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Random forest and Long Short-Term Memory based Machine Learning Models for Classification of Ion Mobility Spectrometry Spectra

机译:用于离子迁移光谱谱分类的随机森林和长短期内存基于机械学习模型

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The development of alarm algorithms in ion mobility spectrometry (IMS) based chemical vapor detection is challenged by the presence of overlapping chemical peaks. IMS technology identifies a chemical through hard-coded alarm windows. Alarm windows are designed as range of reduced mobility values, and act as an IF-THEN statement. Where if a peak forms in the region it then assigns a preset alarm label. A majority of IMS alarm algorithm design has relied on setting boundary conditions based on a statistical variance in product ion peak positions. To develop these alarm windows for IMS detectors the variance in peak position had to be captured through extensive laboratory testing. These windows are determined through time consuming and rigorous laboratory testing across multiple detectors under multiple conditions. Machine learning (ML) is a field of science that intersects with computer science and mathematics to "teach" a computer using large amounts of data. The development of traditional alarm algorithms IMS has left a plethora of data available to be explored by ML techniques. Presented here is a random forest (RF) classification model along with a long short-term memory (LSTM) based neural network model to label the spectra of IMS data with high accuracy.
机译:基于离子迁移光谱(IMS)的化学蒸汽检测中的报警算法的发展是通过重叠化学峰的存在攻击。 IMS技术通过硬编码警报窗口识别化学品。警报窗口设计为减少的移动性值,并充当if-then语句。如果该区域中的峰形表单,则它会分配预设警报标签。大多数IMS报警算法设计依赖于基于产品离子峰位置的统计方差设定边界条件。要为IMS探测器开发这些警报窗口,探测器必须通过广泛的实验室测试来捕获峰值位置的方差。在多种条件下,通过跨多个探测器的耗时和严格的实验室测试来确定这些窗口。机器学习(ML)是一种与计算机科学和数学相交的科学领域,使用大量数据与计算机“教导”计算机。传统报警算法IMS的开发留下了ML技术可用于探索多种数据。这里介绍的是随机森林(RF)分类模型以及基于长期的短期内存(LSTM)的神经网络模型,以高精度地标记IMS数据的光谱。

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