When laser-induced breakdown spectroscopy (LIBS) was used in online equivalence ratio measurements, the spatial–temporal temperature gradient would lead to significantly varied baselines, which require real-time corrections. In this study, the correlation relationship between spectral intensity and broadband radiation was utilized as the physical constraint in automatic baseline correction. The pixel-to-pixel correlation coefficient on the wavelength (\({R}^{2}\)) was introduced as the “Correlation Spectrum”, and the summation of all correlation coefficients (\(sum{R}^{2}\)) was used as an objective function. By maximizing \(sum{R}^{2}\) using Particle Swarm Optimization algorithm, the key parameters in the adaptive iteratively reweighted Penalized Least Squares (airPLS) method were automatically optimized. The baselines fitted by airPLS could adapt to the plasma excitation variations in a temperature range from 1458 to 1705 K, which improved accuracy in equivalence ratio measurements. The potential advantages of Correlation Spectrum in weak peak identification and broadening effect evaluation were also demonstrated. The benchmarked results suggested that the proposed approach can be conveniently employed in online LIBS measurements and improve its quantification performance.

当激光诱导击穿光谱（LIBS）用于在线当量比测量时，时空温度梯度会导致基线发生显着变化，需要实时校正。本研究利用光谱强度与宽带辐射之间的相关关系作为自动基线校正的物理约束。波长上的像素到像素的相关系数（\({R}^{2}\)）被引入为“相关谱”，所有相关系数的总和（\(sum{R}^{2} }\) ) 被用作目标函数。通过使用粒子群优化算法最大化\(sum{R}^{2}\)，自动优化自适应迭代重加权惩罚最小二乘法 (airPLS) 中的关键参数。 AirPLS拟合的基线可以适应1458至1705 K温度范围内的等离子体激发变化，从而提高了当量比测量的准确性。还论证了相关谱在弱峰识别和展宽效果评估方面的潜在优势。基准测试结果表明，所提出的方法可以方便地应用于在线 LIBS 测量并提高其量化性能。