使用atmospheric pressure等离子体光学发射光谱的机器学习分析检测甲烷至1 ppm
Methane detection to 1 ppm using machine learning analysis of atmospheric pressure plasma optical emission spectra
摘要：
从一个小体积，5 μ L，大气压RF驱动的氦等离子体的光发射光谱结合偏最小二乘判别分析（PLS-DA）用于检测痕量浓度的甲烷气体。获得了1 ppm的检测限和样品浓度高达100 ppm的CH 4进行了分类使用九类模型。为了解决光谱发射数据的高维性和共线性问题，研究了一系列算法增强，包括正则化、简单数据分割和子集选择、VIP特征选择和波长变量压缩。这些方法显示出显著减少波长变量和光谱分辨率/带宽的数量的潜力。在更具挑战性的多段训练测试场景下，波长可变压缩表现出可靠的预测性能，准确度值> 97%。等离子体电子能量分布函数的简单建模突出了目标甲烷、其分解产物和大气杂质之间的复杂交叉敏感性及其对激发和发射的影响。
英文摘要：
Optical emission spectroscopy from a small-volume, 5 uL, atmospheric pressure RF-driven helium plasma was used in conjunction with Partial Least Squares Discriminant Analysis (PLS-DA) for the detection of trace concentrations of methane gas. A limit of detection of 1 ppm was obtained and sample concentrations up to 100 ppm CH4 were classified using a nine-category model. A range of algorithm enhancements were investigated including regularization, simple data segmentation and subset selection, VIP feature selection and wavelength variable compression in order to address the high dimensionality and collinearity of spectral emission data. These approaches showed the potential for significant reduction in the number of wavelength variables and the spectral resolution/bandwidth. Wavelength variable compression exhibited reliable predictive performance, with accuracy values > 97%, under more challenging multi-session train - test scenarios. Simple modelling of plasma electron energy distribution functions highlights the complex cross-sensitivities between the target methane, its dissociation products and atmospheric impurities and their impact on excitation and emission.
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