Quantitative determination of ademetionine in tablets utilizing ATR-FTIR and partial least squares methods approaches

In this study there were utilized a combination of Fourier transform infrared spectroscopy in attenuated total reflection mode (ATR-FTIR) and partial least squares (PLS) regression method to develop quantitative models for determining the concentration of ademetionine in commercial tablets. The established and validated models were specifically designed for a commercial product containing ademetionine 1,4-butandiesulfonate. The coefficient of determination for the developed model was 0.999. Relative standard deviation (RSD) does not exceed 1.6% for repeatability and intermediate accuracy, which meets the international ICH and AOAC requirements for the method performance. The validation results effectively confirmed that this method is suitable and meets the current requirements for analytical methods in drug quality control. Consequently, this approach can be used for routine ademetionine analysis in pharmaceutical products and has the potential to be applied to other active pharmaceutical ingredients (APIs) in drug quality control.

Rapid evaluation of Ziziphi Spinosae Semen and its adulterants based on the combination of FT-NIR and multivariate algorithms

Ziziphi Spinosae Semen (ZSS) is a valued seed renowned for its sedative and sleep-enhancing properties. However, the price increase has been accompanied by adulteration. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) combined with multivariate algorithms were employed to identify the adulteration and quantitatively predict the adulteration ratio. The findings suggested that the utilization of chromaticity extractor was insufficient for identification of adulteration ratio. The raw spectrum of ZMS and HAS adulterants extracted by FT-NIR was processed by SNV + CARS and 1d + SG + ICO respectively, the average accuracy of machine learning classification model was improved from 77.06 % to 97.58 %. Furthermore, the R2 values of the calibration and prediction set of the two quantitative prediction regression models of adulteration ratio are greater than 0.99, demonstrating excellent linearity and predictive accuracy. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided a significant approach to addressing the growing issue of ZSS adulteration.

New strategies to analyze argentatins A and B in guayule (Parthenium argentatum, A. Gray)

There is considerable interest in the exploitation of compounds belonging to the triterpenoid family from guayule (Parthenium argentatum, A. Gray), as they offer several beneficial effects to human health. The most abundant triterpenoids in guayule resin are the argentatins, which are currently analyzed by labor-intensive and time-consuming techniques. The purpose of the present study was to estimate argentatins and isoargentatins A and B in guayule using near-infrared spectroscopy (NIRS) and flow injection analysis (FIA). Results revealed that the best partial least squares regression model exhibited excellent correlation with the values estimated by NIRS calibration (r2c = 0.99-1.00) and cross-validation (r2cv = 0.94-0.99), and the residual predictive deviation was >3 in all cases. After optimization of the liquid chromatography-mass spectrometry and FIA parameters, the FIA mode could reliably collect data for argentatin A and B after applying a calculated coverage factor. In sum, NIRS and FIA appear to be a robust option for the estimation and routine analysis of argentatins in guayule stems and resin, respectively.