Application of near infrared spectroscopy for rapid analysis of intermediates of Tanreqing injection

A method for rapid quantitative analysis of four kinds of Tanreqing injection intermediates was developed based on Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares (PLS) algorithm. The NIR spectra of 120 samples were collected in transflective mode. The concentrations of chlorogenic acid, caffeic acid, luteoloside, baicalin, ursodesoxycholic acid (UDCA), and chenodeoxycholic acid (CDCA) were determined with the HPLC–DAD/ELSD as reference method. In the PLS calibration, the NIR spectra were pretreated with different methods and the number of PLS factors used in the model calibration was optimized by leave-one-out cross-validation. The performance of the final PLS models was evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), BIAS, standard error of prediction (SEP), and correlation coefficients (R). The R values in the prediction sets were all higher than 0.93, and the SEPs for the 6 compounds are 1.18, 6.02, 2.71, 155, 126, 30.0 mg/l, respectively. The established models were used for the liquid preparation process analysis of Tanreqing injection in three batches, and a model updating method was proposed for the long-term usage of the established models. This work demonstrated that NIR spectroscopy is more rapid and convenient than the conventional methods to analyze the intermediates of Tanreqing injection, and the presented method is helpful to the implementation of process analytical technology (PAT) in pharmaceutical industry of Chinese Medicines Injections.     

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。     

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 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 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.     

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%.     

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。     

近红外光谱法快速分析小儿复方磺胺甲嗯唑颗粒的水分含量

目的利用近红外漫反射光谱（NIRDRS）分析技术和化学计量学方法对小儿复方磺胺甲嗯唑颗粒的水分含量进行快速定量分析。方法以全国不同企业生产的小儿复方磺胺甲嗯唑颗粒为分析对象,为扩大检测的浓度范围,通过恒温恒湿引湿的方法制得实验室制备样品,用光纤探头直接接触样品采集近红外漫反射光谱,采用偏最小二乘法（PLS）建立模型。结果小儿复方磺胺甲嗯唑颗粒水分定量分析模型由64个样本经内部交叉验证建立,42个样本用于外部验证,浓度范围为0．12％～6．15％,内部交叉验证相关系数（r）为0．9975,交叉验证均方差（RMSECV）为0．0835,外部验证均方差（RMSEP）为0．0865。结论建立的定量分析模型能对小儿复方磺胺甲嚷唑颗粒的水分含量进行准确、快速定量分析,方法简单可靠,可用于药品的现场快速分析。     

Differentiation and quantitative determination of surface and hydrate water in lyophilized mannitol using NIR spectroscopy

Mannitol hydrate is a metastable form produced during lyophilization. It is unstable, and therefore can undergo dehydration to release water to the surrounding environment at room temperature. The analysis of this form is challenging due to its thermodynamic instability. This study describes the development of a fast and non-invasive method to determine the mannitol hydrate and surface water content in a lyophilized product using near-infrared (NIR) spectroscopy. The mannitol hydrate was produced through lyophilization and characterized using XRPD, TGA, and NIR spectroscopy. Quantitative methods for hydrate and surface water were developed for NIR spectra with curve fitting and partial least square (PLS) regression models. The curve fitting method deconvoluted the NIR spectra into hydrate and surface water peaks and generated a calibration model by correlating pure spectra peak area to concentration. The standard error of prediction (SEP) for hydrate and surface water content were 0.65 and 0.40%, respectively. The PLS model developed for the same sample set was better than the curve fitting model; SEP = 0.50% for hydrate water and 0.22% for surface water, respectively. The methods can be used to monitor the formation and stability of mannitol hydrate in mannitol-containing formulations during the lyophilization process.     

Process analytical technology: chemometric analysis of Raman and near infra-red spectroscopic data for predicting physical properties of extended release matrix tablets

The purpose of this work was to develop a correlation between pharmaceutical properties such as hardness, porosity, and content with prediction models employed using Raman and near infra-red (NIR) spectroscopic methods. Metoprolol tartrate tablets were prepared by direct compression and wet granulation methods. NIR spectroscopy and chemical imaging, and Raman spectra were collected, and hardness, porosity, and dissolution were measured. The NIR PLS model showed a validated correlation coefficient of >0.90 for the predicted versus measured porosity, hardness, and amount of drug with raw and second derivative NIR spectra. Raman spectra correlated porosity of the tablets using raw data for directly compressed tablets and wet granulated tablets (r(2) > 0.90). A very close root-mean square error of calibration (RMSEC) and root-mean square error of prediction (RMSEP) values were found in all the cases indicating validity of the calibration models. Raman spectroscopy was used for the first time to predict physical quality attribute such as porosity successfully. Chemical imaging utilizing NIR detector also demonstrated to show physical changes due to compression differences. In conclusion, sensor technologies can be potentially used to predict physical parameters of the matrix tablets.     

