发表机构
University College Dublin; Commonwealth Scientific and Industrial Research Organisation（都柏林大学学院; 联邦科学与工业研究组织）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对光谱类测量中的软传感器开发，提出一种自动化特征区域选择框架，利用信息指纹和留出验证选择少量连续通道，在葡萄糖伏安数据上仅用11-42个通道将误差降低88.3-94.6%。
AI 中文摘要
可持续生产日益依赖于过程分析化学和过程分析技术来支持监测、控制和自动化。在这些情境中使用的技术，包括拉曼光谱、红外光谱和电化学伏安法，会产生沿物理测量轴排列的高维信号，这里称为光谱类测量。这些信号可能包含冗余、信息量弱和噪声大的区域，从而使开发将信号映射到过程变量的数据驱动软传感器变得复杂。选择信息丰富的区域并非易事，因为视觉上突出的区域不一定最具预测性，而区域间的协同效应也不易推断。在此，我们引入了一个自动化特征区域选择框架，该框架在保留通道顺序的同时，识别出一组简约的连续区域。该框架将通道级目标相关性和信噪比指标结合成一个潜在信息指纹。从该指纹的峰值中推导出可变宽度的候选区间，并使用留出验证数据对其组合进行排序。最终选择倾向于在预测性能相当的候选中使用较少通道的模型。该框架通过使用金电极传感器在高达350 g/L的四个逐渐变宽的标称浓度范围内获取的葡萄糖循环伏安测量数据进行了演示。框架内使用高斯过程回归以适应可能的非线性关系、考虑观测噪声并量化认知不确定性。所选模型仅保留了原始400个通道中的11-42个，相对于全特征高斯过程回归基准，留出测试均方根误差降低了88.3-94.6%。
英文摘要
Sustainable production increasingly relies on process analytical chemistry and process analytical technology to support monitoring, control, and automation. Techniques used in these contexts, including Raman spectroscopy, infrared spectroscopy, and electrochemical voltammetry, generate high-dimensional signals ordered along physical measurement axes, referred to here as spectral-like measurements. These signals can contain redundant, weakly informative, and noisy regions, complicating the development of data-driven soft sensors to map them to process variables. Selecting informative regions is nontrivial, as visually prominent regions are not necessarily the most predictive, while synergistic effects among regions cannot be readily inferred. Here, we introduce an automated feature-region selection framework that identifies a parsimonious set of contiguous regions while preserving channel ordering. The framework combines channel-level target correlation and a signal-to-noise indicator into a latent information fingerprint. Variable-width candidate intervals are derived from peaks in this fingerprint, and their combinations are ranked using held-out validation data. Final selection favours models using fewer channels among candidates with comparable predictive performance. The framework is demonstrated using cyclic voltammetric measurements of glucose acquired with a gold electrode sensor across four progressively broader nominal concentration ranges up to 350 g/L. Gaussian process regression is used within the framework to accommodate possible nonlinear relationships, account for observation noise, and quantify epistemic uncertainty. The selected models retained only 11-42 of the original 400 channels and reduced held-out test root mean squared error by 88.3-94.6% relative to full-feature Gaussian process regression benchmarks.
