LA-TReQNet: Improving Multielement Quantification Model for Laser Ablation Inductively Coupled Plasma Mass Spectrometry Based on Deep Learning Network

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP–MS) is a widely used quantitative technique. However, conventional calibration approaches that depend on internal standards, external reference materials, and linear regression are subject to practical limitations. To address these challenges, we present LA-TReQNet, an end-to-end deep learning framework, that enables fully automated quantitative calibration in LA-ICP–MS. A CNN-LSTM architecture was trained on a data set of 221,364 labeled mass spectra from 5676 samples. We demonstrate that preprocessing of mass spectrometry data substantially impacts model performance and propose an optimized strategy incorporating power transformer-based standardization and data set grouping. By training on extensive multielement data sets, the deep learning model captures complex empirical relationships, thus establishing for the first time a standard-free calibration approach. The optimized LA-TReQNet model accurately quantified 39 elements in three RMs (BCR-2G, BHVO-2G, and BIR-1G) from independent laboratories, confirming its robustness to data source variations. When applied to a broader set of RMs, LA-TReQNet achieved deviations of 0.2% ± 5.8% (SD, n = 198) for major elements and −0.9% ± 9.2% (SD, n = 898) for trace elements relative to certified reference values. The results confirm that deep learning-based elemental quantification achieves accuracy on par with conventional approaches while eliminating the reliance on internal or external standards, thus significantly expanding the applicability of LA-ICP–MS to complex and diverse samples. Moreover, LA-TReQNet eliminates data quality fluctuations from researcher experience differences in traditional handling, improving both processing efficiency and result stability.