近红外光谱法测定端羟基环氧乙烷-四氢呋喃共聚醚的羟值

提出了用近红外光谱测定端羟基环氧乙烷-四氢呋喃共聚醚(PET)的羟值,结合主成分回归和偏最小二乘法建立了PET羟值与其近红外光谱之间的关联模型。结果表明,近红外光谱法与化学分析法的测定结果一致;近红外光谱法测定PET羟值的相对误差在5%以内;利用遗传算法选择部分波长建立校正可以降低模型的预测误差。     

近红外光谱法快速测定肼-70纯度

利用重量法精确配制不同肼质量分数的肼-70样品集,采用偏最小二乘法建立了肼质量分数的近红外光谱模型,以快速测定肼-70的纯度。样品恒温时间为5 min,光谱最佳预处理方式:均值中心化,一阶导数,21点平滑,模型最佳主因子数为4。近红外光谱法与碘酸钾直接滴定法测定结果相比相对偏差绝对值小于0.13%,经t检验,两种方法测定结果无显著性差异。近红外光谱法快速、准确,可用于肼-70产品的质量控制。     

人工神经网络方法用于PC/ABS材料定量分析

采用人工神经网络(ANN)算法建立了不同共混比的ABS/PC样品的近红外光谱数据与共混比的定量校正模型,并对校正模型的准确性进行了验证。实验分析结果表明,该方法适合于高分子材料共混比的测定。     

近红外光谱快速分析青贮饲料pH值和发酵产物

采用近红外光谱技术,结合偏最小二乘回归法,研究了142个不同种类的秸秆青贮饲料样品的pH值和发酵产物(乳酸、乙酸、丙酸、丁酸和氨态氮),建立了干燥粉碎和新鲜样品的近红外漫反射光谱定量分析模型以及浸提液样品的近红外透射光谱定量分析模型。研究发现,pH值的近红外漫反射光谱和透射光谱的分析效果均较好,校正模型决定系数R2和验证集样品预测值与化学值的相关关系决定系数r2都大于0.80,并且干燥粉碎、新鲜和浸提液样品的RPD值分别为3.44、2.50和2.27;3种状态样品的乳酸、乙酸、丁酸和氨态氮的定量分析模型精度需进一步提高;R2在0.64~0.85之间;RPD值在1.38~1.93之间;丙酸含量的测定结果较差。方差分析显示,3种状态样品的测定结果之间均无显著性差异(P>0.05)。     

近红外光谱法快速测定推进剂中N-甲基对硝基苯胺

采用近红外光谱法快速测定固体推进剂中N-甲基对硝基苯胺(MNA)的含量。评价了滤波平滑、一阶导数、二阶导数、多元散射校正(MSC)和标准正态变量校正(SNV)这5种不同光谱预处理方法的优化效果，基于建模参数优化结果建立了MNA定量模型，并对模型进行了准确性和重复性验证。结果表明，光谱最佳预处理方式是SNV，模型最佳主因子数为7，模型校正决定系数(R_C²)和验证决定系数(R_P²)分别为0.998 6和0.987 2，交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为0.012 0和0.009 8，重复性极差和绝对误差均低于0.2%。近红外光谱法与液相色谱法测定结果相比相对偏差在6%以内，经t检验，两种方法测定结果无显著性差异。近红外光谱法快速、准确，可用于推进剂老化进程监控。     

温度对测定乙醇含量近红外模型的影响

以不同浓度的乙醇溶液为实验材料,研究了温度对近红外光谱法定量分析结果的影响.对体积百分比在5%～70%范围内的乙醇溶液,在15,18,20,25℃和28℃等5个温度点做了研究.对光谱经过不同预处理后,使用不同浓度的20个样品建立了测定乙醇含量的眦校正模型,根据模型评价参数选择了最佳模型.分析结果显示,温度在20℃时采用一阶导数(3点平滑)光谱预处理所建立的模型最佳.其相关系数为0.9986,预测均方根误差(RMSEP)和预测标准误差(SEP)分别为0.0079和0.0081.通过对模型进行t-检验,在显著性水平大于0.05的条件下,其测定结果与GC的测定结果对比,两者无显著性差异.应用于测定酒样中乙醇的含量,结果令人满意.     

