Pixel-Level Recognition of Trace Mycotoxins in Red Ginseng Based on Hyperspectral Imaging Combined with 1DCNN-Residual-BiLSTM-Attention Model.

Red ginseng is widely used in food and pharmaceuticals due to its significant nutritional value. However, during the processing and storage of red ginseng, it is susceptible to grow mold and produce mycotoxins, generating security issues. This study proposes a novel approach using hyperspectral imaging technology and a 1D-convolutional neural network-residual-bidirectional-long short-term memory attention mechanism (1DCNN-ResBiLSTM-Attention) for pixel-level mycotoxin recognition in red ginseng. The "Red Ginseng-Mycotoxin" (R-M) dataset is established, and optimal parameters for 1D-CNN, residual bidirectional long short-term memory (ResBiLSTM), and 1DCNN-ResBiLSTM-Attention models are determined. The models achieved testing accuracies of 98.75%, 99.03%, and 99.17%, respectively. To simulate real detection scenarios with potential interfering impurities during the sampling process, a "Red Ginseng-Mycotoxin-Interfering Impurities" (R-M-I) dataset was created. The testing accuracy of the 1DCNN-ResBiLSTM-Attention model reached 96.39%, and it successfully predicted pixel-wise classification for other unknown samples. This study introduces a novel method for real-time mycotoxin monitoring in traditional Chinese medicine, with important implications for the on-site quality control of herbal materials.

An effective and comprehensive optimization strategy for preparing Ginkgo biloba leaf extract enriched in shikimic acid by macroporous resin column chromatography.

INTRODUCTION: Previously reported preparation methods of Ginkgo biloba leaf extract (EGBL) have mainly focused on the enrichment of flavonoid glycosides (FG) and terpene trilactones (TT), which led to the underutilization of G. biloba leaves (GBL). OBJECTIVES: To make full use of GBL, in this study, a comprehensive optimization strategy for preparing EGBL by macroporous resin column chromatography was proposed and applied to enrich FG, TT, and shikimic acid (SA) from GBL. METHODOLOGY: Initially, the static adsorption and desorption were executed to select suitable resin. Then, the influences of solution pH were investigated by the static and dynamic adsorption. Subsequently, eight process parameters were systematically investigated via a definitive screening design (DSD). After verification experiments, scale-up enrichment was carried out, investigating the feasibility of the developed strategy for application on an industrial scale. RESULTS: It was found that XDA1 was the most appropriate adsorbent for the preparation of EGBL at solution pH 2.0. Furthermore, based on the constraints of the desired quality attributes, the optimized ranges of operating parameters were successfully acquired, and the verification experiments demonstrated the accuracy and reliability of using DSD to investigate the chromatography process for the preparation of EGBL. Finally, magnified experiments were successfully performed, obtaining the EGBL containing 26.54% FG, 8.96% TT, and 10.70% SA, which reached the SA level of EGB761, an international standard EGBL. CONCLUSION: The present study not only provided an efficient and convenient approach for the preparation of EGBL enriched in SA but also accelerated efforts to high-value utilization of GBL.

Preparation of Compound Salvia miltiorrhiza-Blumea balsamifera Nanoemulsion Gel and Its Effect on Hypertrophic Scars in the Rabbit Ear Model.

