Ion mobility spectrometry is an important rapid analysis method. However, it is difficult to achieve quantitative analysis when spectral peaks overlap. A new method for analyzing ion mobility spectra is presented here. The method achieves quantitative analysis by combining the advantages of the peak model (in terms of optimal physical and chemical interpretation of the system of interest) and the multiscale orthogonal matching pursuit algorithm (in terms of extracting characteristic peaks). A simulated data set, constructed using the peak model, containing overlapping peaks was analyzed to demonstrate the ability of the multiscale orthogonal matching pursuit algorithm to decompose overlapping peaks. Real data sets for methyl salicylate and a mixture of acetone and methyl salicylate at sixteen concentrations were generated using a vapor generator (using permeation tubes). The characteristic peaks were extracted using the multiscale orthogonal matching pursuit algorithm. Univariate calibrations using the peak area and peak height were prepared to allow quantitative analyses to be performed. Multivariate calibrations using partial-least-squares and poly-partial-least-squares were prepared and the results were compared with the univariate calibration results. Markedly better or similar predictions were made using the univariate calibration models involving physical and chemical interpretations than using the multivariate calibration models.

中文翻译：

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多尺度正交匹配追踪算法结合峰模型解释离子迁移谱并实现定量分析

离子迁移谱是一种重要的快速分析方法。然而，当光谱峰重叠时，难以实现定量分析。这里介绍了一种分析离子迁移谱的新方法。该方法结合了峰模型（在对感兴趣系统的最佳理化解释方面）和多尺度正交匹配追踪算法（在提取特征峰方面）的优点，实现了定量分析。分析了使用峰模型构建的包含重叠峰的模拟数据集，以证明多尺度正交匹配追踪算法分解重叠峰的能力。使用蒸汽发生器（使用渗透管）生成了十六个浓度的水杨酸甲酯和丙酮和水杨酸甲酯的混合物的真实数据集。采用多尺度正交匹配追踪算法提取特征峰。使用峰面积和峰高进行单变量校准，以便进行定量分析。准备使用偏最小二乘法和多偏最小二乘法的多变量校准，并将结果与​​单变量校准结果进行比较。使用涉及物理和化学解释的单变量校准模型比使用多变量校准模型做出了明显更好或相似的预测。采用多尺度正交匹配追踪算法提取特征峰。使用峰面积和峰高进行单变量校准，以便进行定量分析。准备使用偏最小二乘法和多偏最小二乘法的多变量校准，并将结果与​​单变量校准结果进行比较。使用涉及物理和化学解释的单变量校准模型比使用多变量校准模型做出了明显更好或相似的预测。采用多尺度正交匹配追踪算法提取特征峰。使用峰面积和峰高进行单变量校准，以便进行定量分析。准备使用偏最小二乘法和多偏最小二乘法的多变量校准，并将结果与​​单变量校准结果进行比较。使用涉及物理和化学解释的单变量校准模型比使用多变量校准模型做出了明显更好或相似的预测。准备使用偏最小二乘法和多偏最小二乘法的多变量校准，并将结果与​​单变量校准结果进行比较。使用涉及物理和化学解释的单变量校准模型比使用多变量校准模型做出了明显更好或相似的预测。准备使用偏最小二乘法和多偏最小二乘法的多变量校准，并将结果与​​单变量校准结果进行比较。使用涉及物理和化学解释的单变量校准模型比使用多变量校准模型做出了明显更好或相似的预测。