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Identification of Oil Spills by Near-Infrared Spectroscopy (NIR) and Support Vector Machine (SVM)

机译:通过近红外光谱(NIR)和支持向量机(SVM)识别溢油

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摘要

The identification of the spilled oil is an essential and important part in the investigation and handling of oil spill accidents. The combination of near-infrared spectroscopy (NIR) and chemometrics is ideal for such a situation. NIR spectroscopy is a powerful and effective technique and qualitative information can be obtained with classification models. Support vector machines (SVM) have been introduced recently in chemometrics and have proven to be powerful in NIR spectra classification tasks, such as material identification and food discrimination. In this work, the SVM is utilized to classify near infrared spectroscopy of simulated spilled oils of gasoline, diesel fuel and kerosene on the marine. A good classification performance is obtained :the identification rate were 100%, 96% and 98% on the test sets respectively.
机译:溢油的识别是调查和处理溢油事故的关键和重要部分。近红外光谱(NIR)和化学计量学的结合非常适合这种情况。近红外光谱是一种强大而有效的技术,可以通过分类模型获得定性信息。支持向量机(SVM)最近已在化学计量学中引入,并被证明在NIR光谱分类任务(例如材料识别和食品识别)中功能强大。在这项工作中,支持向量机用于对海洋中汽油,柴油和煤油的模拟溢油进行近红外光谱分类。获得了良好的分类性能:在测试集上的识别率分别为100%,96%和98%。

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