Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations

Near infrared (NIR) spectroscopy combined with multivariate calibration was attempted to analyze free amino acid content of Radix Pseudostellariae. The original spectra of Pseudostellariae samples in wavelength range of 10000–4000 cm⁻¹ were acquired. Partial least squares (PLS), kernel PLS (k-PLS), back propagation neural network (BP-NN), and support vector regression (SVR) algorithms were performed comparatively to develop calibration models. Some parameters of the calibration models were optimized by cross-validation. The performance of BP-NN model was better than PLS, k-PLS, and SVR models. The root mean square error of prediction (RMSEP) and the correlation coefficient (R) of BP-NN model were 0.687 and 0.889 in prediction set respectively. Results showed that NIR spectroscopy combined with multivariate calibration has significant potential in quantitative analysis of free amino acid content in Radix Pseudostellariae.     

Rapid quantification of phenolic acids in Radix Salvia Miltrorrhiza extract solutions by FT-NIR spectroscopy in transflective mode

A rapid method for simultaneous determination of main phenolic acids in Radix Salvia Miltrorrhiza extract solutions was developed using Fourier transform near infrared spectroscopy in transflective mode and multivariate calibration and HPLC-UV as the reference method. Partial least squares (PLS) algorithm was conducted on the calibration of regression models. The multiplicative scatter correction, Norris derivative and second derivative were adopted for the spectral pre-processing, and the number of PLS factors were optimized by leave-one-out cross-validation. The performance of the final model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R). The R values achieved in the prediction set were above 0.93. The developed models were used for analysis of unknown samples and routine monitoring with satisfactory results. This work demonstrated that NIR spectroscopy combined with PLS algorithm could be used for the rapid determination of the main phenolic acids of Salvia Miltrorrhiza extract solutions.     

Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms

This paper attempted the feasibility to determine content total polyphenols content in green tea with near infrared (NIR) spectroscopy coupled with an appropriate multivariate calibration method. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were performed comparatively to calibrate regression model. The number of PLS components and the number of intervals were optimized according to root mean square error of cross-validation (RMSECV) in calibration set. The performance of the final model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. Experimental results showed that the performance of siPLS model is the best in contrast to PLS and iPLS. The optimal model was achieved with R=0.9583 and RMSEP=0.7327 in prediction set. This study demonstrated that NIR spectroscopy with siPLS algorithm could be used successfully to analysis of total polyphenols content in green tea, and revealed superiority of siPLS algorithm in contrast with other multivariate calibration methods.     

Determination of potency of heparin active pharmaceutical ingredient by near infrared reflectance spectroscopy

Potency is an important parameter for evaluation of quality of heparin active pharmaceutical ingredient (API). In this paper the feasibility to determine potency of heparin API with near infrared reflectance spectroscopy (NIRS) coupled with partial least squares (PLS) algorithm is attempted. PLS factors, correlation coefficient of calibration set (R_c), the root mean square of cross-validation (RMSECV), correlation coefficient of prediction set (R_p) and the root mean square of prediction (RMSEP) were used to evaluate the performance of the models. The optimal calibration model was obtained with R_p = 0.9721 and RMSEP = 0.55 in the 1700–1898 nm spectral region when using SG-1st derivative spectral transform method and division of calibration/prediction samples was 1/1. Three other additional samples demonstrated good prediction capability of the final model and three validation samples gave good repeatability result. NIRS has the potential to be a final lot release test to be performed in a QC laboratory.     

Determination of molecular weight of hyaluronic acid by near-infrared spectroscopy

This paper attempted the feasibility to determine the molecular weight of hyaluronic acid with near-infrared (NIR) diffuse reflectance spectroscopy. In this work, 46 experimental samples of hyaluronic acid powder were analyzed by partial least square (PLS) regression multivariate calibration method in the selected region of NIR spectra. The leave-one-out cross-validation method was used for the PLS model selection criterion. The accuracy of the final model was evaluated according to correlation coefficient of prediction set (Rp) and root mean square error of prediction set (RMSEP). The repeatability was verified through repeated measurement of spectra coupled with an appropriate chi-square test. Finally, the optimal calibration model was obtained with Rp = 0.9814 and RMSEP = 88.32 when using Savitzky-Golay first (SG-1st) derivative with 9 smoothing points spectral preprocessing method. The parameters above and repeatability of NIR spectroscopy obtained from chi-square test were both within the range of permissible error in factories. This study demonstrated that NIR spectroscopy was superior to conventional methods for the fast determination of molecular weight of hyaluronic acid.     

