Monitoring batch-to-batch reproducibility of liquid–liquid extraction process using in-line near-infrared spectroscopy combined with multivariate analysis

Dual-spectrum online monitoring of puerarin and total flavonoids contents during the extraction process of Pueraria lobata

In the extraction process of traditional Chinese medicine (TCM), the rapid detection of the active components of TCM plays a vital role. We pioneered the choice of dual-spectrum technology based on near-infrared (NIR) and ultraviolet-visible (UV-Vis) spectra and applied it to the quantitative monitoring of online extraction of puerarin and total flavonoids from Pueraria lobata. An online monitoring system was constructed to fulfill the dual-spectrum online monitoring. The extraction solution flows from a flask into a pipeline under the control of a peristaltic pump, removing the solid residues contained in the solution with filters. When the extraction solution flows into the cuvette, it will be measured with NIR and UV-Vis portable spectrometers. After finishing the spectra collection, the peristaltic pump will reverse to push the extraction solution back to the flask. The partial least squares (PLS) models were established to quantitatively analyze puerarin and total flavonoids by NIR, UV-Vis, and dual-spectrum data. As for NIR, UV-Vis and dual-spectrum, the RMSEP (mg/mL) of the puerarin models were 0.0803, 0.0500, and 0.0351, and those of the total flavonoids models were 0.4373, 0.2402, and 0.1795, respectively. The results show that the dual-spectrum online monitoring system is simple, stable and fast (a few seconds) in operation, and accurate in prediction. Compared with individual NIR or UV-Vis spectra, the dual-spectra contains more comprehensive information, and the prediction ability is stronger. This method is expected to become a promising tool for the quantitative analysis of other traditional Chinese medicines.

Self-organizing maps-based generalized feature set selection for model adaption without reference data for batch process

When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios.

A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets

OBJECTIVES

This study is aimed to explore the blending process of Dahuang soda tablets. These are composed of two active pharmaceutical ingredients (APIs, emodin and emodin methyl ether) and four kinds of excipients (sodium bicarbonate, starch, sucrose, and magnesium stearate). Also, the objective is to develop a more robust model to determine the blending end-point.

METHODS

Qualitative and quantitative methods based on near-infrared (NIR) spectroscopy were established to monitor the homogeneity of the powder during the blending process. A calibration set consisting of samples from 15 batches was used to develop two types of calibration models with the partial least squares regression (PLSR) method to explore the influence of density on the model robustness. The principal component analysis-moving block standard deviation (PCA-MBSD) method was used for the end-point determination of the blending with the process spectra.

RESULTS

The model with different densities showed better prediction performance and robustness than the model with fixed powder density. In addition, the blending end-points of APIs and excipients were inconsistent because of the differences in the physical properties and chemical contents among the materials of the design batches. For the complex systems of multi-components, using the PCA-MBSD method to determine the blending end-point of each component is difficult. In these conditions, a quantitative method is a more suitable alternative.

CONCLUSIONS

Our results demonstrated that the effect of density plays an important role in improving the performance of the model, and a robust modeling method has been developed.

A Rapid High Throughput Vibration and Vortex-Assisted Matrix Solid Phase Dispersion for Simultaneous Extraction of Four Isoflavones for Quality Evaluation of Semen Sojae Praeparatum

Isoflavones (daidzein, daidzin, genistein and genistin) were main chemical components and usually selected as markers for quality control of Traditional Chinese Medicine semen sojae praeparatum (SSP). High throughput vibration and vortex-assisted matrix solid phase dispersion and high performance liquid chromatography with diode array detection were developed to simultaneously extract and quantify four isoflavones in SSP. Some parameters influencing extraction efficiency of isoflavones by vortex-assisted matrix solid phase dispersion such as sorbent type, ratio of sample to sorbent, crushing time, vibration frequency, methanol concentration, eluting solvent volume and vortex time were optimized. It was found that the best extraction yields of four isoflavones were obtained when the sample (20 mg) and SBA-3 (40 mg) was crushed by ball mill machine for 2 min at vibration frequency of 800 times per minute. Methanol/water (1.5 ml, 8:2, v/v) solution was dropped into the treated sample and vortexed for 3 min. The recoveries of the four isoflavones ranged from 86.1 to 94.8% and all relative standard deviations were less than 5%. A good linearity (r > 0.9994) was achieved within the range 0.5-125 μg/ml. It was concluded that the high throughput vibration and vortex-assisted matrix solid-phase dispersion coupled with high performance liquid chromatography was user-friendly extraction and quantification method of multiple isoflavones for quality evaluation of SSP.

