Abstract Hot metal desulfurization is the main process step for removing sulfur in blast furnace-based steelmaking. A desulfurization reagent is pneumatically injected into the hot metal through a submerged lance causing it to vibrate. The aim of this study is to develop a mechanical vibration measurement-based method that can detect changes in the gas-forming properties of the reagent. The detection is performed using Elastic Net regression and eXtreme Gradient Boosting-based classification models the classification performance of which is compared. The lance aging causes changes in its dynamic characteristics, and the disturbing effect of this is removed from the measured data of the lance vibration prior to classification by means of a developed cleaning algorithm. The best classification performance in detecting changes in the gas-forming properties, with an area under the receiver operating characteristic curve of 0.916 and Matthews correlation coefficient of 0.699, is achieved using an Elastic Net regression-based classification model. The results of this work serve as a basis for developing industrial applications in which the effective utilization of the excitation, such as vibrations generated by the gas formation can be utilized for process monitoring and as a soft sensor for predicting the reagent-induced process variance.