AI 中文总结
提出受控扰动框架，通过五种扰动类型和十三种度量，在三个数据集上评估拉曼光谱质量度量与下游任务性能的关系，揭示任务特定优缺点并提供可复现评估程序。
AI 中文摘要
拉曼光谱预处理和增强通常通过将输出光谱与参考光谱进行比较来评估。解读这些比较需要证据表明光谱质量度量能反映下游任务性能。我们提出了一个受控扰动框架来检验这种关系。五种扰动类型（基线畸变、独立噪声、相关噪声、全局波数偏移和非线性轴扭曲）在光谱度量（度量损害）和下游性能（任务损害）中产生成对变化。对齐差距（AG）量化了度量损害与任务损害之间的关系随扰动类型变化的程度。排序一致性（OC）衡量度量在两种条件下按任务损害正确排序的频率。该框架评估了十三种输出（MSE、RMSE、MAE、NMSE、光谱角、Pearson相关系数、Wasserstein距离、结构噪声比、峰精度、召回率、F1、伪影比和缺失比）。三个公开数据集提供了细菌分类、糖混合物定量和矿物识别任务。PCA结合逻辑回归、偏最小二乘回归和余弦库匹配提供任务结果。分类器和校准模型要么拟合到未扰动的训练光谱，要么拟合到每个扰动的训练条件，然后在相同的扰动测试光谱上进行评估。矿物查询与未改变的或相应扰动的库进行比较。由此产生的比较识别出任务特定的优势和局限性，包括排序更好但AG并不更小的情况。去除轴扰动并在公共物理网格上比较光谱，测试了这些发现如何依赖于评估设计。该框架为评估现有度量并针对下游任务性能测试新候选者提供了可复现的程序。
英文摘要
Raman preprocessing and enhancement methods are often assessed by comparing their output with a clean reference spectrum using measures such as mean squared error (MSE). Whether these spectral quality measures reflect downstream analytical performance is rarely tested. We propose a controlled-perturbation framework for this test. Five perturbation types (baseline distortion, independent noise, correlated noise, global wavenumber shift, and nonlinear axis warping) at eight strengths produce paired changes in a quality measure (metric harm) and in downstream performance (task harm). The alignment gap (AG) quantifies how much a single monotone metric-to-task relationship, fitted by isotonic regression, improves when each perturbation type receives its own relationship. Ordering concordance (OC), a clustered Kendall-type index, measures how often a measure ranks conditions of different perturbation types in the same order as their task harm. Cluster-bootstrap intervals and Holm-adjusted sign-flip tests compare twelve candidate measures with MSE. Three public datasets are used: bacterial classification by principal component analysis and logistic regression, sugar-mixture quantification by partial least squares regression, and mineral identification by cosine library matching. Models are fitted to unperturbed or to correspondingly perturbed training spectra. For bacterial classifiers fitted to unperturbed spectra, Wasserstein distance improved both statistics relative to MSE (Holm-adjusted p < 0.05), also after axis perturbations were removed or spectra were compared on a common grid; refitting to perturbed spectra reversed this advantage. Peak-based measures and a structure-to-noise ratio improved both statistics only for refitted sugar calibrations, and no candidate did so for mineral identification. Open code supports testing new measures.
Comments18 pages, 6 figures, 3 tables; Supporting Information (13 pages) provided as an ancillary file. v2: revised title and abstract, restructured theory and methods sections, new Figure 1, and additional chemometrics references; numerical results unchanged. Code and aggregate data: https://github.com/PuppyQ08/Raman_quality_measure