Combining convolutional neural networks and on-line Raman spectroscopy for monitoring the Cornu Caprae Hircus hydrolysis process

Cornu Caprae Hircus (goat horn, GH) is one of the frequently used medicinal animal horns in traditional Chinese medicine (TCM). Hydrolysis is one of the key steps for GH pretreatment in pharmaceutical manufacturing. However, the physicochemical complexity of the hydrolysis samples imposes a challenge for hydrolysis process analysis and monitoring. In this study, convolutional neural networks (CNNs), one of the most popular deep learning methods, were used to develop quantitative calibration models based on on-line Raman spectroscopy for monitoring the GH hydrolysis process. Partial least squares (PLS) calibration models were also developed for model performance comparison. For CNN modeling, raw Raman spectra were used as inputs and hyperparameters in the CNN structure were optimized. Results show for four of the seven analytes, the optimized CNN models using raw spectra as inputs outperform the optimized PLS models developed with preprocessed spectra. Therefore, compared with the commonly used PLS algorithm, CNN modeling is also a practicable regression method and can be employed for the analytical purpose of this study. Models with better performance are expected to be obtained by improving the CNN model structure and using more effective hyperparameter optimization approaches in further studies. To the best of our knowledge, this is the first reported case study of combining CNNs and on-line Raman spectroscopy for a regression task.

Real-time process quality control of ramulus cinnamomi by critical quality attribute using microscale thermophoresis and on-line NIR

Real-time process quality control of ramulus cinnamomi (cassia twig) is still a challenge in pharmaceutical industry. Rapid critical quality attribute (CQA) determination of ramulus cinnamomi is essential for quality control. Microscale thermophoresis (MST) was used to investigate the CQA of ramulus cinnamomi by the interaction with biomacromolecule. There was a good affinity between cinnamaldehyde and human serum albumin (HSA) with K_a as 2.1722×10³mol/L. It was an excellent combination of similarity to ibuprofen with same binding force as discovered as hydrogen bond and van der Waals force. Furthermore, regarding cinnamaldehyde as CQA, on-line near-infrared was used to monitor pilot extraction process of ramulus cinnamomi combined with high performance liquid chromatography (HPLC). Quantitative model was established with R_pre² as 0.9798 and RMSECV as 0.0993, suggesting the NIR model was so robust and accurate for pilot process quality control. This method provided a perfect guideline for rapid CQA determination and real-time process quality control of Chinese materia medica (CMM) based on a vital CQA.

Evaluation of a multiple and global analytical indicator of batch consistency: traditional Chinese medicine injection as a case study

This paper evaluates a multiple and global analytical indicator of batch consistency in traditional Chinese medicine injections (TCMIs) via a chemometrics tool, which is more comprehensive to appraise quality consistency of different batches of injections than the traditional method of fingerprint similarity. A commonly used TCMI, Salviae miltiorrhizae and ligustrazine hydrochloride injection (SLI), was employed as a model. With the aid of a chemometrics tool (principal component analysis, PCA), evaluation of multiple and global analytical indicators of batch consistency, which included saccharides, phenolic acids and inorganic salts (18 indicators in total), was carried out to appraise the quality consistency of 13 batches of injection provided by the Guizhou Baite Pharmaceutical Co., Ltd. (Guizhou, China). Compared with the traditional HPLC-UV fingerprint similarity evaluation, the method proposed in the paper can more comprehensively and correctly reflect the quality consistency of different batches of injections. In this paper, the multi-index evaluation result showed poor batch consistency, which was more consistent with the determination results, while the fingerprint similarity evaluation results still showed good batch consistency. The HPLC-UV fingerprint reflects only substances with UV absorption, but it is not able to reflect substances without UV absorption or weak UV absorption, which leads to inappropriate conclusions. Therefore, quality consistency of injections can be effectively appraised by evaluation of multiple and global analytical indicators, instead of HPLC-UV fingerprint only. For visualizing the batch consistency of the multiple and global analytical indicators, a heat map was used to represent the fluctuation. Furthermore, critical indicator identification was also applied to select several indicators that should be paid more attention during the process of quality control of injection. And the analysis result showed that Na⁺, fructose (Fru), glucose (Glc), manninotriose (Man), danshensu (DSS) and salvianolic acid B (SAB) are the indicators that should be given more attention when controlling the quality of injections, also called critical quality control indicators. The proposed method provides a reference for the quality control of TCMIs and has broad application potential.

