{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T16:24:29Z","timestamp":1786465469910,"version":"build-2736575974"},"reference-count":54,"publisher":"American Chemical Society (ACS)","issue":"31","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2114188"],"award-info":[{"award-number":["2114188"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2114203"],"award-info":[{"award-number":["2114203"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2514387"],"award-info":[{"award-number":["2514387"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-AC36-08GO28308"],"award-info":[{"award-number":["DE-AC36-08GO28308"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["SUB-2023-10388"],"award-info":[{"award-number":["SUB-2023-10388"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35GM156609"],"award-info":[{"award-number":["R35GM156609"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-20-1-0366"],"award-info":[{"award-number":["FA9550-20-1-0366"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000928","name":"Welch Foundation","doi-asserted-by":"publisher","award":["A-1261"],"award-info":[{"award-number":["A-1261"]}],"id":[{"id":"10.13039\/100000928","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006206","name":"Biological and Environmental Research","doi-asserted-by":"publisher","award":["DE-SC-0023103"],"award-info":[{"award-number":["DE-SC-0023103"]}],"id":[{"id":"10.13039\/100006206","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007904","name":"Texas A and M University","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/100007904","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017311","name":"Texas A and M Foundation","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/100017311","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.<\/jats:p>","DOI":"10.1021\/acsomega.6c06456","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T13:20:08Z","timestamp":1785849608000},"page":"47122-47134","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Limits\nof Machine Learning-Assisted Raman\nSpectroscopy in Classification of Biological Samples"],"prefix":"10.1021","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9126-6754","authenticated-orcid":true,"given":"Aman","family":"Yadav","sequence":"first","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arlin","family":"Birkby","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noah","family":"Armstrong","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6985-9117","authenticated-orcid":true,"given":"Assame","family":"Arnob","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Hsun","family":"Chou","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]},{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alma","family":"Fernandez","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]},{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aart J.","family":"Verhoef","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]},{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4827-1013","authenticated-orcid":true,"given":"Zhenhuan","family":"Yi","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4343-3231","authenticated-orcid":true,"given":"Siddhant","family":"Gulati","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7144-2748","authenticated-orcid":true,"given":"Siddhi","family":"Kotnis","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Sun","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]},{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katy C.","family":"Kao","sequence":"additional","affiliation":[{"name":"San Jose State University , , , ,","place":["San Jose, California, United States, 95112-3613"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3082-7431","authenticated-orcid":true,"given":"Hung-Jen","family":"Wu","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"2026081110193701100_cit1","doi-asserted-by":"publisher","DOI":"10.1515\/psr-2017-0040","article-title":"Theoretical principles\nof Raman spectroscopy","volume-title":"Phys. Sci. Rev.","author":"Cialla-May","year":"2019"},{"issue":"2","key":"2026081110193701100_cit2","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.vibspec.2011.08.003","article-title":"Raman spectroscopy:\nRecent advancements, techniques\nand applications","volume":"57","author":"Das","year":"2011","journal-title":"Vib. Spectrosc."},{"issue":"3048","key":"2026081110193701100_cit3","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1038\/121501c0","article-title":"A New Type of Secondary\nRadiation","volume":"121","author":"Raman","year":"1928","journal-title":"Nature"},{"issue":"12","key":"2026081110193701100_cit4","doi-asserted-by":"publisher","first-page":"5426","DOI":"10.1038\/s41596-021-00620-3","article-title":"Chemometric\nanalysis in Raman spectroscopy\nfrom experimental design to machine learning\u2013based modeling","volume":"16","author":"Guo","year":"2021","journal-title":"Nat. Protoc."