Multiway Calibration Strategies in Laser-Induced Breakdown Spectroscopy: A Proposal

In this study, a new approach to laser-induced breakdown spectroscopy (LIBS) data modeling using multiway algorithms was investigated. Two case studies, parallel factor analysis (PARAFAC) and unfolded-partial least-squares with residual bilinearization (U-PLS/RBL) algorithms were used in (1) the determination of Al, Cu, and Fe in samples of reference material of printed circuit board (PCB) from electronic waste and (2) the determination of Ca, K, and Mg in samples of a human mineral supplement, where depth was used to obtain multidimensional data in the first case and delay-time in the second. In addition, univariate calibration was applied and compared with the multiway approaches. In all cases, the calibration data set was prepared from salts. PARAFAC showed satisfactory results in the first study, with low prediction errors and good accuracy for most samples, and the U-PLS/RBL algorithm presented the best performance for mineral supplement samples.

Automated summary

A new approach to laser-induced breakdown spectroscopy (LIBS) data modeling using multiway algorithms was investigated in this study, with two case studies (PARAFAC and U-PLS/RBL algorithms) applied for element determination in different sample types. The results showed satisfactory performance of the algorithms in the respective cases, demonstrating the potential of multiway algorithms in LIBS data analysis.