中红外光谱中的稀疏波长数据:建模方法和道采样方法
Miriam Aledda1, Achim Kohler1, Boris Zimmermann1
1Norwegian University of Life Sciences, Ås, Norway.
Journal of biophotonics
|July 13, 2023
概括
本研究探讨了用于生物样本分析的稀疏红外光谱数据的建模. 部分最小平方回归 (PLSR) 有效地模拟使用最小频道的多频数据,提供了具有成本效益的解决方案.
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:
- 开发具有成本效益的红外仪器,用于生物样本分析.
- 稀少的数据,以有限的单频或狭窄的多频频道为特征,为建模带来了挑战.
- 尽量减少通道数量对于降低仪器成本至关重要.
研究的目的:
- 调查道采样对数据建模结果的影响.
- 为各种类型的稀疏红外数据确定最佳建模算法.
- 评估回归技术,从稀疏的光谱数据中预测脂肪酸成分.
主要方法:
- 从牛奶和真菌样本中模拟稀疏的富里埃变换红外 (FTIR) 光谱.
- 部分最小平方回归 (PLSR),多重线性回归 (MLR) 和随机森林 (RF) 的应用用于预测建模.
- 基于不同的频谱通道数量和类型的模型性能评估.
主要成果:
- PLSR在多频稀疏数据方面表现出色,只用三个频道实现了强大的校准模型.
- 对于单频稀疏数据,MLR和RF方法的结果相似,总共需要9个频道.
- 这些发现表明,特定的算法非常适合不同的稀疏数据结构.
结论:
- PLSR是一种高效的算法,用于建模多频稀疏红外光谱数据.
- 最佳的算法选择取决于稀疏数据的性质 (单频与多频).
- 这项研究为开发生物样本准确和经济高效的光谱分析方法提供了途径.
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