Central European Institute of Technology, Brno University of Technology, Purkyňova 123, Brno, 612 00, Czech Republic.
AtomTrace a.s., Kolejní 9, 612 00, Brno, Czech Republic.
Sci Rep. 2017 Jun 9;7(1):3160. doi: 10.1038/s41598-017-03426-0.
In this work, we proposed a new data acquisition approach that significantly improves the repetition rates of Laser-Induced Breakdown Spectroscopy (LIBS) experiments, where high-end echelle spectrometers and intensified detectors are commonly used. The moderate repetition rates of recent LIBS systems are caused by the utilization of intensified detectors and their slow full frame (i.e. echellogram) readout speeds with consequent necessity for echellogram-to-1D spectrum conversion (intensity vs. wavelength). Therefore, we investigated a new methodology where only the most effective pixels of the echellogram were selected and directly used in the LIBS experiments. Such data processing resulted in significant variable down-selection (more than four orders of magnitude). Samples of 50 sedimentary ores samples (distributed in 13 ore types) were analyzed by LIBS system and then classified by linear and non-linear Multivariate Data Analysis algorithms. The utilization of selected pixels from an echellogram yielded increased classification accuracy compared to the utilization of common 1D spectra.
在这项工作中，我们提出了一种新的数据采集方法，可显著提高激光诱导击穿光谱（LIBS）实验的重复率，该实验通常使用高端的阶梯光栅光谱仪和增强型探测器。由于最近的 LIBS 系统采用了增强型探测器及其缓慢的全帧（即阶梯光栅）读出速度，因此需要进行阶梯光栅到 1D 光谱的转换（强度与波长），从而导致其重复率适中。因此，我们研究了一种新的方法，其中仅选择阶梯光栅中最有效的像素，并直接将其用于 LIBS 实验中。这种数据处理导致了显著的可变降选（超过四个数量级）。通过 LIBS 系统分析了 50 个沉积矿石样品（分布在 13 种矿石类型中），然后使用线性和非线性多元数据分析算法进行分类。与使用常见的 1D 光谱相比，从阶梯光栅中选择像素的使用提高了分类准确性。
