Non-contact air humidity recognition based on the fusion of laser-induced breakdown spectroscopy-acoustic signals and machine learning

Addressing the limited adaptability of traditional air humidity detection technology in complex environments, a multimodal air humidity recognition system based on the fusion of laser-induced breakdown spectroscopy (LIBS) and laser-induced plasma acoustics (LIPA) combined with machine learning is proposed for the first time. The experiment has verified the synergistic effect of LIPA and LIBS for the first time: the spectral intensity of hydrogen atoms in LIBS increases with the increase of relative air humidity, while the LIPA signal exhibits humidity-specific acoustic characteristics, with complementary information between the two. Principal Component Analysis (PCA) is applied to reduce the dimensionality and extract features from LIBS and LIPA data, and a Decision Tree (DT) model is used to achieve accurate recognition of four types of air humidity. The fusion data achieves a classification accuracy of 100%, significantly outperforming single LIBS technology. This study verifies the feasibility of the fusion technology, provides a new solution for high-precision humidity monitoring, and is of pioneering significance.