Artificial Neural Networks (ANNs) versus Partial Least Squares (PLS) for spectral interference correction for taking part of the lab to the sample types of applications: an experimental study

机译：人工神经网络（ANN）与偏最小二乘（PLS）进行光谱干扰校正，以使实验室参与应用的样本类型：一项实验研究

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Interference and in particular spectral interference is a well documented problem in optical emission spectrometry. For example, it is commonly encountered even when commercially-available spectrometers with medium to high resolution are used (e.g., those with focal lengths of 0.75 m to 1 m). Such interference must be corrected. Although portable spectrometers are better suited for "taking part of the lab to the sample" types of applications, the effects of interference become more pronounced due to the short focal length of such spectrometers (e.g., 10 cm to 15 cm). We describe use of Artificial Neural Networks (ANNs) and of Partial Least Squares (PLS) methods for spectral interference correction.

机译：干扰，尤其是光谱干扰是光发射光谱学中一个有据可查的问题。例如，即使使用具有中等至高分辨率的市售光谱仪（例如，焦距为0.75 m至1 m的那些光谱仪），也经常遇到这种情况。必须纠正这种干扰。尽管便携式光谱仪更适合于“将实验室的一部分带到样品中”类型的应用，但是由于这种光谱仪的焦距短（例如10厘米至15厘米），所以干扰的影响变得更加明显。我们描述了使用人工神经网络（ANN）和偏最小二乘（PLS）方法进行频谱干扰校正。

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来源
《Sensing and analysis technologies for biomedical and cognitive applications 2016》|2016年|98710M.1-98710M.8|共8页
会议地点 Baltimore MD(US)
作者
Z. Li; Vassili Karanassios;
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作者单位

Department of Chemistry and Waterloo Institute for Nanotechnology University of Waterloo, Waterloo, Ontario Canada N2L 3G1;

Department of Chemistry and Waterloo Institute for Nanotechnology University of Waterloo, Waterloo, Ontario Canada N2L 3G1;

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原文格式 PDF
正文语种 eng
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Artificial Neural Networks (ANNs); Partial Least Squares (PLS); Spectral interference correction;

机译：人工神经网络（ANN）；偏最小二乘（PLS）；光谱干扰校正;

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Artificial Neural Networks (ANNs) versus Partial Least Squares (PLS) for spectral interference correction for taking part of the lab to the sample types of applications: an experimental study

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