[论文解读] Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data

Amalya C. Johnson, Chris Fajardo+3 more|arXiv (Cornell University)|Mar 6, 2026

Spectroscopy and Chemometric Analyses被引用 0

一句话总结

Spectra-Scope 是一个开源 AutoML 框架，具有 Python 与无代码网页应用，能够对光谱数据进行特征工程、模型训练以及特征降维选择，使用可解释模型如随机森林和 LCEN 进行解释。它支持多模态光谱数据，并强调可解释性以揭示物理洞见。

ABSTRACT

Spectroscopy is a central pillar of materials characterization, providing useful information on properties like structure, composition, or excited state dynamics of a system. However, many spectroscopic techniques present challenges in development of interpretable, performant, and reliable supervised learning models due to the wide range of possible nonlinear correlations that can exist between the signal and the response variable (target) of interest. Here, we present Spectra-Scope, an open-source AutoML framework for automatic characterization of material properties from spectroscopy data using interpretable machine learning (ML) models. The software is implemented in Python and a no-code web application. It comprises tools for data preprocessing, nonlinear feature extraction, machine learning model training, and feature downselection. Users can easily train different types of simple, interpretable ML models on a set of feature transformations quickly and with modest computational resources. In this work, we outline the methods of Spectra-Scope and its effectiveness across diverse datasets, with applications to materials and agricultural spectroscopy data. We show that Spectra-Scope can reproduce performance of comparable models in the literature, and highlight how our emphasis on interpretability can be used to rationalize the behavior of individual models and understand the physical processes behind spectral features.

研究动机与目标

• 提供一个强调可解释性的光谱数据开源 AutoML 框架。
• 将光谱特征器打包，将光谱转化为有助于建模的信息性特征。
• 实现可解释模型（随机森林和 LCEN）并进行特征降维选择。
• 支持多模态数据融合并提供面向用户的无代码网页应用以便于使用。

提出的方法

• 实现一套光谱特征化工具箱，包含局部、非局部和集合变换（如 CDF、高斯峰拟合、PCA）。
• 对输入进行非线性特征扩展以增强表示能力。
• 采用可解释模型，如随机森林和 LCEN（LASSO-Clip-Elastic-Net）并进行特征降维选择。
• 将融合 LASSO 作为一种模型以促进光谱的区域级可解释性。
• 提供无代码网页应用（Streamlit），用于数据上传、特征化可视化、模型训练和特征重要性查看。

Figure 1: Outline of this paper and the Spectra-Scope pipeline. (a) Input data can come from any experimental or simulated 1-D array data source for inference on a scalar response variable. (b) Available featurizations of spectral data include the cumulative distribution function, gaussian peak fitt

实验结果

研究问题

• RQ1 Spectra-Scope 能否在可解释管线中再现现有模型在光谱数据上的性能？
• RQ2 不同特征化策略与模型在预测材料性质（来自光谱数据）方面有何差异？
• RQ3 多模态光谱数据在提升性质预测与解释方面能达到何种程度？
• RQ4 可解释模型在不同数据集下识别出光谱的哪些区域为重要？
• RQ5 AutoML 在确保物理合理性与泛化性方面在光谱任务中的局限性是什么？

主要发现

• 随机森林在bond-length回归任务（来自 XANES+PDF 数据）上通常优于 LCEN。
• 顶级特征包括光谱的前 N 个成分、多项式变换和整谱强度，具体取决于模型。
• LCEN 和融合 LASSO 突出显示与目标相关的可解释光谱区域，有助于物理解释。
• 在葡萄萄萄酒 Vis-NIR 与拉曼数据上，模型在预测总可溶性固形物（TSS）方面的百分比 RMSE 与以往研究相当或更好。
• 选定的光谱区域（如在 738 nm、970 nm、以及 1100–1200 nm 附近）与葡萄中的已知振动模态一致，支持物理合理性。
• 融合 LASSO 可直观识别重要的连续光谱区域，强化基于区域的可解释性。

Figure 2: Front page of Spectra-Scope application. Multiple data types can be input and visualized on the home page. The app includes abilities to featurize data, visualize featurizations, train models using random forests or LCEN, and visualize the important or downselected features by the model.