Laser-induced breakdown spectroscopy and digital image data fusion for determination of the Al, Ca, Fe, Mg, and P in mineral fertilizer: overcome matrix effects in solid direct analysis

Laser-induced breakdown spectroscopy (LIBS) and digital images were evaluated in the modeling for the prediction of Al, Ca, Fe, Mg, and P contents in mineral fertilizer samples. For modeling, univariate [matrix-matching calibration (MMC)] and multivariate [multiple linear regression (MLR) using only LIBS data, and data fusion (LIBS + digital image)] calibration strategies were evaluated. The predictive capacity of the models was increased in the following order: MMC < MLR (LIBS) < data fusion. Compared with the MMC and MLR (LIBS data only), the root mean square error (data fusion) values were 17% to 80% lower, demonstrating the improvement in accuracy. (c) 2023 Optica Publishing Group

Automated summary

This study evaluated the use of laser-induced breakdown spectroscopy (LIBS) and digital images for modeling the prediction of Al, Ca, Fe, Mg, and P contents in mineral fertilizer samples. The modeling strategies of matrix-matching calibration (MMC), multiple linear regression (MLR) using only LIBS data, and data fusion (LIBS + digital image) were compared. The results showed that data fusion had higher predictive capacity than MMC and MLR (LIBS data only), with a reduction in root mean square error (RMSE) values by 17% to 80%, indicating improved accuracy.