Study on the application of model transfer technology in the extraction process of Xiao'er Xiaoji Zhike oral liquid / 药学学报

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.

Application of DOSC combined with SBC in batches transfer of NIR quantitative model / 中国中药杂志

Near infrared model established under a certain condition can be applied to the new samples status, environmental conditions or instrument status through the model transfer. Spectral background correction and model update are two types of data process methods of NIR quantitative model transfer, and orthogonal signal regression (OSR) is a method based on spectra background correction, in which virtual standard spectra is used to fit a linear relation between master batches spectra and slave batches spectra, and map the slave batches spectra to the master batch spectra to realize the transfer of near infrared quantitative model. However, the above data processing method requires the represent activeness of the virtual standard spectra, otherwise the big error will occur in the process of regression. Therefore, direct orthogonal signal correction-slope and bias correction (DOSC-SBC) method was proposed in this paper to solve the problem of PLS model's failure to predict accurately the content of target components in the formula of different batches, analyze the difference between the spectra background of the samples from different sources and the prediction error of PLS models. DOSC method was used to eliminate the difference of spectral background unrelated to target value, and after being combined with SBC method, the system errors between the different batches of samples were corrected to make the NIR quantitative model transferred between different batches. After DOSC-SBC method was used in the preparation process of water extraction and ethanol precipitation of Lonicerae Japonicae Flos in this paper, the prediction error of new batches of samples was decreased to 7.30% from 32.3% and to 4.34% from 237%, with significantly improved prediction accuracy, so that the target component in the new batch samples can be quickly quantified. DOSC-SBC model transfer method has realized the transfer of NIR quantitative model between different batches, and this method does not need the standard samples. It is helpful to promote the application of NIR technology in the preparation process of Chinese medicines, and provides references for real-time monitoring of effective components in the preparation process of Chinese medicines.

Near infrared spectrum monitoring of scale up process for macroporous resin purification of Grardeniae extract / 中国中药杂志

To establish a fast detection method during the purifying process of the extracts from Grardeniae using macroporous resin based on near infrared spectroscopy. First, the ethanol eluent was collected from the purification process of small size sample; and near infrared (NIR) spectrum was collected. Then the content of the geniposide was determined by HPLC method, and partial least squares (PLS) method was used to establish the quantitative model to predict the content of geniposide by NIR spectrum. This model was used to supervise the changes of geniposide concentrations in ethanol eluent during medium scale process. Experimental results showed that the NIR small scale model can accurately predict the concentrations of geniposide in the production process of medium scale. However, with the proceeding of batch processes, the prediction performance of the model was decreased, so model updating method was employed to maintain the model. After twice updates, the NIR quantitative model can accurately predict the concentrations of the geniposide during medium scale process. Therefore, through model updates, the established NIR quantitative model can be applied in different scales of macroporous resin purification processes, to improve the data utilization efficiency of small scale process and save the cost of rebuilding the quantitative model of medium scale.

Near Infrared Spectroscopic Model Transfer Based on Simple Linear Regression / 分析化学

To solve the calibration transmission problem in near-infrared ( NIR) spectroscopy, a novel model transfer method, Simple Linear Regression Direct Standard-ization ( SLRDS ) , has been presented. To investigate the validity of the proposed method, a real corn sample NIR dataset was tested and the direct standardization ( DS ) method and piecewise direct standardization ( PDS ) method were involved as a comparison. Our results indicated that SLRDS can correct compressed NIR data differences among instruments and enable the user to share corn sample PLS calibration model among three instruments, at the same time it has higher prediction accuracy, fewer parameters and simpler model than DS and PDS.

On-line Quantitative Monitoring and Control of Tobacco Flavors by Near Infrared Spectroscopy Combined with Advanced Calibration Transfer Method / 分析化学

Whennearinfraredspectroscopyisappliedtoon-linemonitoringandcontroloftobaccoflavors,the variation in temperature can severely deteriorate the predictive performance of near infrared spectroscopic calibration models and results in a significant increases of the root mean square error value for the main constituents in syrup samples from 2. 4% to 29. 0%. In this paper, near infrared spectroscopy has been incorporated with an advanced calibration transfer method-loading space standardization to effectively eliminate the deteriorate effects of temperature variation on quantitative results and finally realize the fast and accurate on-line quantitative monitoring and control of tobacco flavors. The root mean square error value for the main constituents in syrup samples is successfully retained at a satisfying low level of 3 . 8%. The results of this paper will provide technical support for the preparation, preservation and use of tobacco flavors, and realize on-line process quality control of cigarettes.