非破坏性近红外技术,用于在大麻花朵中有效地分析大麻素
Hamza Rafiq1, Jens Hartung2, Torsten Schober1
1Department of Agronomy, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany.
Plants (Basel, Switzerland)
|April 9, 2024
概括
一个手持近红外 (NIR) 设备有效量化大麻中的大麻素. 标准正常变种 (SNV) 预处理优化了总大麻 (CBD) 的测量,增强了工业应用的快速,非破坏性分析.
科学领域:
- 分析化学 分析化学
- 植物科学 植物科学
- 频谱学是一种光谱学.
背景情况:
- 大麻研究需要快速,非破坏性的方法来定量大麻素.
- 现有的方法可能耗时或具有破坏性,限制实时分析.
- 手持近红外光谱 (NIR) 为现场分析提供了一个潜在的解决方案.
研究的目的:
- 评估一台手持的NIR设备,用于在整个大麻花朵中量化总大麻 (CBD),总delta-9-tetrahydrocannabinol (THC) 和总cannabigerol (CBG).
- 使用标准正常变量 (SNV) 和萨维茨基-戈莱 (SG) 平滑等预处理技术优化NIR光谱数据.
- 评估用于大麻素预测的开发模型的准确性和可靠性.
主要方法:
- 使用手持式NIR设备分析了整个大麻花朵.
- 频谱数据经过预处理,包括SNV和SG平滑.
- 部分最小平方回归 (PLSR) 模型是用来预测大麻素度的.
- 模型性能使用预测误差 (RMSEP),确定系数 (R2P) 和性能与偏差 (RPD) 的比率来评估模型性能.
主要成果:
- 在总CBD预测方面,SNV预处理产生了最好的结果 (RMSEP=2.228,R2P=0.792,RPD=2.195).
- 原始光谱数据为总THC提供了最准确的预测 (R2P=0.812,RPD=2.306,RMSEP=1.651).
- 原始数据还显示了总CBG预测的最高R2P (0.806).
结论:
- 手持NIR光谱,特别是SNV预处理,是快速和非破坏性量化大麻中总CBD的有希望的工具.
- 该研究强调了便携式NIR技术在优化大麻素分析方面在种植,制药和监管环境中的潜力.
- 对于THC和CBG可能需要进一步优化,但结果表明NIR对于全面的大麻素分析的可行性.
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