使用近红外光谱技术的快速定量检查技术评估沙夫兰的质量
Ying Zhou1, Han Zhang2, Xiaohui Sheng3
1NMPA Key Laboratory of Quality Evaluation of Traditional Chinese Medicine (Traditional Chinese Patent Medicine), Zhejiang Institute for Food and Drug Control, Hangzhou 310052, China.
Molecules (Basel, Switzerland)
|September 14, 2024
概括
一个新的近红外光谱模型准确地预测了沙夫兰.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 沙夫兰的质量是由其化学成分决定的.
- 沙夫兰需要准确和快速的质量评估方法.
研究的目的:
- 使用近红外光谱 (NIR) 开发沙夫兰品质的预测模型.
- 为了估计沙夫兰的干燥损失 (LD),crocin I,crocin II和picrocrocin含量.
主要方法:
- 从928个藏红花样本收集了NIR光谱.
- 使用各种技术预处理的光谱数据 (例如,萨维茨基-戈莱光滑,MSC).
- 开发了部分最小平方 (PLS) 回归模型用于预测.
主要成果:
- 对于LD (R2=0.8627),总克罗辛 (R2=0.8851) 和皮克罗辛 (R2=0.8592) 的高预测准确度得到了实现.
- 模型展示了低根平均平方误差值.
- 通过结合预处理方法来优化PLS模型.
结论:
- 尼尔光谱 (NIR) 提供了一种精确和有效的方法来评估香的质量.
- 开发的模型可以用来快速评估沙夫兰的质量.
- 这种方法支持开发沙夫兰的快速质量控制系统.
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