Long-term reproducibility improvement of LIBS quantitative analysis based on multi-period data fusion calibration...

Long-term reproducibility is one of the important problems that urgently need to be solved in the quantitative analysis of laser-induced breakdown spectroscopy (LIBS). In this work, a new calibration method based on multi-period data fusion was proposed. Under the same experimental equipment and parameters, LIBS data collected at different times were fused together to establish calibration models. The spectra of the same set of standard samples were collected once a day for 20 days, the spectral data from the first 10 days were used as the training set for the calibration model, and the data from the last 10 days were used as the test set. As a comparison, three types of calibration models of four elements (Mn, Ni, Cr, and V) were established, including the internal calibration model (IS-1) based on the data from first day, multi-period data fusion internal calibration model established using internal calibration (IS-10) and genetic algorithm based back-propagation artificial neural network (GA-BP-ANN) based on the data from first 10 days. The test set spectral data were used to verify the prediction effect of the above three models. It was found that the GA-BP-ANN models have the lowest average relative errors (ARE) and the average standard deviations (ASD). Therefore, the proposed multi-period data fusion GA-BP-ANN model provides a novel method for improving the reproducibility of LIBS long-term repeated measurement.