提出标准化流程提升激光光谱少样本回归精度
A Standard Processing Pipeline for High-accuracy Measurement of Few-shot Regression on Laser Induced Breakdown Spectroscopy
- 用扩散模型去噪+注意力自编码器降维，保留关键光谱特征
- 少样本下平均相对绝对误差达0.2847，比基线提升37.7%
- 适合高精度光谱定量分析、数据稀缺场景的研究者
激光诱导击穿光谱（LIBS）在少样本条件下因光谱噪声和数据稀疏面临高精度定量测量挑战。传统预处理方法难以保持细微光谱特征或捕捉非线性相关性。本文提出标准化处理流程，融合基于扩散的去噪、注意力自编码器降维、分组打乱数据增强及普通最小二乘回归。扩散模块采用3D UNet架构，在去除光谱噪声的同时保留关键发射特征。注意力自编码器有效捕获非线性光谱相关性，将高维光谱数据压缩为紧凑的潜在表示。分组打乱数据增强通过特征组置换生成合成样本，提升模型鲁棒性。在多个元素浓度实验中，所提Diffusion-DA-AE流程实现0.2847的平均相对绝对误差（RMAE），相较基线自编码器和传统PCA-PLS回归分别提升37.7%与37.6%。该框架验证了通用性，建立了少样本LIBS回归的新基准。
原文摘要 · Abstract (English)
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.
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