Hypertrophic scars (HS) still remain an urgent challenge in the medical community. Traditional Chinese medicine (TCM) has unique advantages in the treatment of HS. However, due to the natural barrier of the skin, it is difficult for the natural active components of TCM to more effectively penetrate the skin and exert therapeutic effects. Therefore, the development of an efficient drug delivery system to facilitate enhanced transdermal absorption of TCM becomes imperative for its clinical application. In this study, we designed a compound Salvia miltiorrhiza-Blumea balsamifera nanoemulsion gel (CSB-NEG) and investigated its therapeutic effects on rabbit HS models. The prescription of CSB-NEG was optimized by single-factor, pseudoternary phase diagram, and central composite design experiments. The results showed that the average particle size and PDI of the optimized CSB-NE were 46.0 ± 0.2 nm and 0.222 ± 0.004, respectively, and the encapsulation efficiency of total phenolic acid was 93.37 ± 2.56%. CSB-NEG demonstrated excellent stability and skin permeation in vitro and displayed a significantly enhanced ability to inhibit scar formation compared to the CSB physical mixture in vivo. After 3 weeks of CSB-NEG treatment, the scar appeared to be flat, pink, and flexible. Furthermore, this treatment also resulted in a decrease in the levels of the collagen I/III ratio and TGF-ß1 and Smad2 proteins while simultaneously promoting the growth and remodeling of microvessels. These findings suggest that CSB-NEG has the potential to effectively address the barrier properties of the skin and provide therapeutic benefits for HS, offering a new perspective for the prevention and treatment of HS.

Analytical quality by design based on knowledge organization: A case study of developing an ultrahigh-performance liquid chromatography method for the detection of phenolic compounds.

INTRODUCTION: Despite numerous successful cases, there are still some challenges in using analytical quality by design (AQbD) for the development of analytical methods. Knowledge organization helps to enhance the objectivity of risk assessment, reduce the number of preliminary exploratory experiments, identify potential critical method parameters (CMPs) and their scope. OBJECTIVE: In the present study, we aimed to develop a simple, rapid, and robust analytical method for detecting phenolic compounds in Xiaochaihu capsule intermediates utilizing knowledge organization. METHODS: Knowledge organization and AQbD were combined to obtain the initial analytical conditions through knowledge collection, extraction, reorganization, and analysis. The quantitative relationship between critical method attributes (CMAs) and CMPs was then established by a definitive screening design. The method operable design region was calculated using an exhaustive Monte Carlo approach based on the probability of reaching the standard. Robustness investigation and methodological validation were finally performed. RESULTS: Analytical target profiles, CMAs, potential CMPs, and initial analytical conditions were initially identified, and the optimized ranges of operating parameters were obtained. A UHPLC method was successfully established for the analysis of phenolic compounds in ginger-ginger pinellia percolate, and the method validation outcomes were also satisfactory. CONCLUSION: The developed method can be a reliable means to detect the phenolic compounds of Xiaochaihu capsule intermediates. Knowledge organization provides a new approach for making better use of prior knowledge, significantly enhancing the efficiency of analytical method development. The approach is versatile and can be similarly applied to the development of other methods.

Improving the geographical origin classification of Radix glycyrrhizae (licorice) through hyperspectral imaging assisted by U-Net fine structure recognition.

Radix glycyrrhizae (licorice) is extensively employed in traditional Chinese medicine, and serves as a crucial raw material in industries such as food and cosmetics. The quality of licorice from different origins varies greatly, so classification of its geographical origin is particularly important. This study proposes a technique for fine structure recognition and segmentation of hyperspectral images of licorice using deep learning U-Net neural networks to segment the tissue structure patterns (phloem, xylem, and pith). Firstly, the three partitions were separately labeled using the Labelme tool, which was utilized to train the U-Net model. Secondly, the obtained optimal U-Net model was applied to predict three partitions of all samples. Lastly, various machine learning models (LDA, SVM, and PLS-DA) were trained based on segmented hyperspectral data. In addition, a threshold method and a circumcircle method were applied to segment licorice hyperspectral images for comparison. The results revealed that compared with the threshold segmentation method (which yielded SVM classifier accuracies of 99.17%, 91.15%, and 92.50% on the training set, validation set, and test set, respectively), the U-Net segmentation method significantly enhanced the accuracy of origin classification (99.06%, 94.72% and 96.07%). Conversely, the circumcircle segmentation method did not effectively improve the accuracy of origin classification (99.65%, 91.16% and 92.13%). By integrating Raman imaging of licorice, it can be inferred that the U-Net model, designed for region segmentation based on the inherent tissue structure of licorice, can effectively improve the accuracy origin classification, which has positive significance in the development of intelligence and information technology of Chinese medicine quality control.

