在大麻油中使用FT-NIR光谱学与PLS-R模型相结合,对十种大麻素的快速和可靠量化
Matan Birenboim1, David Kenigsbuch2, Jakob A Shimshoni1
1Department of Food Science, Institute for Postharvest and Food Sciences, Agricultural Research Organization, Volcani Center, P.O. Box 15159, Rishon LeZion 7505101, Israel.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|November 20, 2025
概括
福利埃变换近红外 (FT-NIR) 光谱学可以快速量化大麻油中的大麻素. 这种方法使用部分最小平方回归 (PLS-R),为质量控制提供了具有成本效益的替代方案.
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:
- 传统的染色学方法用于大麻素量化是耗时的.
- 对大麻油的FT-NIR光谱的应用,用于多种大麻素的分析,尚未得到充分的研究.
研究的目的:
- 用PLS-R评估FT-NIR光谱法,同时量化大麻油中的十种大麻素.
- 为了比较FT-NIR模型对油的性能与干燥或湿大麻花朵的性能.
主要方法:
- 从17种大麻油配方中获得了102个FT-NIR光谱.
- 根据HPLC-PDA参考数据开发了部分最小平方回归 (PLS-R) 模型.
- 使用交叉验证和外部预测验证的模型.
主要成果:
- 所有的PLS-R模型都显示出良好的到优秀的预测能力 (R2>0.75,RPD>1.8,RPIQ>2.0).
- 大麻油中七种大麻素的量化模型首次被开发出来.
- 基于油的模型的性能与大麻花朵的模型相比或优于大麻花朵.
结论:
- FT-NIR光谱是一种快速,非破坏性和具有成本效益的工具,用于大麻油中的大麻素量化.
- 这种技术可以实现高频,现场测试,用于质量控制和稳定性评估.
- 油基质的同质性提高了光谱分析的性能.
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