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Laser-induced breakdown spectroscopy (LIBS) is a popular, fast elementalanalysis technique used to determine the chemical composition of targetsamples, such as in industrial analysis of metals or in space exploration.Recently, there has been a rise in the use of machine learning (ML) techniquesfor LIBS data processing. However, ML for LIBS is challenging as: (i) thepredictive models must be lightweight since they need to be deployed in highlyresource-constrained and battery-operated portable LIBS systems; and (ii) sincethese systems can be remote, the models must be able to self-adapt to anydomain shift in input distributions which could be due to the lack of differenttypes of inputs in training data or dynamic environmental/sensor noise. Thison-device retraining of model should not only be fast but also unsupervised dueto the absence of new labeled data in remote LIBS systems. We introduce alightweight multi-layer perceptron (MLP) model for LIBS that can be adaptedon-device without requiring labels for new input data. It shows 89.3% averageaccuracy during data streaming, and up to 2.1% better accuracy compared to anMLP model that does not support adaptation. Finally, we also characterize theinference and retraining performance of our model on Google Pixel2 phone.

Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy