Laser-induced breakdown spectroscopy (LIBS) is a promising multi-elemental analysis technique and has the advantages of rapidness and minimal sample preparation. In traditional LIBS measurement, sample spectra are generally collected based on a single set of fixed experimental parameters, such as laser energy and delay time. When samples have the same main components and similar component concentrations, the difference in their spectral intensities becomes less obvious. This can lower the sensitivity of LIBS measurement and pose a threat to the accuracy and robustness of LIBS qualitative analysis. In this work, we propose a new method to increase the spectral difference between similar samples, namely multiple-setting spectra. For each sample, it adopts different sets of experimental parameters and obtains a group of spectra to increase the fingerprint spectral information. The effectiveness of the proposed method is theoretically verified and then tested on 11 similar coal samples. Specifically, the sample spectra were collected with different laser energy and delay time, and processed by principal component analysis (PCA) and Davies-Bouldin index (DBI). The results show that the use of multiple-settings spectra can significantly improve the sample discrimination accuracy from 81.8% to 96.4%. In addition, the proposed method can maintain the efficiency and cost of LIBS measurement.

激光诱导击穿光谱 (LIBS) 是一种很有前途的多元素分析技术，具有快速和样品制备最少的优点。在传统的 LIBS 测量中，样品光谱通常是根据一组固定的实验参数收集的，例如激光能量和延迟时间。当样品具有相同的主要成分和相似的成分浓度时，它们的光谱强度差异变得不那么明显。这会降低 LIBS 测量的灵敏度，并对 LIBS 定性分析的准确性和稳健性构成威胁。在这项工作中，我们提出了一种增加相似样本之间光谱差异的新方法，即多重设置光谱。对于每个样本，它采用不同组的实验参数，得到一组光谱，增加指纹光谱信息。对该方法的有效性进行了理论验证，并在11个相似煤样上进行了测试。具体而言，采用不同的激光能量和延迟时间采集样品光谱，并通过主成分分析（PCA）和戴维斯-博尔丹指数（DBI）进行处理。结果表明，使用多设置光谱可以显着提高样品判别精度，从81.8%提高到96.4%。此外，所提出的方法可以保持LIBS测量的效率和成本。用不同的激光能量和延迟时间采集样品光谱，并通过主成分分析（PCA）和戴维斯-博尔丹指数（DBI）进行处理。结果表明，使用多设置光谱可以显着提高样品判别精度，从81.8%提高到96.4%。此外，所提出的方法可以保持LIBS测量的效率和成本。用不同的激光能量和延迟时间采集样品光谱，并通过主成分分析（PCA）和戴维斯-博尔丹指数（DBI）进行处理。结果表明，使用多设置光谱可以显着提高样品判别精度，从81.8%提高到96.4%。此外，所提出的方法可以保持LIBS测量的效率和成本。