{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:35:26Z","timestamp":1793115326445,"version":"4.5.0"},"reference-count":31,"publisher":"American Chemical Society (ACS)","license":[{"start":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T00:00:00Z","timestamp":1793059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study presents a comparative evaluation of five state-of-the-art large language models (LLMs) (Claude Opus 4.6, Gemini 3.1 Pro, Grok-4, ChatGPT (GPT-5.4 Thinking), and Qwen 3.5) for their ability to perform key chemometric tasks through natural language interaction. Using a low-field nuclear magnetic resonance (LF-NMR) dataset of edible vegetable oils, the models were prompted to execute a complete multivariate analysis workflow, including data exploration, unsupervised and supervised classification, model validation, and interpretation. Results reveal a clear performance hierarchy: Claude Opus 4.6 was the only model to autonomously execute the entire pipeline with the highest analytical rigor, correctly predicting all nine unlabeled samples. Gemini 3.1 Pro and ChatGPT (GPT-5.4 Thinking) also demonstrated strong capabilities, achieving 100% or near-100% classification accuracy within fallback or native workflows, positioning them as effective analytical co-pilots. Grok-4 produced near-perfect classification results but showed limitations in data handling autonomy and exploratory depth. Qwen 3.5, the only open-source model, suffered a critical, unrecoverable failure at the supervised classification stage after repeated attempts, preventing workflow completion. Overall, the results indicate that linguistic fluency should not be taken as a reliable indicator of analytical competence and that general-purpose benchmarks may have limitations when assessing domain-specific chemometric performance. Critical validation and human oversight remain essential for the trustworthy deployment of these tools in spectroscopy-based workflows.<\/jats:p>","DOI":"10.1021\/acs.analchem.6c02701","type":"journal-article","created":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:02:39Z","timestamp":1793113359000},"source":"Crossref","is-referenced-by-count":0,"title":["LLMs in the Laboratory: Benchmarking Conversational AI for Multivariate Analysis of NMR Spectroscopic Data"],"prefix":"10.1021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4683-5903","authenticated-orcid":true,"given":"Jose\u0301 Rau\u0301l","family":"Belmonte-Sa\u0301nchez","sequence":"first","affiliation":[{"name":"University of Almeri\u0301a Research Centre for Agricultural Food Biotechnology (CIAIMBITAL) , , ,","place":["Almeri\u0301a, Spain, E-04120"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2505-2056","authenticated-orcid":true,"given":"Roberto","family":"Romero-Gonza\u0301lez","sequence":"additional","affiliation":[{"name":"University of Almeri\u0301a Research Centre for Agricultural Food Biotechnology (CIAIMBITAL) , , ,","place":["Almeri\u0301a, Spain, E-04120"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7904-7842","authenticated-orcid":true,"given":"Antonia","family":"Garrido Frenich","sequence":"additional","affiliation":[{"name":"University of Almeri\u0301a Research Centre for Agricultural Food Biotechnology (CIAIMBITAL) , , ,","place":["Almeri\u0301a, Spain, E-04120"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,10,27]]},"reference":[{"key":"2026102711023186800_cit1","unstructured":"OpenAI Limited Partnership\n          . ChatGPT. https:\/\/chatgpt.com\/ (accessed Mar 30, 2026)."},{"key":"2026102711023186800_cit2","unstructured":"Anthropic, Public Benefit Corporation\n          . Claude. https:\/\/claude.ai\/ (accessed Mar 30, 2026)."},{"key":"2026102711023186800_cit3","unstructured":"Google LLC (DeepMind Technologies Limited)\n          . Gemini. https:\/\/gemini.google.com\/ (accessed Mar 30, 2026)."},{"key":"2026102711023186800_cit4","unstructured":"Artificial Intelligence Corporation\n          . Grok. https:\/\/grok.com\/ (accessed Mar 30, 2026)."},{"key":"2026102711023186800_cit5","unstructured":"Alibaba Group Holding Ltd\n          . Qwen. https:\/\/chat.qwen.ai\/ (accessed Mar 30, 2026)."},{"issue":"2","key":"2026102711023186800_cit6","doi-asserted-by":"publisher","first-page":"592","DOI":"10.31127\/tuje.1838765","article-title":"Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma","volume":"10","author":"Ayd\u0131n","year":"2026","journal-title":"Turk. J. Eng."