An optimization strategy for charged aerosol detection to linearize the detector response in the multicomponent quantitative analysis of Qishen Yiqi dripping pills.

Charged aerosol detection, increasingly recognized for quantifying pharmaceutical compounds with weak ultraviolet absorption, is a universal detection technique for high-performance liquid chromatography (HPLC). Charged aerosol detection shows a non-linear response with increasing analyte concentration over a wide range, limiting its versatility in various analytical applications. In this work, a co-optimization strategy for power function value (PFV) and power laws was proposed and applied to broaden the linear range of the standard curve of saccharides in Qishen Yiqi dripping pills using the HPLC-charged aerosol detection (HPLC-CAD) method. Power function values for all analytes were optimized based on empirical models. Subsequently, the optimum power laws were investigated based on a preferred PFV. Additionally, various regression equations were evaluated to ensure the accuracy and precision of the results. With the optimized PFV and power law, the ordinary least squares model demonstrated a satisfactory fit. The optimal PFVs and power laws expanded the standard curve's linear range by 2.7 times compared to default settings, reducing model uncertainty. This paper presents a vital method for developing a multi-component quantitative HPLC-CAD approach without external data transformation outside the provided software, especially suitable for analytical applications of traditional Chinese medicine with significant quality differences.

QbD-Guided Traditional Chinese Medicine Manufacturing Process: Development and Optimization of Fluid-Bed Granulation and Drying Processes for Xiaochaihu Capsules.

Traditional methods of producing Xiaochaihu (XCH) capsules, a traditional Chinese medicine, are time-consuming, costly, and labor-intensive, which is not conductive to modernizing TCM. To address the challenges, new fluid-bed granulation and drying processes with water as the binder were developed and optimized guided by the principles of Quality by Design (QbD) in this study. Ishikawa diagram was applied to conduct a preliminary risk assessment, followed by 6-factor definitive screening design (DSD) serving as a QbD statistical tool to develop and optimize the new processes. Multiple potential factors and interactions were studied with a small number of experiments using the DSD. This study identified critical process parameters (CPPs), established quadratic regression models to reveal CPP-critical quality attributes (CQAs) connections within the DSD framework, and defined a dependable design space. Processes conducted by parameter combinations in the design space produced qualified granules with production yield and raw material utilization higher than 90% and moisture content lower than 4%. Furthermore, quantitative analysis of baicalin of all the granules ensured qualified contents of active pharmaceutical ingredient. The newly developed processes for XCH capsules, with advantages of shorter time, environmental friendliness, and decreased cost, exemplify the effective application of QbD and design of experiments (DoE) methodologies in the modernization of TCM manufacturing processes.

Mechanism modeling and application of Salvia miltiorrhiza percolation process.

Percolation is a common extraction method of food processing industry. In this work, taking the percolation extraction of salvianolic acid B from Salvia miltiorrhiza (Salviae Miltiorrhizae Radix et Rhizoma) as an example, the percolation mechanism model was derived. The volume partition coefficient was calculated according to the impregnation. experiment. The bed layer voidage was measured by single-factor percolation experiment and the internal mass transfer coefficient was calculated by the parameters obtained by fitting the impregnation kinetic model. After screening, the Wilson and Geankoplis, and Koch and Brady formulas were used to calculate the external mass transfer coefficient and the axial diffusion coefficient, respectively. After substituting each parameter into the model, the process of percolation of Salvia miltiorrhiza was predicted, and the coefficient of determination R2 was all greater than 0.94. Sensitivity analysis was used to show that all the parameters studied had a significant impact on the prediction effect. Based on the model, the design space including the range of raw material properties and process parameters was established and successfully verified. At the same time, the model was applied to the quantitative extraction and endpoint prediction of the percolation process.

Applications of Hyperspectral Imaging Technology Combined with Machine Learning in Quality Control of Traditional Chinese Medicine from the Perspective of Artificial Intelligence: A Review.