近红外光谱法测定氯霉素注射液含量

目的利用近红外光谱建立快速测定氯霉素注射液含量的方法。方法用抗生素微生物检定法测定氯霉素注射液含量,采用偏最小二乘法(PLS)建立NIR光谱与抗生素微生物检定法测定值之间的多元校正模型,预测氯霉素注射液中氯霉素的含量。结果所建立校正模型内部交叉验证的决定系数为98.01,内部交叉验证均方差(RMSECV)为1.67;外部验证均方差(RMSEP)为2.68,决定系数为95.19,外部验证预测值与理论值的相关系数为0.9659,预测值平均回收率为100.3%(RSD=3.2%,n=11)。结论本法操作简便、快速、结果准确,可用于药品检测车的现场快速检测。     

近红外漫反射光谱法定量分析川芎中的阿魏酸含量

目的:应用近红外光谱分析技术和化学计量学方法构建了川芎中阿魏酸含量的定量测定模型。方法:通过偏最小二乘法建立数学模型,并对预测集进行预测。结果:34个川芎样品经交叉验证建立校正模型,交叉验证均方根误差(RMSECV)为0.146%,决定系数(R2)为0.9883。用11个川芎样品进行预测,预测值与参考值的决定系数(R2)达0.9751,预测均方根误差(RMSEP)为0.251%。结论:该方法简便快速,结果准确,可应用于对不同产地不同批次的川芎进行快速检查或质量控制。     

近红外光谱法快速测定山橿药材中球松素含量

目的利用近红外光谱（NIR）技术建立一种山橿中球松素含量的快速分析方法。方法以HPLC分析值为参照,采用近红外漫反射光谱技术采集66份山橿样品的近红外漫反射光谱,结合偏最小二乘法（PLS）建立了球松素含量的定量分析模型。结果所建模型的相关系数（R2）、外部验证均方差（RMSEP）和内部交叉验证均方差（RMSECV）分别为0.992 31、0.076 5和0.450 0;验证集样品的NIR测得值与药典法测得值进行配对T检验,差异无统计学意义。结论本方法操作简便、无污染、结果准确可靠,可用于山橿中球松素含量的快速测定。     

近红外光谱法快速测定虎杖中虎杖苷、白藜芦醇、大黄素的含量

目的:建立一种同时测定虎杖提取物中虎杖苷、白藜芦醇和大黄素含量的新方法。方法:利用高效液相色谱紫外检测法测定虎杖中活性成分的化学值,然后采用傅里叶变换近红外光谱技术并结合偏最小二乘法(PLS)建立、优化模型,最后采用校正模型的决定系数(R2)、内部交叉验证均方差(RMSECV)和外部预测误差均方根(RMSEP),对校正模型进行评价。结果:虎杖苷、白藜芦醇和大黄素定量校正模型的R2分别为0.9589,0.9604,0.9128;RMSECV分别为0.0881,0.0172,0.130;RMSEP分别为0.0968,0.0153,0.111。结论:结果证明近红外光谱法用于虎杖中活性成分的定量分析,准确度较高,能满足现实中对虎杖多组分同时测定的精度要求。     

基于近红外光谱的疏血通注射液浸提过程总固体含量分析

目的 建立一种快速测定疏血通注射液浸提过程中总固体含量的近红外光谱法。方法 以水蛭和地龙2种药材浸提过程为例,采用偏最小二乘法(PLS)建立总固体含量的近红外光谱分析校正模型,实现对总固体含量的快速测定。结果 近红外光谱在一阶导数结合Karl Norris平滑滤波处理下,建模效果最佳。水蛭总固体含量校正集相关系数(R)为0.810 8,校正集和验证集预测误差均方根(RMSEC、RMSEP)分别为0.583和0.495,交叉验证误差均方根(RMSECV)为0.81,校正集和验证集相对偏差(RSEC、RSEP)分别为6.11%和5.25%;地龙总固体含量校正集R值为0.975 5,RMSEC和RMSEP分别为1.10和1.85,RMSECV为1.61,RSEC和RSEP分别为4.68%和7.80%。结论 近红外光谱可用于快速测定疏血通注射液浸提过程中总固体含量,有望推广应用于中药浸提过程的在线质量控制。     

