Background correction in near-infrared spectra of plant extracts by orthogonal signal correction

In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.

Wavelet-based classification and influence matrix analysis method for the fast discrimination of Chinese herbal medicines according to the geographical origins with near infrared spectroscopy

A discriminant analysis technique using wavelet transformation(WT)and influence matrixanalysis(CAIMAN)method is proposed for the near infrared(NIR)spectroscopy classifi-cation.In the proposed methodology,NIR spectra are decomposed by WT for data com-pression and a forward feature selection is further employed to extract the relevant informationfrom the wavelet coefficients,reducing both classification errors and model complexity.Adiscriminant-CAIMAN(D-CAIMAN)method is utilized to build the classification model inwavelet domain on the basis of reduced wavelet coefficients of spectral variables.NIR spectradata set of 265 salviae miltiorrhizae radia samples from 9 different geographical origins is usedas an example to test the classification performance of the algorithm.For a comparison,k-nearest neighbor(KNN),linear discriminant analysis(LDA)and quadratic discriminant analysis(QDA)methods are also employed.D-CAIMAN with wavelet-based feature selection(WD-CAIMAN)method shows the best performance,achieving the total classification rate of ioo%in both cross-validation set and prediction set.It is worth noting that the WD-CAIMANclassifier also shows improved sensitivity,selectivity and model interpretability in thecla.ssifications.

Simultaneous LC-MS/MS Determination of Danshensu and Paeoniflorin for Permeability Studies in Caco-2 Intestinal Absorption Model

A high sensitive method based on liquid chromatography tandem mass spectrometry（LC-MS/MS） was developed and validated for the study of permeability of danshensu（DS） and paeoniflorin（PF） in Caco-2 intestinal absorption model. The DS and PF were extracted from cell culture by vacuum-lyophilizing and then separated on a Zorbax Stable Bond C18 column with 0.1% acetic acid aqueous solution and methanol as mobile phase. Detection was carried out by negative electrospray ionization（ESI ） with selected reaction monitoring（SRM） mode. The apparent permeability coefficients（Papp） of DS and PF in Caco-2 cell medium were calculated and the effects of verapamil on the coefficients Papp of the two test compounds were also illustrated. The permeability of PF was much better than that of DS when the two compounds were administrated individually. Co-administration of DS and PF led to the decrease of the transport from apical side to basolateral side for both the compounds. However, the transport in the contrary direction were accelerated. It was also observed that verapamil could accelerate the transport of the test compounds from apical side to basolateral side. However, the absorption-enhanced effect of verapamil was attenuated when DS and PF were co-administrated. These observations suggest that both passive diffusion and active efflux involved in P-gp would effect the passage of DS and PF across Caco-2 cell monolayer. At the same time, the co-administration of DS and PF to an alteration of transport behavior, which suggests that the interaction must be taken into account when ‘n-in-one＇ samples were used in Caco-2 intestinal model.

Characterization of herbal powder blends homogeneity using near-infrared spectroscopy

Homogeneity of powder blend is essential to obtain uniform contents for the tablets and capsules.Near-infrared(NIR)spectroscopy with fiber-optic probe was used as an on-line technique for monitoring the homogeneity of pharmaceutical blend during the blending process instead of the traditional techniques,such as high performance liquid chromatograph(HPLC)method.In this paper NIRS with a SabIR difuse reflectance fiber optic probe was used to monitor the blending process of coptis powder and lactose(excipient)with different contents,and further qualitative methods,like similarity,moving block of standard deviation and mean square were used for calculation purposes with the ollected spectra after the pretreatment of multiplicative signal correction(MSC)and second derivative.Correlation spectrum was used for the wavelength selection.Four different coptis were blended with lactose separately to validate the proposed method,and the blending process of "liu wei di huang"pill was also simulated in bottles to verify this method on multiple herbal blends.The overall results suggest that NIRS is a simple,efective and noninvasive technique can be sucssfuly applied to the determination of homogeneity in the herbal blend.

In-situ monitoring of saccharides removal of alcohol precipitation using near-infrared spectroscopy

As unsafe components in herbal medicine(HM),saccharides can affect not only the drug appearance and stabilization,but also the drug efficacy and safety.The present study focuses on the in-line monitoring of batch alcohol precipitation processes for saccharide removal using nearinfrared(NIR)spectroscopy.NIR spectra in the 4000–10,000-cm^(-1)wavelength range are acquired in situ using a transflectance probe.These directly acquired spectra allow characterization of the dynamic variation tendency of saccharides during alcohol precipitation.Calibration models based on partial least squares(PLS)regression have been developed for the three saccharide impurities,namely glucose,fructose,and sucrose.Model errors are estimated as the root-meansquare errors of cross-validation(RMSECVs)of internal validation and root-mean-square errors of prediction(RMSEPs)of external validation.The RMSECV values of glucose,fructose,and sucrose were 1.150,1.535,and 3.067 mg·mL^(-1),and the RMSEP values were 0.711,1.547,and 3.740 mg·mL^(-1),respectively.The correlation coeffcients(r)between the NIR predictive and the reference measurement values were all above 0.94.Furthermore,NIR predictions based on the constructed models improved our understanding of sugar removal and helped develop a control strategy for alcohol precipitation.The results demonstrate that,as an alternative process analytical technology(PAT)tool for monitoring batch alcohol precipitation processes,NIR spectroscopy is advantageous for both efficient determination of quality characteristics(fast,in situ,and requiring no toxic reagents)and process stability,and evaluating the repeatability.