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and the rapid development of hyperspectral imaging (HSI) technology, the combination of the two has been widely used in the quality evaluation of TCM. Machine learning (ML) is the core wisdom of AI, and its progress in rapid analysis and higher accuracy improves the potential of applying HSI to the field of TCM. This article reviewed five aspects of ML applied to hyperspectral data analysis of TCM: partition of data set, data preprocessing, data dimension reduction, qualitative or quantitative models, and model performance measurement. The different algorithms proposed by researchers for quality assessment of TCM were also compared. Finally, the challenges in the analysis of hyperspectral images for TCM were summarized, and the future works were prospected.

Recognition of the rhizome of red ginseng based on spectral-image dual-scale digital information combined with intelligent algorithms.

Red ginseng is a widely used and extensively researched food and medicinal product with high nutritional value, derived from steamed fresh ginseng. The components in various parts of red ginseng differ significantly, resulting in distinct pharmacological activities and efficacies. This study proposed to establish a hyperspectral imaging technology combined with intelligent algorithms for the recognition of different parts of red ginseng based on the dual-scale of spectrum and image information. Firstly, the spectral information was processed by the best combination of first derivative as pre-processing method and partial least squares discriminant analysis (PLS-DA) as classification model. The recognition accuracy of the rhizome and the main root of red ginseng is 96.79% and 95.94% respectively. Then, the image information was processed by the You Only Look Once version 5 small (YOLO v5s) model. The best parameter combination is epoch = 30, learning rate = 0.01, and activation function is leaky ReLU. In the red ginseng dataset, the highest accuracy, recall and mean Average Precision at IoU (Intersection over Union) threshold 0.5 (mAP@0.5) were 99.01%, 98.51% and 99.07% respectively. The application of spectrum-image dual-scale digital information combined with intelligent algorithms in the recognition of red ginseng is successful, which provides a positive significance for the online and on-site quality control and authenticity identification of crude drugs or fruits.

The Discrimination and Characterization of Volatile Organic Compounds in Different Areas of Zanthoxylum bungeanum Pericarps and Leaves by HS-GC-IMS and HS-SPME-GC-MS.

The pericarps of Zanthoxylum bungeanum (ZBP) and leaves of Zanthoxylum bungeanum (ZBL) are popular spices in China, and they have pharmacological activities as well. In this experiment, the volatile organic compounds (VOCs) of the pericarps of Zanthoxylum bungeanum in Sichuan (SJ) and its leaves (SJY) and the pericarps of Zanthoxylum bungeanum in Shaanxi (SHJ) and its leaves (SHJY) were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The fingerprint of HS-GC-IMS and the heat maps of HS-SPME-GC-MS were established. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed. The results showed that a total of 95 components were identified, 62 components identified by HS-SPME-GC-MS and 40 components identified by HS-GC-IMS, of which 7 were the same. The analysis found that SJ and SHJ were obviously distinguished, while SJY and SHJY were not. There were considerably fewer VOCs in the leaves than in the pericarps. In the characterization of the VOCs of ZBL and ZBP, the flavor of ZBP was more acrid and stronger, while the flavor of ZBL was lighter and slightly acrid. Thirteen and eleven differential markers were identified by HS-GC-IMS and HS-SPME-GC-MS, respectively. This is helpful in distinguishing between SHJ and SJ, which contributes to their quality evaluation.

Discussion on several statistical problems in establishing quality standards of standard decoctions.