近红外光谱法测定尼莫地平片剂尼莫地平含量的4个分析模型所给出的预测值的准确性的比较

用仪器所附的积分球分析模块采集了尼莫地平片剂的片状和将片剂粉碎后的粉末样品的漫反射近红外光谱图。另用所附片剂分析模块采集了尼莫地平片剂的反射和透射近红外光谱图。分别将上述4种近红外光谱(NIRS)图用TQ Analyst 9.0软件建立了4个定量分析的模型(模型1,2,3,4)。另从3个批次各取5个样品,用《中华人民共和国药典(2015版)》中的高效液相色谱法(HPLC)测定其中尼莫地平的含量作为对上述4种模型的预测值的外部验证。根据内部验证和交叉验证所得到的4种模型的各项参数及其性能指数,可见模型2为其中的最优模型。将上述15个样品的NIRS图分别引入4种模型,获得其中尼莫地平含量的预测值;经与HPLC测定值比较,可知4种模型的预测值与HPLC测定值之间的相对误差依次为-2.41%～2.68%,-0.81%～1.18%,-5.03%～4.55%和-5.99%～5.56%。其中以模型2的相对误差最小,模型1稍大,模型3和4最大,但较接近。根据t检验法(α=0.05)的结果,4种模型所得的预测值与HPLC测定值之间无显著差异,因而4种模型均可用于尼莫地平的检测。模型2预测值的相对误差较小,但测定时需要样品前处理,使整个分析时间延长,可考虑应用于均匀性较差或形状有差异的样品的测定。其余3个模型预测值的相对误差较大,但不需要对片剂进行前处理,分析时间较短,可根据不同需要选择应用这4种模型。特别是用模型3和模型4可同时获得样品的反射和透射近红外光谱定量的信息,可对测定结果进行对照试验。     

中药材三七中皂苷类成分的近红外光谱快速无损分析新方法

提出了用近红外漫反射光谱快速无损测定三七中皂苷类成分的新方法采用 HPLC分析了中药材三七固皂昔R_1,人参皂苷Hg_1,Rb_1和Rd的含量,用吸附树脂 比色法测定了三七总皂苷（PNS）的含量,共获得R_1,Bg_1,Rb_1,Rd,PNS的含 量范围分别为1,58-5．08,21,68-46.13,11.46-40.41粉．在3500-1100cm~(-1) 扫描样品,以交叉验证误差均方根（RMsECV）为指标,通过筛选,近红外波段和光 谱预处理方法．采用偏最小二乘算法建立了近红外光谱与5个组分PHLC分析值之间 的校正模型,预测了8个未知样本．R_1,Rg_1,Rb_1,Rd及PNS校正模型的RMSECV 分别为0．40,1.47,1.94,0RMSEP分别为0．53,3．15,2．14,0．70,9．03． 该方法快速无损,结果可靠,为中药材复杂体系中化学组分的测定提供了新的绿色 分析手段．     

近红外光谱法快速鉴定油菜杂交种的纯度

建立近红外光谱技术测定油菜杂交种纯度的方法。考察了样品杯类型、光谱预处理方法和波长范围对近红外模型预测性能的影响。结果发现,由不同样品杯采集近红外光谱所建立的校正模型,其预测性能存在较大的差异,旋转杯明显优于安瓿瓶;采用消除常数偏移量对光谱进行预处理能有效地提取光谱信息,选择5 000~8 000 cm–1波数范围作为建模谱区,其包含的有效信息率最高。在最佳条件下建立油菜杂交种纯度的校正模型,其决定系数(R2)为0.980 0,交互验证均方根误差(RMSECV)为0.008 59。利用该模型对预测集进行测定,预期均方根误差(RMSEP)为0.007 59,表明该模型具有很好的预测性能,近红外光谱法用于杂交种纯度的鉴定是可行的。     