Evaluation of a multiple and global analytical indicator of batch consistency was employed in TCMIs with a multivariate statistical tool.

Critical pharmaceutical process identification considering chemical composition, biological activity, and batch-to-batch consistency: A case study of notoginseng total saponins

Objective

Critical pharmaceutical process identification (CPPI) is an important step in the implementation of quality by design concept to traditional Chinese medicines (TCMs). Risk assessment methods are usually used in CPPI. However, risk evaluation is usually subjective. The purpose of this work is to present a more objective CPPI method.

Methods

A CPPI method considering chemical composition, biological activity, and batch-to-batch consistency was presented in this work. The manufacturing process of notoginseng total saponins (NTS) was investigated as an example. The changes of chemical composition, biological activity, and chemical composition consistency after main processes were measured and compared. A significant change of them indicated a critical process.

Results

After extraction process and chromatography process, saponin purity and chemical composition similarity remarkably increased, and saponin content variations decreased. Thrombin inhibitory activity was remarkably decreased after chromatography process. Because of the large influences on NTS quality, extraction process and chromatography process were identified to be critical processes of NTS.

Conclusion

Based on a comprehensive and objective examination of the role of each process, critical pharmaceutical processes can be identified. A similar method can also be applied to other TCM processes.

Process Pattern-Based Near-Infrared Spectroscopy (NIRS) Fault Detection Using a Potential Function

This paper proposes a near-infrared (NIR) fault detection technology based on a process pattern via a potential function. Near-infrared spectroscopy is used to acquire process information at the molecular level. In this study, the process pattern concept is first introduced in the field of process control and a process pattern construction method based on elastic net-PCA is put forth. Next, the potential function discriminant method is applied to distinguish and classify the constructed process pattern and identify the running state of the industrial system. Finally, the proposed method is verified and analyzed using spectra data of the crude oil desalination and dehydration process. Compared with existing fault detection methods, the proposed approach offers the following advantages: (1) potential function discrimination achieves nonlinear process classification with better fault detection accuracy and good visualization performance; (2) fault detection based on NIR spectra is faster with and possesses greater accuracy because it acquires process information from a microscopic molecular perspective; and (3) the process pattern contains more effective process information and can more comprehensively characterize the essential features of processes.

From quality markers to data mining and intelligence assessment: A smart quality-evaluation strategy for traditional Chinese medicine based on quality markers

BACKGROUND

The quality of traditional Chinese medicine (TCM) forms the foundation of its clinical efficacy. The standardization of TCM is the most important task of TCM modernization. In recent years, there has been great progress in the quality control of TCM. However, there are still many issues related to the current quality standards, and it is difficult to objectively evaluate and effectively control the quality of TCM.

PURPOSE

To face these challenge, we summarized the current quality marker (Q-marker) research based on its characteristics and benefits, and proposed a reasonable and intelligentized quality evaluation strategy for the development and application of Q-markers.

METHODS

Ultra-performance liquid chromatography-quadrupole/time-of-flight with partial least squares-discriminant analysis was suggested to screen the chemical markers from Chinese medicinal materials (CMM), and a bioactive-guided evaluation method was used to select the Q-markers. Near-infrared spectroscopy (NIRS), based on the distinctive wavenumber zones or points from the Q-markers, was developed for its determination. Then, artificial intelligence algorithms were used to clarify the complex relationship between the Q-markers and their integral functions. Internet and mobile communication technology helped us to perform remote analysis and determine the information feedback of test samples.

CHAPTERS

The quality control research, evaluation, standard establishment and quality control of TCM must be based on the systematic analysis of Q-markers to study and describe the material basis of TCM efficacy, define the chemical markers in the plant body, and understand the process of herb drug acquisition, change and transmission laws affecting metabolism and exposure. Based on the advantages of chemometrics, new sensor technologies, including infrared spectroscopy, hyperspectral imaging, chemical imaging, electronic nose and electronic tongue, have become increasingly important in the quality evaluation of CMM. Inspired by the concept of Q-marker, the quantitation can be achieved with the help of artificial intelligence, and these subtle differences can be discovered, allowing the quantitative analysis by NIRS and providing a quick and easy detection method for CMM quality evaluations.

