Machine learning (ML) models have achieved strikingly high accuracies in spectroscopic classification tasks, often without a clear proof that those models used chemically meaningful features. Existing studies have linked these results to data preprocessing choices, noise sensitivity, and model complexity, but no unifying explanation is available so far. In this work, we show that these phenomena arise naturally from the intrinsic high dimensionality of spectral data. Using a theoretical analysis grounded in the Feldman-Hajek theorem and the concentration of measure, we show that even infinitesimal distributional differences, caused by noise, normalisation, or instrumental artefacts, may become perfectly separable in high-dimensional spaces. Through a series of specific experiments on synthetic and real fluorescence spectra, we illustrate how models can achieve near-perfect accuracy even when chemical distinctions are absent, and why feature-importance maps may highlight spectrally irrelevant regions. We provide a rigorous theoretical framework, confirm the effect experimentally, and conclude with practical recommendations for building and interpreting ML models in spectroscopy.

翻译：机器学习（ML）模型在光谱分类任务中已取得显著的高准确率，但通常缺乏明确的证据表明这些模型使用了具有化学意义的特征。现有研究将这些结果与数据预处理选择、噪声敏感性及模型复杂度相关联，但至今尚无统一的解释。在本工作中，我们揭示这些现象自然源于光谱数据固有的高维性。基于费尔德曼-哈杰克定理和测度集中理论的理论分析，我们证明：由噪声、归一化或仪器伪影引起的微小分布差异，在高维空间中可能变得完全可分。通过一系列针对合成荧光光谱和真实荧光光谱的特定实验，我们展示了即便在缺乏化学区分性时模型仍能实现近乎完美准确率的原因，以及为何特征重要性图谱可能突出与光谱无关的区域。我们提供了严格的理论框架，通过实验验证了这一效应，并最终提出了构建和解释光谱学中ML模型的实践建议。