},{"issue":"7","key":"2026102711023186800_cit7","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1038\/s41570-023-00502-0","article-title":"The Future of Chemistry Is Language","volume":"7","author":"White","year":"2023","journal-title":"Nat. Rev. Chem."},{"issue":"5","key":"2026102711023186800_cit8","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1039\/d3dd00113j","article-title":"14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon","volume":"2","author":"Jablonka","year":"2023","journal-title":"Digit. Discov."},{"issue":"2","key":"2026102711023186800_cit9","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1039\/D1DD00009H","article-title":"Natural Language Processing Models That Automate Programming Will Transform Chemistry Research and Teaching","volume":"1","author":"Hocky","year":"2022","journal-title":"Digit. Discov."},{"issue":"5","key":"2026102711023186800_cit10","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/s42256-024-00832-8","article-title":"Augmenting Large Language Models with Chemistry Tools","volume":"6","author":"Bran","year":"2024","journal-title":"Nat. Mach. Intell."},{"issue":"2","key":"2026102711023186800_cit11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000198","article-title":"Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education Using Large Language Models","volume":"2","author":"Kung","year":"2023","journal-title":"PLOS Digit. Health"},{"issue":"7983","key":"2026102711023186800_cit12","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1038\/d41586-023-03235-8","article-title":"How ChatGPT Is Transforming the Postdoc Experience","volume":"622","author":"Nordling","year":"2023","journal-title":"Nature"},{"key":"2026102711023186800_cit13","doi-asserted-by":"publisher","first-page":"121186","DOI":"10.1016\/j.eswa.2023.121186","article-title":"Can ChatGPT Provide Intelligent Diagnoses? A Comparative Study between Predictive Models and ChatGPT to Define a New Medical Diagnostic Bot","volume":"235","author":"Caruccio","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"2026102711023186800_cit14","doi-asserted-by":"publisher","first-page":"73","DOI":"10.12793\/tcp.2024.32.e8","article-title":"Data Science through Natural Language with ChatGPT\u2019s Code Interpreter","volume":"32","author":"Ahn","year":"2024","journal-title":"Transl. Clin. Pharmacol."},{"issue":"13","key":"2026102711023186800_cit15","doi-asserted-by":"publisher","first-page":"6956","DOI":"10.1021\/acs.analchem.4c05046","article-title":"Large Language Models (Such as ChatGPT) as Tools for Machine Learning-Based Data Insights in Analytical Chemistry","volume":"97","author":"Duponchel","year":"2025","journal-title":"Anal. Chem."},{"key":"2026102711023186800_cit16","doi-asserted-by":"publisher","first-page":"110787","DOI":"10.1016\/j.microc.2024.110787","article-title":"Applicability of ChatGPT 3.5 in the Development of New Analytical Procedures","volume":"203","author":"Kalyniukova","year":"2024","journal-title":"Microchem. J."},{"key":"2026102711023186800_cit17","doi-asserted-by":"publisher","first-page":"chemrxiv-2024-qtnkj","DOI":"10.26434\/chemrxiv-2024-qtnkj","article-title":"LMM Spectrometric Determination of an Organic Compound","author":"Kawchak","year":"2024","journal-title":"ChemRxiv"},{"key":"2026102711023186800_cit18","doi-asserted-by":"publisher","first-page":"arXiv.2606.29776","DOI":"10.48550\/arXiv.2606.29776","article-title":"Towards Generalizable and Evidential Nuclear Magnetic Resonance-Based Molecular Structure Elucidation via Large Language Model Agent","author":"Fang","year":"2026","journal-title":"arXiv"},{"issue":"3","key":"2026102711023186800_cit19","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1039\/d5dd00359h","article-title":"Enhancing Molecular Structure Elucidation with Reasoning-Capable LLMs","volume":"5","author":"Priessner","year":"2026","journal-title":"Digit. Discov."