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Potassium is the active element of potash fertilizer, its concentration determines the quality of the product. Self-absorption occurs in laser-induced breakdown spectroscopy (LIBS) analysis of potash, since potassium is the major element with a concentration that can reach the level of 95 wt.%. The present work simulated an online analysis of potassium in potash with a laboratory setup, LIBS spectra were recorded on 27 potash samples collected from a factory in Qinghai province in China with different potassium concentrations in the range from 90 to 97 wt.%. The self-absorption correction was applied to the spectra for K I 404.4 nm and K I 693.9 nm lines. Multivariate regression based on machine learning were processed with the self-absorption corrected spectra in order to get a better potassium determination performance compared to that with uncorrected spectra. The results show the effectiveness of self-absorption correction in the improvement of the analytical performance. The obtained multivariate regression model presents a satisfactory performance to fill the requirements of the aimed application of online and real-time analysis of potash on a product transportation belt in a production site with respect to the specific requirements of the related current national standards. [ABSTRACT FROM AUTHOR]