在Lonicera caerulea中的多的定量分析基于中红外光谱和混合变量选择
Haiwei Wu1, Xuexin Li1, Jianwei Liu1
1College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China.
Molecules (Basel, Switzerland)
|February 27, 2026
概括
一种新的混合变量选择策略有效地使用中红外光谱学量化蓝花 (Lonicera caerulea) 聚. 这种方法提高了功能性食品质量控制的准确性.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
背景情况:
- 蓝色蜜 (Lonicera caerulea) 富含抗氧化物多,对功能性食物至关重要.
- 精确的多量化对于质量控制至关重要,但对于复杂的光谱数据来说具有挑战性.
研究的目的:
- 开发一个强大的定量预测模型,用于蓝花的多含量.
- 为高维,小样本光谱数据提出一种新的混合变量选择策略.
主要方法:
- 在191个Lonicera caerulea样本上使用了中红外光谱 (MIR) 光谱.
- 实施了混合变量选择,结合了PLS-VIP和随机森林回归.
- 开发和优化预测模型,包括XGBoost,PLS,RFR和SVR.
主要成果:
- 混合方法实现了86.8%的维度减小,选择了984个关键波长.
- 优化的XGBoost模型显示出高精度 (R2 = 0.92,RPD = 3.47).
- 性能明显超过了CARS方法,R2提高了16.3%,RPD提高了55.2%.
结论:
- 拟议的混合变量选择策略有效地处理小样本场景中的高维MIR光谱数据.
- 这种方法提供了一种可靠的,非破坏性的工具,用于快速的蓝花多分析.
- 这些发现支持功能性食品行业的先进质量控制.
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