Screening and identification of multiple components in Tanreqing injection using RP-HPLC combined with DAD and ESI-TOF/MS

A liquid chromatography coupled with diode array detector(DAD) and electrospray ionization time-of-flight mass spectrometry(ESI-TOF/MS) method was developed for the screening and identification of the multiple components in Tanreqing injection, a well-known Chinese medicine injection in China. By combining the DAD spectrum and the accurate mass measurement of ESI-TOF/MS, twelve components in Tanreqing injection were identified. This study contributes to clarifying the nature of Tanreqing injection, and provides an effective and reliable process for the comprehensive and systematic characterization of complex traditional Chinese medicine preparations.

Manufacturer identification and storage time determination of “Dong'e Ejiao” using near infrared spectroscopy and chemometrics

We have developed a set of chemometric methods to address two critical issues in quality control of a precious traditional Chinese medicine （TCM）, Dong＇e Ejiao （DEE J）. Based on near infrared （NIR） spectra of multiple samples, the genuine manufacturer of DEE J, e.g. Dong＇e Ejiao Co., Ltd., was accurately identified among 21 suppliers by the fingerprint method using Hotelling T2, distance to Model X （DModX）, and similarity match value （SMV） as dis- criminate criteria. Soft independent modeling of the class analogy algorithm led to a misjudgment ratio of 6.2%, suggesting that the fingerprint method is more suitable for manufacturer identification. For another important feature related to clinical efficacy of DEE J, storage time, the partial least squares-discriminant analysis （PLS-DA） method was applied with a satisfactory misjudgment ratio （15.6%） and individual prediction error around 1 year. Our results demonstrate that NIR spectra comprehensively reflect the essential quality information of DEE J, and with the aid of proper chemometric algorithms, it is able to identify genuine manufacturer and determine accurate storage time. The overall results indicate the promising potential of NIR spectroscopy as an effective quality control tool for DEEJ and other precious TCM products.

An approach to optimize the batch mixing process for improving the quality consistency of the products made from traditional Chinese medicines

The efficacy of traditional Chinese medicine(TCM)is based on the combined effects of its constituents.Variation in chemical composition between batches of TCM has always been the deterring factor in achieving consistency in efficacy.The batch mixing process can significantly reduce the batch-to-batch quality variation in TCM extracts by mixing them in a well-designed proportion.However,reducing the quality variation without sacrificing too much of the production efficiency is one of the challenges.Accordingly,an innovative and practical batch mixing method aimed at providing acceptable efficiency for industrial production of TCM products is proposed in this work,which uses a minimum number of batches of extracts to meet the content limits.The important factors affecting the utilization ratio of the extracts(URE)were studied by simulations.The results have shown that URE was affected by the correlation between the contents of constituents,and URE decreased with the increase in the number of targets and the relative standard deviations of the contents.URE could be increased by increasing the number of storage tanks.The results have provided a reference for designing the batch mixing process.The proposed method has possible application value in reducing the quality variation in TCM and providing acceptable production efficiency simultaneously.

Optimization of micellar electrokinetic capillary chromatography method using central composite design for the analysis of components in Yangwei granule

Central composite design(CCD),together with multiple linear regression,was successfully used to optimize the electrophoretic buffer system of micellar electrokinetic capillary chromatography(MEKC) for the determination of albiflorin,paeoniflorin,liquiritin,and glycyrrhizic acid in the traditional Chinese medicine(TCM) prescription,Yangwei granule.Concentrations of sodium deoxycholate(SDC) and borate,and proportions of ammonia,acetonitrile,and methanol were optimized.The total resolutions of peaks between the analytes and their adjacent peaks in real samples were integrated into the evaluation index of separation efficiency.The optimum electrophoretic buffer contained 80 mmol/L SDC,20 mmol/L borate,5%(v/v) methanol,0.5%(v/v) ammonia,and 5%(v/v) acetonitrile.The correlation coefficients(R 2 ) between the peak areas and the corresponding concentrations of analytes were greater than 0.9956.The limits of detection(LODs) (S/N=3) of the analytes were 0.97-4.00μg/ml.The results indicate the superiority of CCD in optimizing the separation conditions of complex samples such as TCM prescriptions.

Rapid quantification of multi-components in alcohol precipitation liquid of Codonopsis Radix using near infrared spectroscopy(NIRS)

A near infrared spectroscopy（NIRS） approach was established for quality control of the alcohol precipitation liquid in the manufacture of Codonopsis Radix. By applying NIRS with multivariate analysis, it was possible to build variation into the calibration sample set, and the Plackett-Burman design, Box-Behnken design, and a concentrating-diluting method were used to obtain the sample set covered with sufficient fluctuation of process parameters and extended concentration information. NIR data were calibrated to predict the four quality indicators using partial least squares regression（PLSR）. In the four calibration models, the root mean squares errors of prediction（RMSEPs） were 1.22 μg/ml, 10.5 μg/ml, 1.43 μg/ml, and 0.433% for lobetyolin, total flavonoids, pigments, and total solid contents, respectively. The results indicated that multi-components quantification of the alcohol precipitation liquid of Codonopsis Radix could be achieved with an NIRS-based method, which offers a useful tool for real-time release testing（RTRT） of intermediates in the manufacture of Codonopsis Radix.