[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117845-en":3,"doc-seo-117845-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117845,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Exploring Machine Learning in Chemistry through the Classification of Spectra: An Undergraduate Project","Applications of machine learning in chemistry span structure–property prediction and modeling of potential energy surfaces, but classroom-ready approaches remain limited. This work presents a generalized method for classifying spectra with supervised and unsupervised ML, demonstrated using FTIR and mass spectra. The study applies PCA for dimensionality reduction and compares multiple algorithms across three undergraduate projects involving fruits, whiskies, and teas. Trained models accurately distinguish samples, then support classification of unknowns with high performance, adaptable to short workshops or extended independent projects.","[pubs.acs.org/jchemeduc](pubs.acs.org/jchemeduc)  Activity   \nExploring Machine Learning in Chemistry through the Classification of Spectra: An Undergraduate Project  \nAlanah Grant St James, Luke Hand, Thomas Mills, Liwen Song, Annabel S. J. Brunt,  \nPatrick E. Bergstrom Mann, Andrew F. Worrall, Malcolm I. Stewart, * and Claire Vallance  \n Cite This: J. Chem. Educ. 2023, 100, 1343−1350  \nRead Online  \nDownloaded via 90.199.149.19 1 on June 7, 2023 at 05:32:27 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nACCESS  \n Metrics & More  \n Article Recommendations  \n*sı   \nSupporting Information  \nABSTRACT: Applications of machine learning in chemistry are many and varied, from prediction of structure−property relationships, to modeling of potential energy surfaces for large scale atomistic simulations. We describe a generalized approach for the application of machine learning to the classification of spectra which can be used as the basis for a wide variety of undergraduate projects. While our examples use FTIR and mass spectra, the approach could equally well be used with UV−visible, Raman, NMR, or indeed any other type of spectra. We summarize a number of different unsupervised and supervised machine learning algorithms that can be used to classify spectra into groups, and illustrate their application using data from three different projects carried out by fourth year chemistry undergraduates. The three projects investigated the ability of the various machine learning approaches to correctly classify spectra of a variety of fruits, whiskies, and teas, respectively. In all cases the algorithms were able to differentiate between the various samples used in each study, and the trained machine learning models could then be used to classify unknown samples with a high degree of accuracy (>98% in many cases). Depending on the extent to which students are expected to write their own code to perform the data analysis, the general model adopted in this work can be adapted for a variety of purposes, from short (one to two day) practical exercises and workshops, to much longer independent student projects.  \nKEYWORDS: Upper-Division Undergraduate, Laboratory Instruction, Chemoinformatics, Interdisciplinary/Multidisciplinary, Computer-Based Learning, Chemometrics, Mass Spectrometry, Spectroscopy, Computational Chemistry  \n■ INTRODUCTION  \nEvery chemist is familiar with the challenge of identifying an unknown compound or mixture of compounds. With a wide range ofspectroscopic and mass spectrometric techniques at our fingertips, we are now able to perform sophisticated measurements on virtually any type of sample. In some cases, the sample and its spectra are simple enough to make identification straightforward. However, in many cases the spectra are sufficiently complex that our best approach is comparison with spectra from a reference library. 1−3 Spectral matching with library spectra is now almost exclusively performed by computer algorithms, making it easier than ever to identify individual  \nchemical compounds and to characterize complex mixtures.  \nMachine learning (ML), a branch of Artificial Intelligence (AI), offers additional tools for the classification and identification of spectra. Machine learning algorithms use data to train a model, which can then be used to make predictions when presented with previously unseen data.4 ML algorithms have already found a host of applications in chemistry, includingthe prediction of structure−property relationships,5−7 the modeling of potential energy surfaces for large scale atomistic simulations,8 the prediction of the electron densities of  \n© 2023 The Authors. Published by American Chemical Society and Division  \n of Chemical Education, Inc. 1343  \nmolecules,9 the prediction of molecular structures from NMR spectra, 10 and new synthetic routes to complex chemicals.11 However, despite it","cbCaipyiKLd2ADI5","https://ap.wps.com/l/cbCaipyiKLd2ADI5","pdf",3019369,1,"English","en",105,"# Abstract\n## Applications of machine learning in chemistry\n## Generalized approach for spectra classification\n## Algorithms and dimensionality reduction (PCA)\n## Undergraduate project applications and results","[{\"question\":\"What problem does this undergraduate project address in chemistry?\",\"answer\":\"It targets the challenge of identifying unknown compounds or mixtures by classifying complex spectra using machine learning rather than relying solely on manual or purely library-based matching.\"},{\"question\":\"Which spectra types and algorithms are demonstrated?\",\"answer\":\"The examples use FTIR and mass spectra, and the paper summarizes multiple unsupervised and supervised algorithms that classify spectra into groups.\"},{\"question\":\"How is model performance evaluated and what accuracy is reported?\",\"answer\":\"The trained models are used to classify unknown samples after learning from labeled project data, achieving high accuracy, reported as greater than 98% in many cases.\"}]","Exploring Machine Learning in Chemistry through the Classification of Spectra: An Undergraduate Project | 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problem does this undergraduate project address in chemistry?","Question",{"text":74,"@type":75},"It targets the challenge of identifying unknown compounds or mixtures by classifying complex spectra using machine learning rather than relying solely on manual or purely library-based matching.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which spectra types and algorithms are demonstrated?",{"text":79,"@type":75},"The examples use FTIR and mass spectra, and the paper summarizes multiple unsupervised and supervised algorithms that classify spectra into groups.",{"name":81,"@type":72,"acceptedAnswer":82},"How is model performance evaluated and what accuracy is reported?",{"text":83,"@type":75},"The trained models are used to classify unknown samples after learning from labeled project data, achieving high accuracy, reported as greater than 98% in many 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