CONCLUSION

The concept of Q-markers focused on unique CMM differences, dynamic changes and their transmission and traceability to establish an overall quality control and traceability system. Based on the basic attributes, an integration model and artificial intelligence research path was proposed, with the hope of providing new ideas and perspectives for the TCM quality management.

Near infrared spectroscopy based monitoring of extraction processes of raw material with the help of dynamic predictive modeling

The control of batch-to-batch quality variations remains a challenging task for pharmaceutical industries, e.g., traditional Chinese medicine (TCM) manufacturing. One difficult problem is to produce pharmaceutical products with consistent quality from raw material of large quality variations. In this paper, an integrated methodology combining the near infrared spectroscopy (NIRS) and dynamic predictive modeling is developed for the monitoring and control of the batch extraction process of licorice. With the spectra data in hand, the initial state of the process is firstly estimated with a state-space model to construct a process monitoring strategy for the early detection of variations induced by the initial process inputs such as raw materials. Secondly, the quality property of the end product is predicted at the mid-course during the extraction process with a partial least squares (PLS) model. The batch-end-time (BET) is then adjusted accordingly to minimize the quality variations. In conclusion, our study shows that with the help of the dynamic predictive modeling, NIRS can offer the past and future information of the process, which enables more accurate monitoring and control of process performance and product quality.

Combining near infrared spectroscopy with predictive model and expertise to monitor herb extraction processes

Albeit extensively utilized, herb extraction process (HEP) is hard to be monitored because of its batch nature and the fluctuating quality of raw materials. Process analytical tools like near infrared spectroscopy (NIRS) can offer nondestructive examinations and collect abundant data of the process, which in principle contain the information about the quality of both the product and the process itself. However, extra effort is often required for the data mining of such process measurements, and extracting knowledge of the quality of process can be even harder. In this study, we take the extraction process of licorice as a typical HEP instance, and combine NIRS with classical partial least squared regression (PLSR) and expertise for its on-line monitoring. We show that our scheme effectively extracts information with clear physical meanings, through which we can even uncover the process fault that does not induce evident abnormalities in the product quality. Moreover, the constructed model can continuously evolve with more process data from daily operations, and the idea of the whole framework can be directly generalized to other HEP.

Near-Infrared Spectroscopy Assay of Key Quality-Indicative Ingredients of Tongkang Tablets

The objective of this paper is to develop an easy and fast near-infrared spectroscopy (NIRS) assay for the four key quality-indicative active ingredients of Tongkang tablets by comparing the true content of the active ingredients measured by high performance liquid chromatography (HPLC) and the NIRS data. The HPLC values for the active ingredients content of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin in Tongkang tablets were set as reference values. The NIRS raw spectra of Tongkang tablets were processed using first-order convolution method. The iterative optimization method was chosen to optimize the band for Cimicifuga glycoside and 5-O-methylvisamminol, and correlation coefficient method was used to determine the optimal band of calycosin glucoside and hesperidin. A near-infrared quantitative calibration model was established for each quality-indicative ingredient by partial least-squares method on the basis of the contents detected by HPLC and the obtained NIRS spectra. The correlation coefficient R ² values of the four models of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin were 0.9025, 0.8582, 0.9250, and 0.9325, respectively. It was demonstrated that the accuracy of the validation values was approximately 90% by comparison of the predicted results from NIRS models and the HPLC true values, which suggested that NIRS assay was successfully established and validated. It was expected that the quantitative analysis models of the four indicative ingredients could be used to rapidly perform quality control in industrial production of Tongkang tablets.

