用Transformer提升细菌拉曼光谱分类准确率，验证了其在重复实验中的稳定性。
Transformer-Based Classification of Bacterial Raman Spectra with LOOCV
- 基于Transformer的模型直接处理原始光谱数据，无需预处理
- 在六种细菌、九次独立重复中分类准确率全面领先
- 适合需要高鲁棒性的生物医学光谱分析场景
近年来，基于Transformer的模型在拉曼光谱分类中受到关注。本研究采用嵌套的留一复制交叉验证框架，系统评估了基于Transformer的方法，并与传统机器学习流程（PCA/ICA结合LDA、SVM、随机森林）进行对比。使用包含5,417个单细胞拉曼光谱、来自六种细菌和九次独立测量复制的数据集。Transformer在所有独立测试复制中均表现最优，显著优于所有传统方法。对学习到的隐空间分析显示，其类别分离效果优于基于PCA和ICA的表示。此外，该模型在直接应用于原始拉曼光谱时仍保持优异性能，表现出跨复制测量的强鲁棒性。这些结果突显了Transformer模型在拉曼光谱分类中的潜力，并强调了考虑复制信息的验证对于真实模型评估的重要性。
原文摘要 · Abstract (English)
Transformer-based models have recently attracted increasing attention for Raman spectral classification. In this study, a transformer-based approach was systematically evaluated using a nested leave-one-replicate-out cross-validation framework and compared with conventional machine-learning pipelines combining PCA or ICA with LDA, SVM, and Random Forest classifiers. A bacterial Raman dataset comprising 5,417 single-cell spectra from six bacterial species and nine independent measurement replicates was used. The transformer consistently achieved the highest classification performance across independent test replicates and significantly outperformed all conventional approaches. Analysis of the learned latent feature space revealed improved class separation compared with PCA- and ICA-based representations. Furthermore, the transformer maintained superior performance when applied directly to raw Raman spectra without preprocessing, demonstrating robust behavior across measurement replicates. These findings highlight the potential of transformer-based models for robust Raman spectral classification and emphasize the importance of replicate-aware validation for realistic model evaluation.
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