Towards real time release testing of Shuxuening injection based on near infrared spectroscopy and accuracy profile

Real time release testing (RTRT) has been applied in the pharmaceutical process to ensure the high quality of finished products. Near infrared (NIR) spectroscopy is one of the primary analytical methods to implement RTRT. In this study, an NIR quantitative method was developed to determine the content of total flavonol glycosides in Shuxuening injection and validated by the accuracy profile approach. Combining the NIR validation with quality specification limits, a reliable RTRT method was constructed. Shuxuening injection samples of different concentrations were prepared and characterized by NIR spectroscopy. A first-order Savitzky–Golay derivative was used to pretreat the NIR spectra, and the competitive adaptive reweighted sampling method was used to select the feature variables. Partial least squares (PLS) regression was used to build the NIR quantitative model. The trueness, precision, and accuracy of the developed NIR models were validated by accuracy profile, and the measurement uncertainty was also estimated. Finally, the unreliability graph as a decision tool was established to avoid risk, enabling correct decision making to release of Shuxuening injection. The root mean square error of calibration, root mean square error of cross validation, root mean square error of prediction, and the ratio of prediction to deviation of the PLS model were 19.6 μg·mL−1, 20.9 μg·mL−1, 29.9 μg·mL−1, and 12.2, respectively, indicating the NIR quantitative model had good predictive performance. The validation results prove that the precision, trueness, and accuracy of the NIR quantitative model were within the acceptable limits. Based on the unreliability graph, the decision to release Shuxuening injection was satisfied, if the prediction of total flavonol glycosides fell into the range from 783 μg·mL−1 to 900 μg·mL−1. The RTRT method for Shuxuening injection based on NIR spectroscopy and accuracy profile can improve the efficiency and accuracy of quality control.