盐补骨脂配方颗粒中指标成分及水分测定的NIR模型建立

吴文辉; 唐小龙; 冯健; 胡昌江

doi:10.13422/j.cnki.syfjx.2014130041

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盐补骨脂配方颗粒中指标成分及水分测定的NIR模型建立

化学与分析 | 更新时间：2024-01-04

- 盐补骨脂配方颗粒中指标成分及水分测定的NIR模型建立
- Establishment of NIR Model of Determination of Index Components and Moisture Content in Salt Psoraleae Fructus Granules
- 中国实验方剂学杂志 2014年20卷第13期页码：41-44
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家西南地区中药材规范化种植及大宗中药材综合利用开发技术项目（2011BAI13B00）
- DOI：10.13422/j.cnki.syfjx.2014130041
  中图分类号： O657.3;R286.0
- 纸质出版日期：2014
- 稿件说明：
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吴文辉, 唐小龙, 冯健, 等. 盐补骨脂配方颗粒中指标成分及水分测定的NIR模型建立[J]. 中国实验方剂学杂志, 2014,20(13):41-44.

WU Wen-hui, TANG Xiao-long, FENG Jian, et al. Establishment of NIR Model of Determination of Index Components and Moisture Content in Salt Psoraleae Fructus Granules[J]. Chinese journal of experimental traditional medical formulae, 2014, 20(13): 41-44.
吴文辉, 唐小龙, 冯健, 等. 盐补骨脂配方颗粒中指标成分及水分测定的NIR模型建立[J]. 中国实验方剂学杂志, 2014,20(13):41-44. DOI： 10.13422/j.cnki.syfjx.2014130041.

WU Wen-hui, TANG Xiao-long, FENG Jian, et al. Establishment of NIR Model of Determination of Index Components and Moisture Content in Salt Psoraleae Fructus Granules[J]. Chinese journal of experimental traditional medical formulae, 2014, 20(13): 41-44. DOI： 10.13422/j.cnki.syfjx.2014130041.

摘要

目的：建立盐补骨脂配方颗粒中补骨脂素、异补骨脂素及水分测定的NIR模型。方法：以HPLC和水分测定为基准数据，采用NIR技术建立10批盐补骨脂配方颗粒中指标成分（补骨脂素和异补骨脂素）及水分测定的定量模型。结果：补骨脂素与异补骨脂素含量测定模型的交互验证均方根误差（RMSECV）0.025 0，相关系数（r）0.886 8，预测均方根误差（RMSEPV）0.025 5，r=0.891 0；水分测定模型的RMSECV=0.161，r=0.897 5，RMSEPV=0.150 0，r=0.921 8。结论：建立的NIR模型稳定、准确、可靠，可快速测定其覆盖范围内的补骨脂素、异补骨脂素及水分含量。

Abstract

Objective: To establish NIR model of psoralen

isopsoralen and moisture content in salt Psoraleae Fructus granules. Method: With determination of HPLC and moisture content as reference data

NIR was adopted to build quantitative measurement models of psoralen

isopsoralen and moisture content of 10 batches of salt Psoraleae Fructus granules. Result: Root mean square error of cross validation (RMSECV) of determination model of psoralen and isopsoralen was 0.025 0 with correlation coefficient(r) of 0.886 8

root mean square error of prediction(RMSEPV) was 0.025 5 with r=0.891 0;RMSECV of moisture determination was 0.161 with r=0.897 5

RMSEPV was 0.150 0 with r=0.921 8. Conclusion: These established models was stable

accurate and reliable

which could be applied for fast determination of psoralen

isopsoralen and moisture within their coverage.

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