Adaptive learning for soil classification in laser-induced breakdown spectroscopy streaming

The application of machine learning (ML) has accelerated the development of laser-induced breakdown spectroscopy (LIBS) in soil analysis. However, analyzing remote LIBS data in real time using ML is challenging due to several factors. Firstly, building robust ML models requires extensive calibration datasets, which are not always possible with limited LIBS experimental data. Secondly, matrix effects can worsen LIBS performance, and changes in sample physical properties or the apparatus can impact the distribution and intensity of emission lines. These issues may lead to concept drift in real-time/online data streaming, causing the relationship between the input and the target spectra to change over time. Consequently, an ML model designed for one LIBS system may not apply to another. To conquer these challenges, we propose a framework based on transfer learning to use limited experimental data and adapt to the emission line variation in the LIBS streaming. A model is first pre-trained using a large labelled source dataset and then fine-tuned with new experimental measurements to classify soil samples. LIBS measurements are conducted with variations in sample properties and experimental parameters to simulate differences in remote LIBS sensors. The collected spectra are fed into the model by chunks, and data evolution is dynamically learned by self-balanced learning to self-adapt to the domain shift. The proposed framework is found effective in improving classification accuracy during data streaming by implementing transfer learning and supporting adaptation compared to the literature. The code of the proposed method is available in the GitHub at https://github.com/kelci2017/LIBS_streaming .