Spectral deconvolution is essential for extracting peak structures that encode material properties and chemical structures, but conventional automated methods often fail when spectra contain high-intensity noise or unknown background components. In practice, scientists rarely interpret spectra in isolation. Instead, they identify physically meaningful peaks by relating spectral structures to auxiliary information such as physical-property values, chemical structures, and trends across related measurements. Here, we propose a Bayesian framework that integrates spectral deconvolution with a model of expert scientific reasoning. In this work, expert scientific reasoning refers to the practice of evaluating candidate spectral structures by their consistency with independently measured physical-property values, rather than to manual expert intervention during inference. We formalize this reasoning as a physical-property regression layer, implemented using Gaussian process regression, and couple it with Bayesian spectral deconvolution. By averaging the physical-property likelihood over posterior predictive spectra inferred from Bayesian spectral deconvolution, the proposed method selects spectral models according to the consistency between inferred spectral structures and physical-property information. We validate the framework using synthetic spectra with high-intensity noise or unknown backgrounds and infrared spectra of poly(lactic acid). The method recovers physically meaningful peak structures that conventional Bayesian spectral deconvolution misses or misidentifies from spectra alone, including weak peaks in poly(lactic acid) IR spectra related to measured degradation rates. These results demonstrate that integrating expert scientific reasoning with Bayesian spectral deconvolution enables robust peak estimation under conditions where spectrum-only inference is unreliable.

翻译：光谱解卷积对于提取编码材料属性与化学结构的峰结构至关重要，但传统自动化方法在光谱包含高强度噪声或未知背景成分时往往失效。实际研究中，科学家很少孤立地解读光谱，而是通过将光谱结构与辅助信息（如物理性质值、化学结构及跨相关测量趋势）关联来识别具有物理意义的峰。本文提出一种将光谱解卷积与专家科学推理模型相结合的贝叶斯框架。此处专家科学推理指通过候选光谱结构与独立测量的物理性质值的一致性进行评价（而非推理过程中的人工专家干预）。我们将该推理形式化为物理性质回归层，采用高斯过程回归实现，并将其与贝叶斯光谱解卷积耦合。通过将物理性质似然度对贝叶斯光谱解卷积推断的后验预测光谱进行平均，该方法根据推断光谱结构与物理性质信息的一致性选择光谱模型。我们采用含高强度噪声或未知背景的合成光谱及聚乳酸红外光谱验证该框架。该方法能恢复传统贝叶斯光谱解卷积单独从光谱中遗漏或误判的具有物理意义的峰结构，包括聚乳酸红外光谱中与实测降解速率相关的弱峰。结果表明，在仅凭光谱推断不可靠的条件下，融合专家科学推理与贝叶斯光谱解卷积可实现稳健的峰值估计。