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Multivariate Analysis as a Tool to Identify Concentrations from Strongly Overlapping Gas Spectra

机译:多元分析作为从强重叠气体光谱中识别浓度的工具

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摘要

We applied a multivariate analysis (MVA) to spectroscopic data of gas mixtures in the mid-IR in order to calculate the concentrations of the single components which exhibit strongly overlapping absorption spectra. This is a common challenge in broadband spectroscopy. Photoacoustic (PA) measurements of different volatile organic compounds (VOCs) in the wavelength region of 3250 nm to 3550 nm served as the exemplary detection technique. Partial least squares regression (PLS) was used to calculate concentrations from the PA spectra. After calibration, the PLS model was able to determine concentrations of single VOCs with a relative accuracy of 2.60%.
机译:我们对中红外中的气体混合物的光谱数据进行了多元分析(MVA),以计算表现出强烈重叠的吸收光谱的单个组分的浓度。这是宽带光谱学中的普遍挑战。在3250 nm至3550 nm波长范围内对不同挥发性有机化合物(VOC)的光声(PA)测量用作示例检测技术。偏最小二乘回归(PLS)用于从PA光谱计算浓度。校准后,PLS模型能够以2.60%的相对准确度确定单个VOC的浓度。

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