},{"issue":"14","key":"2026081110193701100_cit5","doi-asserted-by":"publisher","first-page":"2203104","DOI":"10.1002\/adom.202203104","article-title":"Recent\nProgresses in Machine Learning Assisted Raman Spectroscopy","volume":"11","author":"Qi","year":"2023","journal-title":"Adv. Opt. Mater."},{"issue":"6","key":"2026081110193701100_cit6","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1021\/acs.jpca.1c10417","article-title":"A Concise Review on Recent Developments\nof Machine Learning for the Prediction of Vibrational Spectra","volume":"126","author":"Han","year":"2022","journal-title":"J. Phys. Chem. A"},{"key":"2026081110193701100_cit7","doi-asserted-by":"publisher","first-page":"115796","DOI":"10.1016\/j.trac.2019.115796","article-title":"Deep learning\nand artificial intelligence methods for Raman and surface-enhanced\nRaman scattering","volume":"124","author":"Lussier","year":"2020","journal-title":"TrAC, Trends Anal. Chem."},{"key":"2026081110193701100_cit8","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.vibspec.2018.09.003","article-title":"Raman spectroscopy\nbased analysis of milk using random forest classification","volume":"99","author":"Amjad","year":"2018","journal-title":"Vib. Spectrosc."},{"issue":"9","key":"2026081110193701100_cit9","doi-asserted-by":"publisher","first-page":"1824","DOI":"10.1039\/D2AN00129B","article-title":"Identifying the\ncharge density and\ndielectric environment of graphene using Raman spectroscopy and deep\nlearning","volume":"147","author":"Chen","year":"2022","journal-title":"Analyst"},{"key":"2026081110193701100_cit10","doi-asserted-by":"publisher","first-page":"100020","DOI":"10.1016\/j.dche.2022.100020","article-title":"Integration of surface-enhanced\nRaman spectroscopy (SERS) and machine learning tools for coffee beverage\nclassification","volume":"3","author":"Hu","year":"2022","journal-title":"Digital Chem. Eng."},{"issue":"3","key":"2026081110193701100_cit11","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1021\/acsmeasuresciau.4c00014","article-title":"Direct Glycan Analysis\nof Biological Samples and Intact Glycoproteins\nby Integrating Machine Learning-Driven Surface-Enhanced Raman Scattering\nand Boronic Acid Arrays","volume":"4","author":"Hu","year":"2024","journal-title":"ACS Meas. Sci. Au"},{"issue":"19","key":"2026081110193701100_cit12","doi-asserted-by":"publisher","first-page":"11290","DOI":"10.1021\/acs.analchem.8b01831","article-title":"Label-Free Prostate Cancer Detection\nby Characterization of Extracellular Vesicles Using Raman Spectroscopy","volume":"90","author":"Lee","year":"2018","journal-title":"Anal. Chem."},{"issue":"1","key":"2026081110193701100_cit13","doi-asserted-by":"publisher","first-page":"14846","DOI":"10.1038\/s41598-017-13940-w","article-title":"Rapid identification\nof staphylococci by Raman spectroscopy","volume":"7","author":"Rebros\u030cova\u0301","year":"2017","journal-title":"Sci.\nRep."},{"issue":"3","key":"2026081110193701100_cit14","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1016\/j.talanta.2010.01.046","article-title":"Identification\nof pharmaceutical\ntablets by Raman spectroscopy and chemometrics","volume":"81","author":"Roggo","year":"2010","journal-title":"Talanta"},{"issue":"4","key":"2026081110193701100_cit15","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1021\/acssensors.9b00476","article-title":"Trace Detection\nof Tetrahydrocannabinol\nin Body Fluid via Surface-Enhanced Raman Scattering and Principal\nComponent Analysis","volume":"4","author":"Sivashanmugan","year":"2019","journal-title":"ACS Sens."},{"issue":"16\u201317","key":"2026081110193701100_cit16","doi-asserted-by":"publisher","first-page":"2193","DOI":"10.1002\/elps.201900004","article-title":"Elucidating\nfentanyls differentiation from morphines in chemical and biological\nsamples with surface-enhanced Raman spectroscopy","volume":"40","author":"Wang","year":"2019","journal-title":"Electrophoresis"},{"issue":"11","key":"2026081110193701100_cit17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002683","article-title":"Variable generalization\nperformance of a deep learning\nmodel to detect pneumonia in chest radiographs: A cross-sectional\nstudy","volume":"15","author":"Zech","year":"2018","journal-title":"PLoS Med."},{"key":"2026081110193701100_cit18","doi-asserted-by":"publisher","first-page":"5067","DOI":"10.3390\/s21155067","article-title":"Noise Sources and Requirements\nfor Confocal Raman Spectrometers\nin Biosensor Applications","volume":"21","author":"Jahn","year":"2021","journal-title":"Sensors"},{"key":"2026081110193701100_cit19","doi-asserted-by":"publisher","first-page":"117578","DOI":"10.1016\/j.trac.2024.117578","article-title":"Recent progress\nand applications of Raman spectrum denoising algorithms in chemical\nand biological analyses: A review","volume":"172","author":"Fang","year":"2024","journal-title":"TrAC, Trends\nAnal. Chem."