtone of the infrared and Raman hydroxyl stretching vibrations. The NIR spectrum of the ammonium-jarosite shows additional bands at 6460 and 6143 cm-1, attributed to the first fundamental overtones of NH stretching vibrations. A set of bands are observed in the 4700-5500 cm-1 region which are assigned to combination bands of the hydroxyl stretching and deformation vibrations. The ammonium-jarosite shows additional bands at 4730 and 4621 cm-1, attributed to the combination of NH stretching and bending vibrations. NIR spectroscopy has the ability to distinguish between the jarosite minerals even when the formula of the minerals is closely related. The NIR spectroscopic technique has great potential as a mineral exploratory tool on planets and in particular Mars.     

Elimination of interference information by a new hybrid algorithm for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the interference information for multivariate calibration of near-infrared (NIR) spectra that includes noise, background and systemic spectral variation irrelevant to concentration. The method consists of two parts: approximate derivative based on continuous wavelet transform (CWT) and orthogonal signal correction (OSC). After the approximate derivative calculated by CWT, OSC was performed. It was successfully applied to real complex NIR spectral data to eliminate the interference information. Correction for the interference of NIR spectra resulted in a substantial improvement in the predicted precision, and a more concise calibration model was obtained. The proposed procedure also compared favourably with several pretreatment methods, and the new method appears to provide a high-performance pretreatment tool for multivariate calibration of NIR spectra. In addition, the strategy proposed here can be applied to various other spectral data for quantitative purposes as well.     

Near-infrared calibration transfer based on spectral regression

A calibration transfer method for near-infrared (NIR) spectra based on spectral regression is proposed. Spectral regression method can reveal low dimensional manifold structure in high dimensional spectroscopic data and is suitable to transfer the NIR spectra of different instruments. A comparative study of the proposed method and piecewise direct standardization (PDS) for standardization on two benchmark NIR data sets is presented. Experimental results show that spectral regression method outperforms PDS and is quite competitive with PDS with background correction. When the standardization subset has sufficient samples, spectral regression method exhibits excellent performance.     

Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

NIR analysis of pharmaceutical samples without reference data: improving the calibration

Using an appropriate set of samples to construct the calibration set is crucial with a view to ensuring accurate multivariate calibration of NIR spectroscopic data. In this work, we developed and optimized a new methodology for incorporating physical variability in pharmaceutical production based on the NIR spectrum for the process. Such a spectrum contains the spectral changes caused by each treatment applied to the component mixture during the production process. The proposed methodology involves adding a set of process spectra (viz. difference spectra between those for production tablets and a laboratory mixture of identical nominal composition) to the set of laboratory samples, which span the wanted concentration range, in order to construct a calibration set incorporating all physical changes undergone by the samples in each step of the production process. The best calibration model among those tested was selected by establishing the influence of spectral pretreatments used to obtain the process spectrum and construct the calibration models, and also by determining the multiplying factor m to be applied to the process spectra in order to ensure incorporation of all variability sources into the calibration model. The specific samples to be included in the calibration set were selected by principal component analysis (PCA). To this end, the new methodology for constructing calibration sets for determining the Active Principle Ingredients (API) and excipients was applied to Irbesartan tablets and validated by application to the API and excipients of paracetamol tablets. The proposed methodology provides simple, robust calibration models for determining the different components of a pharmaceutical formulation.     

复杂样品近红外光谱定量分析模型的构建方法

针对复杂样品近红外光谱分析中校正集的设计问题, 探讨了标准样品参与复杂样品建模的可行性. 通过标准样品和复杂基质样品共同构建的偏最小二乘(PLS)模型, 考察了波段筛选和建模参数对预测结果的影响. 结果表明, 采用PLS方法建立定量模型时, 校正集样品性质应该尽量与预测集样品相似, 当样品的性质相差较大时, 适当增加校正集样品的差异性可使模型具有更强的预测能力. 同时, 波段优选对提高预测结果的准确性具有重要的意义.