Large Language Models as Spectrographic Assistants: Opportunities and Challenges in Laboratory Data Analysis

Background: The integration of large language models (LLMs) into analytical chemistry has emerged as a pivotal innovation, offering transformative solutions to handle complex analytical workflows and exponentially growing datasets. This review critically examines the capabilities, applications, and limitations of LLMs in advancing spectral interpretation, predictive modeling, and experimental automation within the field. Methods: Through a systematic analysis of recent literature and case studies, we evaluate LLMs’ performance in processing multimodal data (e.g., spectral, textual, and numerical inputs), their adaptability to domain-specific tasks, and their synergy with traditional computational tools. Key applications include automated spectral analysis, environmental monitoring, and real-time decision support. Results: LLMs demonstrate remarkable versatility in tasks such as chemical state identification from spectra and predictive modeling of environmental parameters, achieving parity with conventional machine learning models in accuracy. However, their deployment is constrained by challenges in contextual reasoning, data security, and interpretability, necessitating hybrid human-AI frameworks for validation. Conclusions: This review underscores the importance of domain-specific optimization, robust validation protocols, and ethical governance to harness LLMs’ potential fully. Future efforts should prioritize interdisciplinary collaboration to refine LLM architectures, enhance interoperability, and address scalability in real-world scientific applications.