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

Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics

INTRODUCTION

There are some problems in the quality control of Epimedii Folium (leaves of Epimedium brevicornum Maxim.), such as the mixed use of Epimedii Folium from different harvesting periods and regions, incomplete quality evaluation, and time-consuming analysis methods.

OBJECTIVE

Near-infrared (NIR) spectroscopy was conducted to establish a rapid overall quality evaluation method for Epimedii Folium.

MATERIALS AND METHODS

Quantitative models of the total solid, moisture, total flavonoid, and flavonol glycoside (Epimedin A, Epimedin B, Epimedin C, Icariin) contents of Epimedii Folium were established by partial least squares regression (PLSR). The root mean square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of models. The qualitative models of Epimedii Folium from different geographic origins and harvest periods were established based on K-nearest neighbor (KNN), back-propagation neural network (BPNN), and random forest (RF). Accuracy and Kappa values were used to evaluate the performance of models. A new multivariable signal conversion strategy was proposed, which combines NIR spectroscopy with the PLSR model to predict the absorbance values of retention time points in the high-performance liquid chromatography (HPLC) fingerprint to obtain the predicted HPLC fingerprint. The Pearson correlation coefficient and cosine coefficient were used to evaluate the similarity between real and predicted HPLC fingerprints.

RESULTS

Qualitative models, quantitative models, and the similarity between real and predicted HPLC fingerprints are satisfactory.

CONCLUSION

The method serves as a fast and green analytical quality evaluation method of Epimedii Folium and can replace traditional methods to achieve the overall quality evaluation of Epimedii Folium.

Advances in analytical techniques and quality control of traditional Chinese medicine injections

Traditional Chinese medicine injections (TCMIs) are a new pharmaceutical form in the modernization of traditional Chinese medicines (TCMs). Its efficacy is rapid, the curative effect is improved, and is widely used in critical and acute diseases, complicated and severe diseases, and other treatment. However, with the broad applications of TCMIs, clinical adverse reactions frequently occur, and safety problems become more prominent. Therefore, the quality control of TCMIs is essential. Chemical analysis methods and biological analysis methods are widely used in the quality control of TCMIs. This article describes the current status of TCMIs, the analytical techniques, and methods currently used, and the quality control of TCMIs. A summary of the advantages and disadvantages of the current analysis methods is presented. An overview of the quality control of TCMIs is introduced. In addition, emerging techniques of the quality control of TCMIs are introduced.

Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration

Near infrared spectroscopy (NIR) with diffuse reflectance associated to multivariate calibration has as main advantage the replacement of the physical separation of interferents by the mathematical separation of their signals, rapidly with no need for reagent consumption, chemical waste production or sample manipulation. Seeking to optimize quality control analyses, this spectroscopic analytical method was shown to be a viable alternative to the classical Kjeldahl method for the determination of protein nitrogen in yellow fever vaccine. The most suitable multivariate calibration was achieved by the partial least squares method (PLS) with multiplicative signal correction (MSC) treatment and data mean centering (MC), using a minimum number of latent variables (LV) equal to 1, with the lower value of the square root of the mean squared prediction error (0.00330) associated with the highest percentage value (91%) of samples. Accuracy ranged 95 to 105% recovery in the 4000-5184 cm^-1 region.

On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

There is a growing need for the effective on-line process monitoring during the manufacture of traditional Chinese medicine to ensure quality consistency. In this study, the potential of near infrared (NIR) spectroscopy technique to monitor the extraction process of Flos Lonicerae Japonicae was investigated. A new algorithm of synergy interval PLS with genetic algorithm (Si-GA-PLS) was proposed for modeling. Four different PLS models, namely Full-PLS, Si-PLS, GA-PLS, and Si-GA-PLS, were established, and their performances in predicting two quality parameters (viz. total acid and soluble solid contents) were compared. In conclusion, Si-GA-PLS model got the best results due to the combination of superiority of Si-PLS and GA. For Si-GA-PLS, the determination coefficient (R_p²) and root-mean-square error for the prediction set (RMSEP) were 0.9561 and 147.6544μg/ml for total acid, 0.9062 and 0.1078% for soluble solid contents, correspondingly. The overall results demonstrated that the NIR spectroscopy technique combined with Si-GA-PLS calibration is a reliable and non-destructive alternative method for on-line monitoring of the extraction process of TCM on the production scale.