ABSTRACT
<b>Objective::To investigate the feasibility of near-infrared spectroscopy for detecting the coating film thickness of Tianshu tablets. <b>Method::Nine batches of Tianshu tablets were taken during the coating process. Then, their near-infrared diffuse reflection spectra were collected. The sample set was divided into calibration set and validation set by Kennard-Stone algorithm. The preprocessing method was selected. The synergy interval partial least squares (siPLS) and moving window partial least squares (mwPLS) were employed to screen the optimal spectral interval. And the corresponding quantitative calibration model of partial least squares (PLS) were established. Some evaluation parameters were adopted to assess the performance of the model. <b>Result::The method of first derivative and Norris Derivative smoothing combined with standard normal variate transformation was suitable for processing the spectra. The optimal PLS model was established in the preferred band interval of siPLS. The correlation coefficient between the predicted value and the measured value of calibration set was 0.966, and the correlation coefficient between the predicted value and the measured value of validation set was 0.991.Both root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) values were small, which showed the fitness and predictive performance of the model were favorable. <b>Conclusion::The near-infrared spectroscopy technique can be used to determine coating film thickness of Tianshu tablets with high accuracy, which provides technical supports for the in-line determination of coating thickness in the production process of traditional Chinese medicine tablets.
ABSTRACT
Objective: To investigate how to rapidly predict coating terminal point in the thin film coating of Tianshu Tablets by near-infrared spectroscopy (NIRS). Methods: Firstly, synergy interval partial least square (siPLS) was used to optimize the modeling intervals and the pretreatment methods were screened. Secondly, according to the optimal modeling parameters, the conformity test and similarity matching were used to establish calibration models based on the established reference spectra library. Subsequently, by comparing the similarities between the test spectra and the reference spectra, the threshold value was set. The relation among the similarities, threshold value and the coating end point was obtained. Finally, the performance of the calibration model was verified by the validation set. Results: During the coating process, the similarities between the test spectra and the reference spectra were gradually increasing until the similarities of continuous test spectra exceeded the threshold, thus, indicating the end point. Conclusion: Conformity test and similarity matching can sensitively monitor the change of similarities among spectra and the corresponding trend. Both models have high performance, which can accurately predict the coating terminal point. It’s of great significance to reduce batch-to-batch variation in film coating and the loss of coating materials, and improve the coating efficiency and the quality uniformity of solid preparation.
ABSTRACT
To establish a rapid quantitative analysis method for online monitoring of chlorogenic acid in aqueous solution of Lonicera Japonica Flos extraction by using micro-electromechanical near infrared spectroscopy (MEMS-NIR). High performance liquid chromatography(HPLC) was used as reference method．Kennard-Stone (K-S) algorithm was used to divide sample sets, and partial least square(PLS) regression was adopted to establish the multivariate analysis model between the HPLC analysis contents and NIR spectra. The synergy interval partial least squares (SiPLS) was used to selected modeling waveband to establish PLS models. RPD was used to evaluate the prediction performance of the models. MDLs was calculated based on two types of error detection theory, on-line analytical modeling approach of Lonicera Japonica Flos extraction process was expressed scientifically by MDL. The result shows that the model established by multiplicative scatter correction(MSC) was the best, with the root mean square with cross validation(RMSECV), root mean square error of correction(RMSEC) and root mean square error of prediction(RMSEP) of chlorogenic acid as 1.707, 1.489, 2.362, respectively, the determination coefficient of the calibration model was 0.998 5, and the determination coefficient of the prediction was 0.988 1．The value of RPD is 9.468.The MDL (0.042 15 g•L⁻¹) selected by SiPLS is less than the original,which demonstrated that SiPLS was beneficial to improve the prediction performance of the model. In this study, a more accurate expression of the prediction performance of the model from the two types of error detection theory, to further illustrate MEMS-NIR spectroscopy can be used for on-line monitoring of Lonicera Japonica Flos extraction process.