A Standard Processing Pipeline for High-accuracy Measurement of Few-shot Regression on Laser Induced Breakdown Spectroscopy

2026-06-20

AI Summary

This study addresses the challenges of spectral noise interference and data scarcity in laser-induced breakdown spectroscopy (LIBS) under few-shot scenarios by proposing a novel quantitative analysis pipeline. The approach integrates 3D U-Net-based diffusion denoising with an attention-mechanism autoencoder for spectral preprocessing, complemented by a group-shuffling data augmentation strategy and ordinary least squares regression. This framework substantially enhances regression accuracy and generalization capability in data-limited settings. Experimental results across multi-element concentration prediction demonstrate an average relative mean absolute error (RMAE) of 0.2847, representing improvements of 37.7% and 37.6% over baseline autoencoder and conventional PCA-PLS methods, respectively.

Abstract

Laser-induced breakdown spectroscopy (LIBS) faces challenges in high-accuracy quantitative measurement under few-shot scenarios due to spectral noise and data scarcity. Traditional preprocessing methods often fail to preserve subtle spectral features or capture nonlinear correlations. This work proposes a standardized processing pipeline integrating diffusion-based denoising, attention-based autoencoder for dimensionality reduction, group shuffling data augmentation, and ordinary least squares regression. The diffusion module employs a 3D UNet architecture to remove spectral noise while preserving essential emission features. The attention-autoencoder captures nonlinear spectral correlations, effectively reducing high-dimensional spectral data to compact latent representations. Group shuffling data augmentation enhances model robustness by creating synthetic samples through feature group permutation. Experimental results on multiple elemental concentrations demonstrate that our Diffusion-DA-AE pipeline achieves superior performance with a mean RMAE of 0.2847, representing 37.7\% and 37.6\% improvements over baseline autoencoder and traditional PCA-PLS regression, respectively. The framework's effectiveness validates its generalizability and establishes a new benchmark for few-shot LIBS regression.

Problem

• Laser-induced breakdown spectroscopy
• few-shot regression
• spectral noise
• data scarcity
• quantitative measurement

Innovation

• diffusion denoising
• attention-based autoencoder
• group shuffling augmentation
• few-shot regression
• laser-induced breakdown spectroscopy