Rapid measurement of epimedin A, epimedin B, epimedin C, icariin, and moisture in Herba Epimedii using near infrared spectroscopy

In this work, near infrared (NIR) spectroscopy was used in combination with chemometrics to determine the epimedin A, epimedin B, epimedin C, icariin, and moisture contents of Herba Epimedii. The variable selection method genetic algorithm (GA) and regression tool support vector machine (SVM) were used to improve the model performance. Four different calibration models, namely Full-PLS, GA-PLS, Full-SVM, and GA-SVM, were established, and their performances in terms of prediction accuracy and model robustness were systemically studied and compared. In conclusion, the performances of the models based on the efficient variables selected through GA were better than those based on full spectra, and the nonlinear models were superior over the linear models. In addition, the GA-SVM model demonstrated the optimal performance in predicting five quality parameters (viz. epimedin A, epimedin B, epimedin C, icariin, and moisture). For GA-SVM, the determination coefficient (R_p²), root-mean-square error (RMSEP), and residual predictive deviation (RPD) for the prediction set were 0.9015, 0.0268%, and 2.20 for epimedin A; 0.9089, 0.0656%, and 3.08 for epimedin B; 0.9056, 0.1787%, and 3.18 for epimedin C; 0.8192, 0.0657%, and 2.26 for icariin; and 0.9367, 0.2062%, and 4.12 for moisture, correspondingly. Results indicated that NIR spectroscopy coupled with GA-SVM calibration can be used as a reliable alternative strategy to measure the epimedin A, epimedin B, epimedin C, icariin, and moisture contents of Herba Epimedii because this technique is fast, economic, and nondestructive compared with traditional chemical methods.

LC-MS/MS analysis and pharmacokinetic study on five bioactive constituents of Tanreqing injection in rats

Tanreqing injection (TRQ), a well-known traditional Chinese medicine formula, is commonly used to treat respiratory diseases. In the present study, a rapid, selective, and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated to simultaneously determinate the plasma contents of 5 major constituents of TRQ, including chlorogenic acid (CHA), caffeic acid (CFA), baicalin (BA), ursodeoxycholic acid (UDCA) and chenodeoxycholic acid (CDCA) in rats after intravenous administration of TRQ. Chromatographic separation was performed on an Agilent Zorbax SB-C₁₈ column (3.5 μm, 100 mm × 2.1 mm), with acetonitrile and 0.1% aqueous formic acid as mobile phase at a flow rate of 0.3 mL·min^-1. The calibration curves were linear over the ranges of 27.0-13 333.0 ng·mL^-1 for CFA, 30.0-14 933.0 ng·mL^-1 for CHA, 50.0-50 333.0 ng·mL^-1 for BA, 550.0-55 000.0 ng·mL^-1 for UDCA, and 480.0-48 000.0 ng·mL^-1 for CDCA, respectively. Intra- and inter-day precisions (relative standard deviations, RSDs) were from 3.11% to 14.08%. The extraction recoveries were greater than 71% and accuracy (relative recovery) was from 89% to 137% for all analytes, except endogenous bile acids. This validated method was successfully applied to the first pharmacokinetic study of CFA, CHA, BA, UDCA and CDCA in rat plasma after intravenous administration of TRQ.

Determination of total organic carbon and soluble solids contents in Tanreqing injection intermediates with NIR spectroscopy and chemometrics

Near infrared spectroscopy combined with chemometrics was investigated for the fast determination of total organic carbon (TOC) and soluble solids contents (SSC) of Tanreqing injection intermediates. The NIR spectra were collected in transflective mode, and the TOC and SSC reference values were determined with Multi N/C UV HS analyzer and loss on drying method. The samples were divided into calibration sets and validation sets using the Kennard-Stone (KS) algorithm. The Dixon test, leverage and studentized residual test were studied for the sample outlier analysis. The selection of wavebands, spectra pretreated method and the number of latent variables were optimized to obtain better results. The quantitative calibration models were established with 3 different PLS regression algorithms, named linear PLS, non-linear PLS and concentration weighted PLS, and the net result was defined as the average of the predicted values of the different calibration models. The overall results indicated that the presented method is more powerful than single multivariable regression method, characterized by higher mean recovery rate (MRR) of the validation set, and can be used for the rapid determination of TOC and SSC values of Tanreqing injection intermediates, which are two important quality indicators for the process monitoring.

Online NIR Analysis and Prediction Model for Synthesis Process of Ethyl 2-Chloropropionate

Online near-infrared spectroscopy was used as a process analysis technique in the synthesis of 2-chloropropionate for the first time. Then, the partial least squares regression (PLSR) quantitative model of the product solution concentration was established and optimized. Correlation coefficient (R²) of partial least squares regression (PLSR) calibration model was 0.9944, and the root mean square error of correction (RMSEC) was 0.018105 mol/L. These values of PLSR and RMSEC could prove that the quantitative calibration model had good performance. Moreover, the root mean square error of prediction (RMSEP) of validation set was 0.036429 mol/L. The results were very similar to those of offline gas chromatographic analysis, which could prove the method was valid.

