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使用多源数据开发机器学习模型,用于Eucommia ulmoides叶的地理可追溯性和内容预测

Yanying Zhang¹, Xinyan Zhu², Yuanzhong Wang-²

¹College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming, 650500, China; Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|March 11, 2024

概括

这项研究使用机器学习和光谱学来追踪Eucommia ulmoides叶的起源并预测其化学含量. 一项新的战略有效地评估了药用植物的质量,实现了地理可追溯性的100%准确性.

科学领域:

食品科学与技术食品科学与技术
药理学是指药理学,即药理学是指药理学.
分析化学是一种分析化学.

背景情况:

对Eucommia ulmoides叶片产品的需求不断增长,需要进行严格的质量和安全评估.
评估药用植物的传统方法往往缺乏速度和科学严谨性.
地理可追溯性和化学含量预测对于确保Eucommia ulmoides叶的质量至关重要.

研究的目的:

开发和评估机器学习模型,用于Eucommia ulmoides叶的地理可追溯性和化学含量预测.
研究数据预处理技术和数据融合策略对模型性能的影响.
建立一个有效的战略,以快速和科学地评估Eucommia ulmoides叶的质量.

主要方法:

利用了包括红外光谱在内的多源数据.
应用了传统的机器学习算法,例如部分最小平方区分分析 (PLS-DA) 和部分最小平方回归 (PLSR).
探索各种数据预处理方法 (例如,第一个导数) 和低级数据融合策略.

主要成果:

使用PLS-DA实现了地理可追溯性100%的准确性,其中包括两种红外光谱技术的低水平融合和第一个衍生预处理.
对奥库,基尼酸和酸的最佳PLSR模型显示了令人满意的预测性能 (RPD>2.0).
素的PLSR模型显示预测能力较弱 (RPD = 1.541),表明需要进一步改进.

结论:

拟议的战略有效地实现了Eucommia ulmoides叶的地理可追溯性和化学含量预测.
红外光谱技术的低级数据融合与特定的预处理相结合,显著提高了可追溯性准确性.
这项研究为其他食品和药用植物的质量评估提供了有价值的见解和方法.

关键词:

内容预测内容预测叶子Eucommia ulmoides的叶子可以使用.地理可追溯性地理可追溯性机器学习是机器学习.质量评估质量评价质量评价

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