Since 2016, a number of studies have been published on standard decoctions used in Chinese medicine. However, there is little research on statistical issues related to establishing the quality standards for standard decoctions. In view of the currently established quality standard methods for standard decoctions, an improvement scheme is proposed from a statistical perspective. This review explores the requirements for dry matter yield rate data and index component transfer data for the application of two methods specified in "Technical Requirements for Quality Control and Standard Establishment of Chinese Medicine Formula Granules," which include the average value plus or minus three times the standard deviation ( X - ± 3 S D ) or 70% to 130% of the average value ( X - ± 30 % X - ). The square-root arcsine transformation method is used as an approach to solve the problem of unreasonable standard ranges of standard decoctions. This review also proposes the use of merged data to establish a standard. A method to judge whether multiple sets of standard decoction data can be merged is also provided. When multiple sets of data have a similar central tendency and a similar discrete tendency, they can be merged to establish a more reliable quality standard. Assuming that the dry matter yield rate and transfer rate conform to a binomial distribution, the number of batches of prepared slices that are needed to establish the standard decoction quality standard is estimated. It is recommended that no less than 30 batches of prepared slices should be used for the establishment of standard decoction quality standards.

Process optimization for the synthesis of functionalized Au@AgNPs for specific detection of Hg2+ based on quality by design (QbD).

The current study highlights the advantages of using the quality by design (QbD) approach to synthesise and optimize SERS substrates for the detection of Hg2+. Considering that the performance of Au@AgNPs is affected by many factors, Plackett-Burman (PB) experimental design was used to determine the critical process parameters (CPPs) for evaluating the performance of Au@AgNPs. The quantitative relationships between the CPPs and the critical quality attributes (CQAs) were assessed by Box-Behnken Design (BBD). The optimal design space for Au@AgNPs was calculated via a Monte Carlo algorithm. Finally, detection of Hg2+ in the range of 1 ∼ 100 ng mL-1 (R 2 = 0.9891) was achieved by SERS in combination with 4,4-bipyridine (Dpy) as signal molecules. The recoveries for licorice ranged from 83.53% to 92.96%. Specificity and practicality studies indicated that the method based on the QbD concept and design space not only met the optimal performance of Au@AgNPs but also improved the rapid detection of Hg2+ in Chinese medicine samples.

Near-infrared spectroscopy and HPLC combined with chemometrics for comprehensive evaluation of six organic acids in Ginkgo biloba leaf extract.

OBJECTIVES: To investigate the feasibility of using near-infrared spectroscopy for rapid determination of main organic acids in Ginkgo biloba leaf extract (EGBL). METHODS: Main organic acids in EGBL were assayed using the HPLC method. Critical factors of the chromatographic separation were optimized by a novel analytical quality by design approach. Partial least squares-discriminant analysis (PLS-DA) was performed to screen the marker components, and principal component analysis (PCA) was utilized to distinguish the different samples. Then, spectral quantification potential was investigated using PLS and support vector machine (SVM) approaches. For modelling, different spectral preprocessing and wavelength selection methods were systematically compared. KEY FINDINGS: It was found that quinic acid, protocatechuic acid and 6-hydroxykynurenic acid were identified as possible index components. PLS-DA based on contents and PCA based on near-infrared spectra can both effectively distinguish the different EGBL samples. The calibration models with wonderful prediction performance can be both developed by the PLS and SVM algorithms. CONCLUSIONS: NIR spectroscopy combined with chemometrics can realize the rapid and non-destructive qualitative and quantitative analysis of EGBL. The proposed method may be applied to quality control of EGBL and other natural products in commercial use.

Establishing a chromatographic fingerprint using tandem UV/charged aerosol detection and similarity analysis for Shengmai capsule: A novel method for natural product quality control.

INTRODUCTION: Shengmai San, a well-known traditional Chinese medicine formula, is used to treat coronary heart diseases and myocardial infarction. The complex composition and complicated mechanism of the Shengmai preparations bring a significant challenge in the development of a suitable quality control method. OBJECTIVES: This work aims to establish a chromatographic fingerprinting method and propose a weighting algorithm for application in fingerprint similarity analysis to ensure consistent quality of the Shengmai capsule. METHODOLOGY: A chromatographic fingerprint method was established using tandem UV/charged aerosol detection (CAD) for Shengmai capsule quality control. After method verification, the developed method was applied to analyze 15 batches of the samples. Then a weighting algorithm of the fingerprint peak was proposed and used for the fingerprint similarity analysis. RESULTS: An HPLC-UV/CAD fingerprint method was successfully developed for the Shengmai capsules. Chromatographic conditions of the HPLC-UV/CAD method were optimized with a definitive screening design, and the optimized ranges of operating parameters were obtained with a Monte Carlo simulation method. The combined use of the proposed weighting algorithm and similarity analysis on fingerprint data improves the sensitivity of distinguishing batch-to-batch quality differences. CONCLUSION: The developed HPLC-UV/CAD fingerprint method is robust, reliable, and efficient. The proposed weighting algorithm combined with similarity analysis is promising and meaningful for the quality consistency assessment of HPLC-UV/CAD fingerprints.