Crystallinity evaluation of tacrolimus solid dispersions by chemometric analysis

Different destructive and nondestructive analytical methods, namely powder X-ray diffractometry (PXRD), differential scanning calorimetry (DSC), Raman and near-infrared (NIR) spectroscopy and imaging, to detect and characterize tacrolimus trace crystallinity in an amorphous solid dispersion (SD) using chemometric analysis were developed. The SD was spiked with different percentages of the crystalline drug to construct an array of SDs with different crystallinity percentages. Partial least square (PLS) regression analysis was employed to compare the performance of the calibration models created using these analytical methods. The obtained results indicated a significant interaction between tacrolimus and the employed polymer and a drug dissolution dependency on the crystalline fraction within the SDs. Using two PLS factors, these analytical methods were ranked according to its specificity to detect the trace crystallinity of SDs as NIR > PXRD > Raman > DSC. Through the application of PLS, root-mean-squared error of calibration values of 2.91%, 5.36%, 7.07% and 11.58% were calculated for the calibration models constructed by NIR, PXRD, Raman and DSC, respectively. Having a prediction error of 2.1% and a correlation coefficient of 0.99, it is demonstrated that combined NIR imaging and chemometric analysis outperformed the other methods in detecting trace crystallinity in tacrolimus amorphous systems. The spatial distributions of amorphous and crystalline drug were also obtained in order to allow for studying the crystallization dissemination in the solid dispersions. Consequently, NIR and NIR imaging coupled with chemometry was shown to be a powerful tool for the prediction of drug crystallinity within SDs.     

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。     

Acetaminophen determination in low-dose pharmaceutical syrup by NIR spectroscopy

The aim of the present study was first to develop a robust near infrared (NIR) calibration model able to determine the acetaminophen content of a low-dose syrup formulation (2%, w/v). Therefore, variability sources such as production campaigns, batches, API concentration, syrup basis, operators and sample temperatures were introduced in the calibration set. A prediction model was then built using partial least square (PLS) regression. First derivative followed by standard normal variate (SNV) were chosen as signal pre-processing. Based on the random subsets cross-validation, 4 PLS factors were selected for the prediction model. The method was then validated for an API concentration ranging from 16 to 24 mg/mL (1.6–2.4%, w/v) using an external validation set. The 0.26 mg/mL RMSEP suggested the global accuracy of the model. The accuracy profile obtained from the validation results, based on tolerance intervals, confirmed the adequate accuracy of the results generated by the method all over the investigated API concentration range. Finally, the NIR model was used to monitor in real time the API concentration while mixing syrups containing various amounts of API, a good agreement was found between the NIR method and the theoretical concentrations.     

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。     

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.     

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。     

硫酸羟氯喹颗粒水分近红外光谱在线定量模型的建立

目的 为实时检测硫酸羟氯喹颗粒在流化床干燥过程中的水分含量变化,建立颗粒水分的在线近红外光谱定量模型.方法 物料颗粒在流化床的干燥过程中,实时取样并用水分测定仪测量颗粒水分,采用多元散射校正(multiplicative signal correction,MSC)、一阶导数和Karl Norris平滑的光谱预处理方法...     

近红外光谱法测定头孢氨苄颗粒含量

目的 建立测定头孢氨苄颗粒含量的近红外光谱（NIR）快速分析方法。方法 以全国不同企业生产的187批头孢氨苄颗粒样品采集近红外光谱,分别建立校正集和检验集,校正集经内部交叉验证,建立校正模型,对检验集的样品进行分析。结果 头孢氨苄颗粒的浓度为0.042 0∽0.138 0 mg.mg 1,内部交叉验证决定系数（R2）为98.84,内部交叉验证均方差（RMSECV）为0.003,外部验证预测均方差（RMSEP）为0.003,预测值与真值的相关系数为0.995 0。结论 所建方法快速、简便、结果准确,可用于头孢氨苄颗粒的快速定量检验。     

近红外光谱法测定盐酸吡格列酮片的含量

目的：利用近红外光谱建立快速测定盐酸吡格列酮片含量的方法。方法：用HPLC法测定100批盐酸吡格列酮片样品含量并采集各样品的近红外光谱数据,从中随机抽取80批样品组成校正集,另20批样品组成测试集,采用偏最小二乘法（PLS）建立定量模型,并预测测试集样品的盐酸吡格列酮含量。结果：所建立定量模型的交叉验证测定系数r^2为0．9903,交叉验证均方差（RMSPCV）为0．394;测试集样品的测定系数r^2为0．9875,预测均方差（RMSEP）为0．259,平均预测偏差0．18％。结论：本法操作简便、快速、环保,可用于盐酸吡格列酮片的快速定量分析。