近红外光谱法测定盐酸环丙沙星片中环丙沙星

目的 利用近红外光谱（NIR）分析技术和化学计量学方法对盐酸环丙沙星片进行无损、快速定量分析。方法 以不同生产企业生产的盐酸环丙沙星片为分析对象,用光纤探头测定其近红外漫反射光谱;定量模型的预处理方法为二阶导数,波长范围为6 101.9～4 555.2 cm^-1,采用偏最小二乘法（PLS）建立分析模型。结果 定量分析模型由93批样品经内部交叉验证建立,177批样品进行外部验证,环丙沙星质量分数范围为54.20%～82.54%,相关系数为0.986 3,交叉验证均方差（RMSECV）为1.06,外部验证均方差（RMSEP）为0.92。结论 该方法快速、简便具有一定的专属性,可用于盐酸环丙沙星片中环丙沙星的快速定量分析。     

注射用奥美拉唑钠水分含量的近红外漫反射光谱法测定

采用近红外漫反射光谱法对注射用奥美拉唑钠的水分含量进行定量分析.采集62份样品的近红外光谱,采用偏最小二乘法建立数学模型.50个样品经内部交叉验证建立预测模型,内部交叉验证均方差为0.136%;12个样品进行外部验证,外部验证预测均方差为0.074%,预测值与对照值的相关系数为0.9966.平均回收率为99.4%,RSD为3.2%.     

基于近红外光谱的阿立哌唑片溶出行为研究

目的 建立阿立哌唑片剂溶出行为近红外定量模型,预测片剂的溶出行为。方法 采集阿立哌唑片剂近红外光谱,进行溶出度试验,分别于3、6、9、12、15、30 min时测定每片的溶出度,采取卷积平滑方法预处理波段4 000.00～4 396.90 cm^-1和5 326.43～12 000.00 cm^-1的近红外光谱,以偏最小二乘法建立溶出行为模型。结果 不同时间点的校正均方根误差(RMSEC)和预测均方根误差(RMSEP)均在8%以下,不同时间点校正相关系数(R_C)和预测相关系数(R_P)均在0.95以上(6 min的相关系数除外),近红外光谱和各时间点溶出度之间呈现出良好的相关性。结论 近红外光谱分析技术能够预测阿立哌唑片剂的溶出行为,为近红外光谱分析技术在线监测片剂质量奠定了理论基础。     

近红外漫反射光谱法测定红霉素肠溶片中红霉素A的含量

目的:应用近红外漫反射技术和化学计量学的方法对红霉素肠溶片中的红霉素 A 进行定量分析。方法:通过偏最小二乘法建立数学模型,对预测集进行预测,并对实际样品的含量进行测定。结果:40个校正集样品经内部交叉验证建立校正模型,内部交叉验证确定系数 R~2=99.86,内部交叉验证均方根误差(RMSECV)为0.50。对10个预测集样品进行外部验证,预测均方根误差(RMSEP)为0.493,预测值与真实值的相关系数达0.9995。预测值的平均回收率为100.11%(RSD=0.96%,n=10),方法精密度 RSD 为0.78%(n=8)。方法稳定性 RSD 为0.95%(n=7)。结论:本方法快速简便,结果准确,适用于药品快速检查和质量控制。     

吡嗪酰胺片近红外定量分析通用性模型的建立

目的建立近红外通用性模型,能对不同厂家吡嗪酰胺片的含量进行快速、无损地测定,有效监控其质量。方法采集9个浓度梯度的各3批自制样品及来源于20个不同厂家46批次的真实样品近红外漫反射光谱,并通过聚类分析方法确定校正集和预测集,考察不同预处理方法、谱段和光滑点数的影响,选择建立了最佳的吡嗪酰胺片的定量模型。结果 46个校正集样品经交叉验证建立校正模型,交叉验证均方根误差（RMSECV）为0.775,相关系数为99.4%;27个预测集真实样品的预测均方根误差（RMSEP）为0.962,预测值与真实值的相关系数为99.8%。预测值的平均回收率为99.9%（RSD为1.26%）。方法精密度RSD为0.84%（n=6）,方法稳定性RSD为0.5%（n=5）。对6个厂家6批真实样品含量测定,相对误差均小于1.53%。结论所建立的定量模型能够对不同厂家不同规格的样品作出准确、快速的含量分析。     

基于近红外技术的金银花药材多指标成分快速检测

目的 采用近红外光谱(NIR)漫反射法和化学计量学方法对金银花药材质控指标绿原酸和木犀草苷的含量同时进行快速检测,实现金银花药材质量的高效评价。方法 HPLC分别测定不同批次金银花药材中绿原酸和木犀草苷成分的含量,同时采集药材的NIR光谱,最终采用偏最小二乘回归(PLSR)法分别建立NIR与绿原酸和木犀草苷含量间的定量校正模型,并对预测集样品中指标成分的含量进行预测。结果 所建PLSR定量模型的相关系数均>0.90,RMSECV分别为0.469和0.012 4;采用独立验证集对模型进行外部验证,RMSEP分别为0.478和0.010 5,与RMSEC值相近且与RMSECV的比值接近于1,RSEP分别为10.19%和14.76%。所建NIR快速检测方法的准确度和精密度结果良好。结论 该分析方法简便高效,可应用于不同批次金银花药材中绿原酸和木犀草苷2个关键质控指标含量的快速检测及质量控制,结果准确可靠。     