Liquid chromatographic study of two structural isomeric pentacyclic triterpenes on reversed-phase stationary phase with hydroxypropyl-ß-cyclodextrin as mobile phase additive.

Retention behavior of two structural isomeric pentacyclic triterpenic acids, maslinic acid and corosolic acid, was investigated by reverse phase high performance liquid chromatography (HPLC) with hydroxypropyl-ß-cyclodextrin (HP-ß-CD) as mobile phase additive. Inclusion complexation of maslinic acid, corosolic acid with hydroxypropyl-ß-cyclodextrin was evaluated under different concentration of hydroxypropyl-ß-cyclodextrin. Apparent formation constant (Km) between methanol and hydroxypropyl-ß-cyclodextrin was determined to be 13.82 L mol-1 under 25 °C using UV-spectrophotometry. Two retention models were employed individually for evaluation of inclusion complexation between the two pentacyclic triterpenic acids and hydroxypropyl-ß-cyclodextrin. It was found that a higher apparent formation constant (Kf) for corosolic acid and hydroxypropyl-ß-cyclodextrin was obtained, 19115 L mol-1, indicating that a greater affinity of hydroxypropyl-ß-cyclodextrin with corosolic acid was produced compared with that of maslinic acid, 11775 L mol-1, in the selected mobile phase, and stoichiometric ratio for both of inclusion complex was found to be 1:1. Thermodynamic analysis showed that a negative standard enthalpy change (ΔH) and an entropy change (ΔS*) for analyte transfer were obtained, where ΔH of maslinic acid and corosolic acid was found to be -10.188 kJ mol-1 and -10.650 kJ mol-1, ΔS* of two compounds was -2.092 and -2.180, respectively, indicating that transfer of structural isomers from mobile phase to stationary phase was enthalpically driven. Meanwhile, positive values were obtained for standard enthalpy change and standard entropy change, 136 kJ mol-1 and 274 kJ mol-1 and 536 J mol-1 K-1and 1004 J mol-1 K-1, for inclusion complexation between maslinic acid, corosolic acid and hydroxypropyl-ß-cyclodextrin, while negative values were obtained for Gibbs free energy during formation of inclusion complex, -160 kJ mol-1 and -299 kJ mol-1, indicating a spontaneous inclusion reaction happened.

Determination of inhibitory activity of Salvia miltiorrhiza extracts on xanthine oxidase with a paper-based analytical device.

A novel paper-based analytical device (PAD) was prepared and applied to determine the xanthine oxidase (XOD) inhibitory activity of Salvia miltiorrhiza extracts (SME). First, polycaprolactone was 3D printed on filter paper and heated to form hydrophobic barriers. Then the modified paper was cut according to the specific design. Necessary reagents including XOD for the colorimetric assay were immobilized on two separate pieces of paper. By simply adding phosphate buffer, the reaction was performed on the double-layer PAD. Quantitative results were obtained by analyzing the color intensity with the specialized device system (consisting of a smartphone, a detection box and sandwich plates). The 3D-printed detection box was small, with a size of 9.0 cm × 7.0 cm × 11.5 cm. Color component G performed well in terms of linearity and detection limits and thus was identified as the index. The reaction conditions were optimized using a definitive screening design. Moreover, a 10% glycerol solution was found to be a suitable stabilizer. When the stabilizer was added, the activity of XOD could be maintained for at least 15 days under 4 °C or -20 °C storage conditions. The inhibitory activity of SME was investigated and compared to that of allopurinol. The results obtained with the PAD showed agreement with those obtained with the microplate method. In conclusion, the proposed PAD method is simple, accurate and has a potential for point-of-care testing. It also holds promise for use in rapid quality testing of medicinal herbs, intermediate products, and preparations of traditional Chinese medicines.