On-line quantitative monitoring of liquid-liquid extraction of Lonicera japonica and Artemisia annua using near-infrared spectroscopy and chemometrics

BACKGROUND

Liquid-liquid extraction of Lonicera japonica and Artemisia annua (JQ) plays a significant role in manufacturing Reduning injection. Many process parameters may influence liquid-liquid extraction and cause fluctuations in product quality.

OBJECTIVE

To develop a near-infrared (NIR) spectroscopy method for on-line monitoring of liquid-liquid extraction of JQ.

MATERIALS AND METHODS

Eleven batches of JQ extraction solution were obtained, ten for building quantitative models and one for assessing the predictive accuracy of established models. Neochlorogenic acid (NCA), chlorogenic acid (CA), cryptochlorogenic acid (CCA), isochlorogenic acid B (ICAB), isochlorogenic acid A (ICAA), isochlorogenic acid C (ICAC) and soluble solid content (SSC) were selected as quality control indicators, and measured by reference methods. NIR spectra were collected in transmittance mode. After selecting the spectral sub-ranges, optimizing the spectral pretreatment and neglecting outliers, partial least squares regression models were built to predict the content of indicators. The model performance was evaluated by the coefficients of determination (R (2)), the root mean square errors of prediction (RMSEP) and the relative standard error of prediction (RSEP).

RESULTS

For NCA, CA, CCA, ICAB, ICAA, ICAC and SSC, R (2) was 0.9674, 0.9704, 0.9641, 0.9514, 0.9436, 0.9640, 0.9809, RMSEP was 0.0280, 0.2913, 0.0710, 0.0590, 0.0815, 0.1506, 1.167, and RSEP was 2.32%, 4.14%, 3.86%, 5.65%, 7.29%, 6.95% and 4.18%, respectively.

CONCLUSION

This study demonstrated that NIR spectroscopy could provide good predictive ability in monitoring of the content of quality control indicators in liquid-liquid extraction of JQ.

A comparative study of using in-line near-infrared spectra, ultraviolet spectra and fused spectra to monitor Panax notoginseng adsorption process

The step of enriching and purifying saponins by macroporous resin column chromatography is closely related to the safety and efficacy of Panax notoginseng products during their manufacturing processes. Adsorption process is one of the most critical unit operations within each chromatographic cycle. In order to understand the adsorption process directly, it is necessary to develop a rapid and precise method to monitor the adsorption process in real time. In this study, comparative evaluation of using near-infrared (NIR) spectra, ultraviolet (UV) spectra and fused spectra to monitor the adsorption process of P. notoginseng was conducted. The uninformative variable elimination by partial least squares (UVE-PLS) regression models were established for quantification of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1 and ginsenoside Rd in effluents based on different spectra. There was a significant improvement provided by the models based on fused spectra. The results in this work were conducive to solving the problems about real-time quantitative analysis of saponins during P. notoginseng adsorption. The fusion method of NIR and UV spectra combined with UVE-PLS regression could be a promising strategy to real-time analyze the components, which are difficult to be quantified by individual spectroscopic technique.

Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint

Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.

Application of near-infrared spectroscopy combined with multivariate analysis in monitoring of crude heparin purification process

Ion-exchange chromatography is a widely used purification technology in the heparin manufacturing process. To improve the efficiency and understand the process directly, a rapid and equally precise method needs to be developed to measure heparin concentration in chromatography process. Here, two robust partial least squares regression (PLS-R) models were established for quantification of heparin based on the near-infrared (NIR) spectroscopy with 80 samples of adsorption process and 76 samples of elution process. Several variables selection algorithms, including correlation coefficient method, successive projection algorithm (SPA) and interval partial least squares (iPLSs), were performed to remove non-informative variables. The results showed that the correlation coefficient of validation (Rp) and the residual predictive deviation (RPD) corresponded to 0.957 and 3.4472 for adsorption process, 0.968 and 3.9849 for elution process, respectively. The approach was found considerable potential for real-time monitoring the heparin concentration of chromatography process.