},{"issue":"5","key":"2026102711023186800_cit20","doi-asserted-by":"publisher","first-page":"343","DOI":"10.3390\/nu4050343","article-title":"1H Nuclear Magnetic Resonance Study of Olive Oils Commercially Available as Italian Products in the United States of America","volume":"4","author":"del Coco","year":"2012","journal-title":"Nutrients"},{"key":"2026102711023186800_cit21","doi-asserted-by":"publisher","first-page":"2818","DOI":"10.1016\/j.csbj.2020.09.033","article-title":"Deep Metabolome: Applications of Deep Learning in Metabolomics","volume":"18","author":"Pomyen","year":"2020","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"2026102711023186800_cit22","doi-asserted-by":"publisher","first-page":"125949","DOI":"10.1016\/j.talanta.2024.125949","article-title":"AI in Analytical Chemistry: Advancements, Challenges, and Future Directions","volume":"274","author":"Cardoso Rial","year":"2024","journal-title":"Talanta"},{"key":"2026102711023186800_cit23","doi-asserted-by":"publisher","first-page":"1417900","DOI":"10.3389\/feduc.2024.1417900","article-title":"ChatGPT as a Data Analyst: An Exploratory Study on AI-Supported Quantitative Data Analysis in Empirical Research","volume":"9","author":"Prandner","year":"2025","journal-title":"Front. Educ."},{"key":"2026102711023186800_cit24","doi-asserted-by":"publisher","first-page":"9496","DOI":"10.18653\/v1\/2023.findings-emnlp.637","article-title":"Is GPT-4 a Good Data Analyst?","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2023","author":"Cheng","year":"2023"},{"key":"2026102711023186800_cit25","doi-asserted-by":"crossref","unstructured":"Dinh, T.; Zeng, Y.; Zhang, R.; Lin, Z.; Gira, M.; Rajput, S.; Sohn, J.; Papailiopoulos, D.; Lee, K.\n          LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks\n          Advances in Neural Information Processing Systems; NeurIPS, 2022; Vol. 35, pp 11763\u221211784. DOI: 10.52202\/068431-0855","DOI":"10.52202\/068431-0855"},{"issue":"2","key":"2026102711023186800_cit26","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1039\/d2dd00087c","article-title":"Assessment of Chemistry Knowledge in Large Language Models That Generate Code","volume":"2","author":"White","year":"2023","journal-title":"Digit. Discov."},{"key":"2026102711023186800_cit27","doi-asserted-by":"crossref","unstructured":"Guo, T.; Guo, K.; Nan, B.; Liang, Z.; Guo, Z.; Chawla, N. V.; Wiest, O.; Zhang, X.\n          What Can Large Language Models Do in Chemistry? A Comprehensive Benchmark on Eight Tasks\n          Advances in Neural Information Processing Systems; NeurIPS, 2023; Vol. 36, pp 59662\u221259688. DOI: 10.52202\/075280-2607","DOI":"10.52202\/075280-2607"},{"issue":"1","key":"2026102711023186800_cit28","doi-asserted-by":"publisher","first-page":"2260300","DOI":"10.1080\/27660400.2023.2260300","article-title":"Prompt Engineering of GPT-4 for Chemical Research: What Can\/Cannot Be Done?","volume":"3","author":"Hatakeyama-Sato","year":"2023","journal-title":"Sci. Technol. Adv. Mater. Methods"},{"key":"2026102711023186800_cit29","doi-asserted-by":"publisher","first-page":"111610","DOI":"10.1016\/j.foodcont.2025.111610","article-title":"Expanding Horizons: Toward the Classification of Extra Virgin Olive Oil Using 1H NMR and Machine Learning","volume":"179","author":"Belmonte-Sa\u0301nchez","year":"2026","journal-title":"Food Control"},{"key":"2026102711023186800_cit30","unstructured":"Chiang, W. L.; Zheng, L.; Sheng, Y.; Angelopoulos, A. N.; Li, T.; Li, D.; Zhu, B.; Zhang, H.; Jordan, M. I.; Gonzalez, J. E.; Stoica, I.\n          Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference\n          Proceedings of the 41st International Conference on Machine Learning; Proceedings of Machine Learning Research; PMLR, 2024; Vol. 235, pp 8359\u22128388."},{"key":"2026102711023186800_cit31","doi-asserted-by":"publisher","first-page":"117000","DOI":"10.1016\/j.lwt.2024.117000","article-title":"Benchtop NMR Spectroscopy for the Quantification of Fat Content in Milk: From Calibration Curve to Deep Learning","volume":"212","author":"Belmonte-Sa\u0301nchez","year":"2024","journal-title":"LWT"}],"container-title":["Analytical Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.analchem.6c02701","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/ancham\/article-pdf\/doi\/10.1021\/acs.analchem.6c02701\/68998905\/ac6c02701.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/ancham\/article-pdf\/doi\/10.1021\/acs.analchem.6c02701\/68998905\/ac6c02701.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:10:51Z","timestamp":1793113851000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/ancham\/article\/doi\/10.1021\/acs.analchem.6c02701\/5459281\/LLMs-in-the-Laboratory-Benchmarking-Conversational"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,27]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1021\/acs.analchem.6c02701","relation":{},"ISSN":["0003-2700","1520-6882"],"issn-type":[{"value":"0003-2700","type":"print"},{"value":"1520-6882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,27]]}}}