Development of an HPLC-MS method for the determination of four terpene trilactones in Ginkgo biloba leaf extract via quality by design.

Previously reported HPLC-evaporative light scattering detection methods for terpene trilactone determination in Ginkgo biloba leaf extract (EGBL) have complicated sample preparation steps and are time-consuming. Thus, in this work, an HPLC-MS method for the determination of terpene trilactones in EGBL was developed with a novel analytical quality by design approach to provide robust and simple measurements. For this purpose, analytical target profiles and systematic risk analyses were performed to identify potential critical method attributes and critical method parameters. After screening experiments, a Box-Behnken design approach was utilized to investigate the relationships between critical method attributes and critical method parameters. A hypercube design space obtained by a Monte Carlo method was used for choosing the analytical control strategy. Then, verification experiments were performed within the design space, and the models were found to be accurate. After that, the optimized method was verified and successfully used for quality control analysis of EGBL from different manufacturers, and the results were almost the same as those determined by HPLC-evaporative light scattering detection. To our knowledge, this is the first study to establish a robust HPLC-MS method for determination of terpene trilactones in EGBL based on a novel analytical quality by design concept, which can improve the quality control of commercial EGBL.

[Definitive screening design combined with design space for optimizing purification process of Scutellariae Radix extract].

In the pharmacopoeia, many process parameters for the purification process of Scutellariae Radix are unclear. In this study, deterministic screening design combined with design space method was used to optimize the purification process of Scutellariae Radix extract. Nine method parameters such as mass fraction of solution(X_1), first acid precipitation pH(X_2) and first holding time(X_3) in the purification process were firstly studied by definitive screening design. The yield of baicalin was defined as the evaluation index. A stepwise regression method was used then to build quantitative models between evaluation index and method parameters and the three most critical impact parameters were determined. Probability-based design space was calculated and successfully verified with the experimental error simulation method. Finally, the second standing temperature, the first standing temperature and the pH value of the second acid precipitation were determined as the three most critical method parameters. The recommended operating space was as follows: the second standing temperature 5-7 ℃, the first standing temperature 13-15 ℃, and the pH of the second acid precipitation 1.5-1.7. Within this operating space, the baicalin yield in the purification process was over 80%, and the probability of reaching the standard was over 0.96. In this study, we optimized the effect of various parameters for the purification process of the Scutellariae Radix extract in the pharmacopoeia on the yield of baicalin and provided a reference for industrial production of the exact of Scutellariae Radix.

A simple and effective method for the preparation of high-purity shikimic acid from chromatography wash effluent of Ginkgo biloba leaf extract by macroporous resin considering the effect of varying feed solution compositions.

OBJECTIVES: The present study investigated the feasibility of preparing high-purity shikimic acid (SA) from the chromatography wash effluent of Ginkgo biloba leaf extract by macroporous resin. METHODS: First, static/dynamic adsorption and desorption were conducted to screen out the optimal resin. Second, the key parameters of the chromatographic process were optimised with face-centred central composite design (CCD). Third, wash effluent indices were measured, different batches of wash effluent were used to prepare SA under the optimised parameters, and the effect of varying feed solution compositions on final products was investigated. KEY FINDINGS: It was found that the final purity and recovery rate of SA prepared with ADS-21 resin were not lower than 70 and 60%, respectively, when the purity of SA in the wash effluent was higher than 21.4%. The quality of the final product can be predicted based on the properties of wash effluent. CONCLUSIONS: The proposed method could not only provide a simple, green and promising approach for the large-scale purification of SA from wash effluent but also be used to develop process intermediate quality standards for other natural products.