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烟酰胺片的近红外漫反射光谱快速、无损定量测定 被引量:2

Rapid nondestructive quantitative analysis of Nicotinamide Tablets by near infrared diffuse reflectance spectrometry
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摘要 目的建立近红外漫反射光谱法检测烟酰胺片的方法。方法应用傅立叶变换近红外光谱仪和外接积分球以及固体光纤探头,原料粉末采用PbS检测器,素片和空白辅料片采用InGaAs检测器,在12 000~4 000 cm-1,每份样品扫描64次求平均光谱,通过偏最小二乘法建立模型,建模采用波段为9 750.7 cm-1~7 498.2 cm-1。结果交互证实法确定64次扫描最佳主成分数为4,均方误为3.34,预示集平均回收率为99.56%,RSD为2.58%。为确证近红外漫反射光谱法应用的可行性,预示集与参比方法高效液相色谱法测定结果进行比较,采用配对t检验,近红外法与高效液相色谱法比较无显著性差异(P>0.05)。结论显示近红外漫反射光谱法快速,简便,无损,能够用于烟酰胺片的含量测定。 Objective To develop a near infrared diffuse reflectance analysis ( NIRDRA ) method for rapid nondestructive quantitative determination of nieotinamide in tablets. Methods All spectra were measured by a Fourier transform spectrometer e-quipped with a PbS and a InGaAs detector, an external integrating sphere and a fibre - optic probe for reflectance measurements. All samples were scanned from 12 000 cm-1 to 4 000 cm-1 ,and each spectrum was obtained though automatic mean of 64 scans. First derivative spectrum preprocessing method was used, and spectral region ( 9 750.7 - 7 498.2 cm-1 ) was selected to develop mathematical models by partial least square method for nieotinamide tablets samples. Results The optimal rank and mean square error determined for samples though automatic mean of 64 scans by cross validation method were 4 and 3.34 respectively; the av- erage recovery was 99.56% ;and the RSD was 2.58%. To verify that the NIR method was feasible for application, the predicted values were compared to the observed values measured by HPLC which was used as the reference method. The paired t - test was applied and the results showed that there was no significant difference between the NIR method and the reference methods ( P 〉 0. 05 ). Conclusion The NIRDRA method was rapid, simple,and noninvasive, and it can be applied to assay for the content of nieotinamide in tablets.
出处 《齐鲁药事》 2011年第11期639-641,共3页 qilu pharmaceutical affairs
关键词 近红外漫反射光谱法 烟酰胺片 偏最小二乘(PLS) Near infrared diffuse reflectance speetrometry Nicotinamide tablets Partial least square method
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