Abstract
Infrared spectroscopy offers rapid, non destructive measurement of chemical and material properties but suffers from high dimensional, overlapping spectral bands that challenge conventional chemometric approaches. Emerging large language models (LLMs), with their capacity for generalization and reasoning, offer promising potential for automating complex scientific workflows. Despite this promise, their application in IR spectral analysis remains largely unexplored. This study addresses the critical challenge of achieving accurate, automated infrared spectral interpretation under low-data conditions using an LLM-driven framework. We introduce an end-to-end, large language model driven agent framework that integrates a structured literature knowledge base, automated spectral preprocessing, feature extraction, and multi task reasoning in a unified pipeline. By querying a curated corpus of peer reviewed IR publications, the agent selects scientifically validated routines. The selected methods transform each spectrum into low dimensional feature sets, which are fed into few shot prompt templates for classification, regression, and anomaly detection. A closed loop, multi turn protocol iteratively appends mispredicted samples to the prompt, enabling dynamic refinement of predictions. Across diverse materials: stamp pad ink, Chinese medicine, Pu'er tea, Citri Reticulatae Pericarpium and waste water COD datasets, the multi turn LLM consistently outperforms single turn inference, rivaling or exceeding machine learning and deep learning models under low data regimes.
Abstract (translated)
红外光谱技术可以快速、无损地测量化学和材料属性，但其高维、重叠的光谱带给传统化学计量方法带来了挑战。新兴的大语言模型（LLM）凭借其泛化能力和推理能力，在自动化复杂科学工作流程方面展现出巨大潜力。尽管有这些前景，但在红外光谱分析中的应用仍处于初步阶段。 本研究旨在利用一个由大语言模型驱动的框架解决在低数据条件下实现准确、自动化的红外光谱解析的关键挑战。我们提出了一种端到端的大语言模型驱动代理框架，该框架整合了结构化的文献知识库、自动化光谱预处理、特征提取和多任务推理，并将其统一在一个管道中。 通过查询一个经过审核的同行评议的红外出版物集合，该代理选择科学验证的方法。所选方法将每个光谱转换为低维特征集，然后这些特征集被输入到少量样本提示模板中进行分类、回归及异常检测。一种封闭循环、多轮协议迭代地将预测错误的样本添加到提示中，从而能够动态优化预测。 在不同的材料上进行了测试：印泥墨水、中药、普洱茶、橘皮和废水COD数据集，在低数据条件下，经过多轮对话的大语言模型始终优于单次推理，并且其性能可以与机器学习及深度学习模型相匹敌或超越。
URL
https://arxiv.org/abs/2507.21471
