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首页> 外文期刊>Journal of Analytical Atomic Spectrometry >Identifying laser-induced plasma emission spectra of particles in a gas-solid flow based on the standard deviation of intensity across an emission line
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Identifying laser-induced plasma emission spectra of particles in a gas-solid flow based on the standard deviation of intensity across an emission line

机译：根据跨发射线的强度标准偏差确定气固流中颗粒的激光诱导等离子体发射光谱

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摘要

A new conditional data processing scheme named the standard deviation (SD) method is presented and evaluated for identifying the spectral data of a gas-solid flow based on laser-induced breakdown spectroscopy. The SD method is compared with two conditional data processing methods called the signal-to-noise ratio (SNR) method and the absolute peak intensity method. First, the performance of the three methods for identifying the spectral data of the same fly ash sample was compared. Then, the stability of the three methods was checked by identifying the spectral data of a set of 12 coal samples under different experimental conditions. The rejection rate, false rejection rate and false acceptance rate under various conditional analysis threshold values were used to evaluate these three different methods. The characteristic peaks at Si 288.16 nm and C 247.86 nm were selected for the analysis of fly ash and coal samples, respectively. The results show that true data and spurious data could be completely and accurately identified by the SD method. Moreover, it has been proved that the threshold values of the absolute peak intensity method and the SNR method fluctuate dramatically while the threshold value of the SD method remains stable under different experimental conditions. Compared with the other two methods, the SD method has better applicability and reliability when faced with variable detection conditions. So it has a greater advantage in identifying spurious laser-induced plasma emission spectra of particles in a gas-solid flow.
机译：提出了一种新的条件数据处理方案，称为标准偏差（SD）方法，并进行了评估，以基于激光诱导击穿光谱法识别气固流的光谱数据。将SD方法与两种条件数据处理方法（称为信噪比（SNR）方法和绝对峰值强度方法）进行比较。首先，比较了用于识别同一粉煤灰样品光谱数据的三种方法的性能。然后，通过确定一组12个煤样品在不同实验条件下的光谱数据来检查这三种方法的稳定性。使用各种条件分析阈值下的拒绝率，错误拒绝率和错误接受率来评估这三种不同的方法。选择Si 288.16 nm和C 247.86 nm处的特征峰分别分析粉煤灰和煤样品。结果表明，采用SD方法可以完全，准确地识别真实数据和虚假数据。而且，已经证明，在不同的实验条件下，绝对峰强度法和SNR法的阈值波动很大，而SD法的阈值保持稳定。与其他两种方法相比，SD方法在面对变化的检测条件时具有更好的适用性和可靠性。因此，它在识别气固流中的粒子的伪激光诱导的等离子体发射光谱方面具有更大的优势。

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