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首页> 外文期刊>Journal of chromatography, A: Including electrophoresis and other separation methods >Benchmarking machine learning methods for comprehensive chemical fingerprinting and pattern recognition
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Benchmarking machine learning methods for comprehensive chemical fingerprinting and pattern recognition

机译：用于综合化学指纹和模式识别的基准机学习方法

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Machine learning (ML) has been used previously to recognize particular patterns of constituent compounds. Here, ML is used with comprehensive chemical fingerprints that capture the distribution of all constituent compounds to flexibly perform various pattern recognition tasks. Such pattern recognition requires a sequence of chemical analysis, data analysis, and pattern analysis. Chemical analysis with comprehensive multidimensional chromatography is a maturing approach for highly effective separations of complex samples and so provides a solid foundation for undertaking comprehensive chemical fingerprinting. Data analysis with smart templates employs marker peaks and chemical logic for chromatographic alignment and peak-regions to delineate chromatographic windows in which analytes are quantified and matched consistently across chromatograms to create chemical profiles that serve as complete fingerprints. Pattern analysis uses ML techniques with the resulting fingerprints to recognize sample characteristics, e.g., for classification. Our experiments evaluated the effectiveness of seventeen different ML techniques for various classification problems with chemical fingerprints from a rich data set from 126 wine samples of different varieties, geographic regions, vintages, and wineries. Results of these experiments showed an accuracy range from 58% to 88% for different ML methods on the most difficult classification problems and 96% to 100% for different ML methods on the least difficult classification problems. Averaged over 14 classification problems, accuracy for the different methods ranged from 80% to 90%, with some relatively simple ML techniques among the top-performing methods. (C) 2019 Elsevier B.V. All rights reserved.
机译：先前已经使用了机器学习（ML）以识别成分化合物的特定模式。这里，ML用于综合化学指纹，捕获所有组成化合物的分布，以灵活地执行各种模式识别任务。这种模式识别需要一系列化学分析，数据分析和模式分析。具有综合多维色谱法的化学分析是一种成熟的复杂样品分离的成熟方法，为综合化学指纹识别提供了坚实的基础。具有智能模板的数据分析采用标记峰和化学逻辑，用于色谱对准和峰值区，以描绘色谱窗口，其中分析物被定量分析，并在色谱图中一致地匹配，以产生作为完整指纹的化学分布。图案分析使用ML技术具有所得到的指纹，以识别样本特征，例如，进行分类。我们的实验评估了来自来自不同品种，地理区域，年份，葡萄酒厂和葡萄酒厂的126种葡萄酒样本的丰富数据集的化学指纹，对各种分类问题的有效性。这些实验的结果表明，在最困难的分类问题上的不同ML方法的精度为58％至88％，而不同的ML方法在最困难的分类问题上的96％至100％。平均超过14个分类问题，不同方法的准确性范围为80％至90％，具有一些相对简单的ML技术在最佳的方法中。 （c）2019 Elsevier B.v.保留所有权利。

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