Trace methane detection in the parts per million range is reported using a novel detection scheme based on optical emission spectra from low temperature atmospheric pressure microplasmas. These bright low-cost plasma sources were operated under non-equilibrium conditions, producing spectra with a complex and variable sensitivity to trace levels of added gases. A data-driven machine learning approach based on partial least squares discriminant analysis was implemented for CH 4 concentrations up to 100 ppm in He, to provide binary classification of samples above or below a threshold of 2 ppm. With a low-resolution spectrometer and a custom spectral alignment procedure, a prediction accuracy of 98% was achieved, demonstrating the power of machine learning with otherwise prohibitively complex spectral analysis. This work establishes proof of principle for low cost and high-resolution trace gas detection with the potential for field deployment and autonomous remote monitori...

中文翻译：

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通过等离子光发射光谱和有监督的机器学习检测痕量甲烷

使用基于低温大气压微等离子体发射光谱的新型检测方案，报告了百万分之几的痕量甲烷检测。这些明亮的低成本等离子体源在非平衡条件下运行，产生的光谱对痕量添加气体具有复杂且可变的灵敏度。对于偏高的He中100 ppm的CH 4浓度，采用了基于偏最小二乘判别分析的数据驱动的机器学习方法，以提供高于或低于2 ppm阈值的样品的二进制分类。使用低分辨率光谱仪和自定义的光谱对齐程序，可以达到98％的预测精度，这证明了机器学习的强大功能，而光谱分析却过于复杂。