基于近红外光谱技术的亚麻纤维化学成分含量快速测定

在推进亚麻纤维的纺纱及其产业化生产过程中,快速、准确的定量分析纤维的化学成分是重要趋势。该研究利用近红外光谱技术分析亚麻纤维化学成分,以化学分析法测定值为对照,采用偏最小二乘法(PLS)建立亚麻纤维化学成分的近红外模型,从而实现了其化学成分的高效、快速定量分析。结果表明,建立的亚麻纤维纤维素、半纤维素、木质素和果胶近红外模型的校正相关系数(R_C)与验证相关系数(R_(CV))均在0.9以上,校正均方根误差(RMSEC)小于预测均方根误差(RMSEP)且均小于1。外部验证和双尾t检验表明模型预测结果较为准确,预测值与化学分析法得到的实测值无显著性差异,故该模型可用于相关化学成分含量的快速预测。     

阴离子交换树脂结合近红外漫反射光谱测定碳酸饮料中日落黄

采用强碱性阴离子交换树脂富集饮料中的合成食用色素日落黄,用近红外漫反射光谱技术直接测定富集有色素的树脂.将34个模拟样品建模,日落黄浓度范围为0.05～1.2g/L.以柠檬黄和亮丽春红5R为干扰,经偏最小二乘回归建模,得到决定系数为0.9883,标准偏差为0.0187的稳健模型.定量预测3种不同市售饮料中的日落黄,回收...     

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.     

Robust calibrations on reduced sample sets for API content prediction in tablets: Definition of a cost-effective NIR model development strategy

Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34–40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.     

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.     

A procedure for calibration transfer between near-infrared instruments--a worked example using a transmittance single tablet assay for piroxicam in intact tablets

A procedure was developed for different modes of calibration transfer in near-infrared (NIR) spectroscopy, which included a method for the selection of a subset of samples appropriate for transfer. As a worked example, these guidelines were applied to the transfer of a multivariate calibration model, representing a validated NIR single tablet assay for the active within an intact pharmaceutical product, between three equivalent dispersive NIR transmission instruments. Transfer was first evaluated between two instruments, representing the situation where both were available during calibration development. A spectral correction method alone, applied to the transfer instrument, was not sufficient to facilitate transfer, with further optimisation of the calibration model using a novel wavelength selection algorithm necessary to remove regions of the spectral range that resulted in skewed predictions on the second instrument. Through this approach, a single calibration model was found to be equally accurate and precise on the two instruments. A procedure, using the Kennard-Stone algorithm, is described for determining a reduced number of samples as a transfer set using only the spectral information from the original instrument. The purpose of the subset was to permit transfer to a new instrument where that instrument was not available until after calibration development or where it was undesirable to re-measure the full sample set (i.e. due to excessive reference chemistry). Utilising the transfer set, transfer to a third instrument was evaluated. The calibration model, optimised between the first two instruments, was not directly applicable for the third instrument, with further wavelength selection required to remove a small region of spectral data. On completion, using a full statistical evaluation, a single calibration model was found to be equally accurate and precise on all three instruments.     

基于粒子群算法的最小二乘支持向量机在红花提取液近红外定量分析中的应用

提出一种基于粒子群算法的最小二乘支持向量机(PSO-LS-SVM)方法,用于建立红花提取过程关键质控指标的定量分析模型.近红外光谱数据经波段选择、预处理和主成分分析(降维)后,利用粒子群优化(PSO)算法对最小二乘支持向量机算法中的参数进行优化,然后使用最优参数建立固含量和羟基红花黄色素A(HSYA)浓度的定量校正模型.将校正结果与偏最小二乘法回归(PLSR)和BP神经网络(BP-ANN)比较,并将所建的3个模型用于红花提取过程未知样本的预测.结果表明,BP-ANN校正结果优于PSO-LS-SVM和PLSR,但是对验证集和未知样品集的预测能力较差,而PSO-LS-SVM和PLSR模型的校正、验证结果相近,相关系数均大于0.987,RMSEC和RMSEP值相近且小于0.074,RPD值均大于6.26,RSEP均小于5.70％.对于未知样品集,pSO-LS-SVM模型的RPD值大于8.06,RMSEP和RSEP值分别小于0.07％和5.84％,较BP-ANN和PLSR模型更低.本研究所建立的PSO-LS-SVM模型表现出较好的模型稳定性和预测精度,具有一定的实践意义和应用价值,可推广用于红花提取过程的近红外光谱定量分析.     