注射用阿莫西林钠克拉维酸钾近红外定量分析通用性模型的建立

目的利用近红外漫反射光谱分析技术和化学计量学的方法对注射用阿莫西林钠克拉维酸钾进行无损、快速定量分析。方法采集26批实验室自制样品和40批不同企业市售样品的近红外漫反射光谱,通过聚类分析确定校正集和验证集,采用偏最小二乘法(PLS)建立定量分析模型。结果 3个定量模型中阿莫西林浓度范围为21.28%～75.57%,克拉维酸浓度范围为2.67%～15.85%,水分范围为0.46%～15.7%。阿莫西林定量模型的交叉验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为1.42%和1.54%;克拉维酸定量模型的RMSECV和RMSEP分别为0.56%和0.71%;水分定量模型的RMSECV和RMSEP分别为0.11%和0.14%。结论建立的3个非破坏性快速定量分析模型用于不同厂家生产的注射用阿莫西林钠克拉维酸钾样品测定是可行的。     

维生素B2片快速检验的近红外漫反射光谱法定量模型初探

目的初步建立近红外漫反射光纤光谱法测定维生素B2片含量的定量模型。方法利用＂药品快检车＂配备的近红外光谱仪,用光纤探头采集近红外漫反射光纤光谱,对国内不同生产企业的维生素B2片样品建立了定量分析模型。应用最小二乘法（PLS）,在7 116.4~4 468.5 cm-1范围内,光谱采用一阶导数与多元散射校正（MSC）预处理,建立回归校正模型。结果维生素B2在57.5~87.5 mg.g-1的浓度范围内,定量分析模型的相关系数R2为0.9378,内部交叉验证均方差（RMSECV）为1.79,预测均方差（RMSEP）为1.36。结论近红外漫反射光纤光谱法能够实现维生素B2片的无损定量分析,可用于快速筛查。     

近红外光谱技术快速测定杞菊地黄丸的水分含量

目的应用近红外光谱技术建立一种杞菊地黄丸(浓缩丸)中水分含量的快速测定方法。方法以甲苯法测定的样品中水分的含量为真实值,运用近红外漫反射光谱技术采集96份杞菊地黄丸(浓缩丸)样品的近红外漫反射光谱,结合偏最小二乘法(PLS)建立水分含量的定量分析模型。结果所建水分校正模型的相关系数(R2)和内部交叉验证均方差(RMSECV)分别为0.988 09和0.0587;经外部验证,模型的预测相关系数(r2)和预测均方差(RMSEP)分别为0.9969和0.075 2。结论该方法操作简便,无污染,结果准确可靠,可用于杞菊地黄丸(浓缩丸)中水分含量的快速测定。     

Chemometric Evaluation of Near Infrared,Fourier Transform Infrared,and Raman Spectroscopic Models for the Prediction of Nimodipine Polymorphs

The objective of this study was to assess the performance of the chemometric model to predict the proportion of the recrystallized polymorphs of nimodipine from the cosolvent formulations. Ranging from 100% to 0% (w/w) of polymorph I, the two polymorphs mixtures were prepared and characterized spectroscopically using Fourier transformed infrared spectroscopy (FTIR), near-infrared spectroscopy (NIR), and Raman spectroscopy. Instrumental responses were treated to construct multivariate calibration model using principal component regression (PCR) and partial least square regression approaches. Treated data showed better model fitting than without treatment, which demonstrated higher correlation coefficient (R²) and lower root mean square of standard error (RMSE) and standard error (SE). Multiple scattering correction and standard normal variate exhibited higher R² and lower RMSE and SE values than second derivative. Goodness of fit for FTIR and NIR (R² ~ 0.99) data was better than Raman (R² ~ 0.95). Furthermore, the models were applied on the recrystallized polymorphs obtained by storing nimodipine-cosolvent formulations at selected stability conditions. The relative composition of the polymorphs differed with storage conditions. NIR-chemical imaging on recrystallized sample of nimodipine at 15°C qualitatively corroborated the model-based prediction of the two polymorphs. Therefore, these studies strongly suggest the importance of the potential utility of the chemometric model in predicting nimodipine polymorphs.