A severely overlapped-spectra decomposition approach for plasma emission spectroscopy

Ideal decomposition of severely overlapped spectra due to limited spectral resolution remains an unsolved puzzle for spectroscopic technologies. Although ultra-high-resolution spectrometers can resolve spectral overlap problems, they often entail high costs and significant signal loss, which naturally leads to relative high limit of detection (LOD). In this work, based on the assumption that the measured spectrum is the convolution of the much less overlapped real plasma emission profile and the spectrometer instrumental response, we propose a center-wavelength coupled with broadening-width-ratio constrained decomposition (CC-BCD) method for severely overlapped peaks from relatively low-resolution spectrometers. More specifically, the method incorporates the extra supporting information (the central wavelengths and the ratio of broadening width of the overlapped peaks obtained from ultra-high-resolution spectra) as hard constraints into the decomposition model, transforming the model from an otherwise underdetermined mathematical fit into a physically guided reconstruction and enabling accurate and stable resolution of severely overlapped peaks. The method was successfully applied for plasma emission technology such as laser-induced breakdown spectroscopy (LIBS) and spark-discharge optical emission spectroscopy (SD-OES). For uranium ores analysis using LIBS, the completely overlapped peaks (U II 385.957 nm and Fe I 385.991 nm) were fully resolved, reducing LOD to 7.3 mg/kg, two orders of magnitude compared with that of using ultra-high-resolution spectrometer and the lowest record ever for ores. For brass sample detection using SD-OES, the severely overlapped peaks (Zn I 328.233 nm and Cu I 328.272 nm) were also resolved, reducing LOD for Zn from 0.39 wt% to 0.11 wt%. The proposed method enables relatively low-resolution spectrometers to achieve high resolution capabilities while retaining the high optical throughput, thereby providing a highly sensitive and low-cost approach for scenarios where the analysis heavily relies on severely overlapped lines, such as ultra-high-sensitivity analysis of uranium in complex matrices.