Yan, Hui
Study on rapid non-destructive detection of eugenol in caryophylli flos by NIR spectroscopy and partial least square - Vol. 80(6), November-December - Mumbai Indian Journal of Pharmaceutical Science 2018 - 1136-1142
A method for rapid non-destructive detection of eugenol in caryophylli flos was developed with near-infrared spectroscopy and chemometrics. One hundred and three samples were collected, and gas chromatography with internal standard was used to determine the reference value of eugenol in caryophylli flos. Near-infrared spectra were recorded from their powders and pretreated with standard normal variate and 1st derivative, and then a model was built with the chemometrics method, partial least square. Outliers of samples were detected. When 6 factors were adopted in a model, the performance was found to be the best. For prediction set, the slope was 0.9461, the offset was 0.3008, R-square was 0.9388, root mean square error of prediction was 0.6987 %, bias was -0.2815 % and residual prediction deviation was 5.89. The statistical analysis showed the predicted results were consistent with the reference values. It is feasible that near-infrared spectroscopy could be used to rapidly and accurately detect the main active content, eugenol in caryophylli flos as a method of quality control.
PHARMACEUTICS
Study on rapid non-destructive detection of eugenol in caryophylli flos by NIR spectroscopy and partial least square - Vol. 80(6), November-December - Mumbai Indian Journal of Pharmaceutical Science 2018 - 1136-1142
A method for rapid non-destructive detection of eugenol in caryophylli flos was developed with near-infrared spectroscopy and chemometrics. One hundred and three samples were collected, and gas chromatography with internal standard was used to determine the reference value of eugenol in caryophylli flos. Near-infrared spectra were recorded from their powders and pretreated with standard normal variate and 1st derivative, and then a model was built with the chemometrics method, partial least square. Outliers of samples were detected. When 6 factors were adopted in a model, the performance was found to be the best. For prediction set, the slope was 0.9461, the offset was 0.3008, R-square was 0.9388, root mean square error of prediction was 0.6987 %, bias was -0.2815 % and residual prediction deviation was 5.89. The statistical analysis showed the predicted results were consistent with the reference values. It is feasible that near-infrared spectroscopy could be used to rapidly and accurately detect the main active content, eugenol in caryophylli flos as a method of quality control.
PHARMACEUTICS