共轴双脉冲激光诱导击穿光谱和最小二乘支持向量机法定量分析植物油中铬

利用共轴双脉冲激光诱导击穿光谱( DP-LIBS)技术对植物油(大豆油、花生油和玉米油)中的重金属铬( Cr)含量进行定量分析。采用Ava-Spec双通道高精度光谱仪采集样品的LIBS光谱,然后通过其LIBS谱线图确定了CN分子谱线(421.49 nm)、Ca原子谱线(422.64 nm)及Cr的3条原子谱线(425.39、427.43和428.87 nm),根据上述谱线建立了Cr元素的单变量定标模型和最小二乘支持向量机(LS-SVM)校正模型,并用验证样品对它们进行检验。研究结果表明,对于单变量定标法,大豆油、花生油及玉米油验证样品的平均预测相对误差(PRE)分别为12.57%,12.11%和13.72%;对于三变量LS-SVM法,其定标样品真实值与预测值之间的拟合度 R2分别为0.9785,0.9792和0.9654,验证样品的平均 PRE 分别为8.92%,8.33%和10.98%;对于五变量LS-SVM法(增加两基体元素谱线变量),其定标样品真实值与预测值之间的拟合度R2分别为0.9895,0.9901和0.9855,验证样品的平均PRE分别为7.46%,8.96%和8.95%。由此可知,LS-SVM校正模型性能优于单变量定标法,且五变量LS-SVM校正模型性能优于三变量LS-SVM校正模型;采用LS-SVM法及引入合适的基体元素谱线( CN、Ca)能有效减小定量分析误差,提高LIBS技术对植物油中Cr含量预测的精度。     

PDS用于不同温度下的近红外光谱模型传递研究

采用合适的计算方法可降低测定环境对近红外光谱校正模型稳健性的影响。该文以喷气燃料为研究对象,考察了分段直接校正算法对所建模型预测结果的影响,通过选择转移样品数及窗口宽度,建立了最佳的校正模型和光谱转移参数。结果表明,在20℃下建立近红外光谱校正模型,直接预测30℃下喷气燃料的密度,预测集样品均方根误差（RMSEP）为0.2031,而30℃近红外光谱采用分段直接校正算法模型转移后,预测集样品均方根误差（RMSEP）降低为0.1354,预测结果得到明显改善,有效地解决了样品温度对近红外光谱分析结果的影响。     

Partial least squares-near infrared determination of pesticides in commercial formulations

A solvent free, fast and environmentally friendly near infrared-based methodology (NIR) was developed for pesticide determination in commercially available formulations. This methodology was based on the direct measurement of the diffuse reflectance spectra of solid samples and a multivariate calibration model (partial least squares, PLS) to determine the active principle concentration in commercial formulations. The PLS calibration set was built on using the spiked samples by mixing different amounts of pesticide standards and powdered samples. Buprofezin, Diuron and Daminozide were used as test analytes. Concentration of Buprofezin in the samples was calculated employing a 4-factors PLS calibration using the spectral information in the range between 2231–2430 and 1657–1784 nm. For Diuron determination a 1-factor PLS calibration model using the spectral range 1110–2497 nm, after a linear removed correction. Daminozide determination was carried out employing a 4-factors PLS model using the spectral information in the ranges 1644–1772 and 2014–2607 nm without baseline correction. The root mean square errors of prediction (RMSEP) found were 1.1, 1.7 and 0.7% (w/w) for Buprofezin, Diuron and Daminozide determination, respectively. The developed PLS-NIR procedure allows the determination of 120 samples/h, does not require any sample pre-treatment and avoids waste generation.     

Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks

Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.