},{"issue":"10","key":"2026081110193701100_cit20","doi-asserted-by":"publisher","first-page":"12102","DOI":"10.1364\/OE.22.012102","article-title":"Recovery of\nRaman spectra with low signal-to-noise ratio using Wiener estimation","volume":"22","author":"Chen","year":"2014","journal-title":"Opt. Express"},{"issue":"12","key":"2026081110193701100_cit21","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1007\/s10295-019-02241-y","article-title":"Beneficial mutations for carotenoid production identified\nfrom laboratory-evolved Saccharomyces cerevisiae","volume":"46","author":"Godara","year":"2019","journal-title":"J. Ind. Microbiol. Biotechnol."},{"issue":"1","key":"2026081110193701100_cit22","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1186\/s13007-024-01302-3","article-title":"In-vivo\nRaman microspectroscopy reveals differential nitrate concentration\nin different developmental zones in Arabidopsis roots","volume":"20","author":"Ferna\u0301ndez Gonza\u0301lez","year":"2024","journal-title":"Plant Methods"},{"issue":"12","key":"2026081110193701100_cit23","doi-asserted-by":"publisher","first-page":"2300052","DOI":"10.1002\/adsr.202300052","article-title":"Advancing Glycan Analysis: A New\nPlatform Integrating SERS, Boronic Acids, and Machine Learning Algorithms","volume":"2","author":"Hu","year":"2023","journal-title":"Adv. Sens. Res."},{"issue":"4","key":"2026081110193701100_cit24","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1021\/acs.jcim.9b01037","article-title":"Robust Classification of High-Dimensional\nSpectroscopy Data Using Deep Learning and Data Synthesis","volume":"60","author":"Houston","year":"2020","journal-title":"J. Chem. Inf. Model."},{"issue":"7","key":"2026081110193701100_cit25","doi-asserted-by":"publisher","first-page":"2311","DOI":"10.1021\/acs.jcim.3c00761","article-title":"A Comparative Analysis\nof Data Synthesis Techniques\nto Improve Classification Accuracy of Raman Spectroscopy Data","volume":"64","author":"Flanagan","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"2026081110193701100_cit26","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ultramic.2016.05.005","article-title":"Denoising\ntime-resolved microscopy\nimage sequences with singular value thresholding","volume":"178","author":"Furnival","year":"2017","journal-title":"Ultramicroscopy"},{"key":"2026081110193701100_cit27","doi-asserted-by":"publisher","first-page":"108777","DOI":"10.1016\/j.microc.2023.108777","article-title":"Modified denoising\nmethod of Raman spectra-based deep learning for Raman semi-quantitative\nanalysis and imaging","volume":"191","author":"Zeng","year":"2023","journal-title":"Microchem. J."},{"key":"2026081110193701100_cit28","doi-asserted-by":"publisher","first-page":"114887","DOI":"10.1016\/j.marpolbul.2023.114887","article-title":"A double sliding-window\nmethod for baseline correction\nand noise estimation for Raman spectra of microplastics","volume":"190","author":"Yang","year":"2023","journal-title":"Mar. Pollut. Bull."},{"issue":"22","key":"2026081110193701100_cit29","doi-asserted-by":"publisher","first-page":"23241","DOI":"10.1021\/acsomega.3c09247","article-title":"RamanFormer: A Transformer-Based\nQuantification Approach for Raman Mixture Components","volume":"9","author":"Koyun","year":"2024","journal-title":"ACS Omega"},{"issue":"30","key":"2026081110193701100_cit30","doi-asserted-by":"publisher","first-page":"3759","DOI":"10.1039\/C8AY01089G","article-title":"Algorithm\nfor optimal denoising of\nRaman spectra","volume":"10","author":"Barton","year":"2018","journal-title":"Anal. Methods"},{"issue":"3","key":"2026081110193701100_cit31","doi-asserted-by":"publisher","first-page":"2060","DOI":"10.1021\/acsomega.0c05041","article-title":"On Selecting a Suitable\nSpectral Matching Method for Automated Analytical Applications of\nRaman Spectroscopy","volume":"6","author":"Samuel","year":"2021","journal-title":"ACS Omega"},{"issue":"21","key":"2026081110193701100_cit32","doi-asserted-by":"publisher","first-page":"21251","DOI":"10.1021\/acsnano.3c05510","article-title":"Identifying\nSurface-Enhanced Raman\nSpectra with a Raman Library Using Machine Learning","volume":"17","author":"Ju","year":"2023","journal-title":"ACS Nano"},{"key":"2026081110193701100_cit33","unstructured":"Information, N. C. F. B.\n          \n          PubChem. Compound Summary for CID 379, Octanoic Acid. 2025; https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/Octanoic-Acid.accessed 5 March 2025."},{"key":"2026081110193701100_cit34","unstructured":"Information, N. C. F. B.\n          \n          PubChem. Compound Summary for CID 10850, Tricaprylin, 2025; https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/Tricaprylin. accessed 5 March 2025."},{"key":"2026081110193701100_cit35","volume-title":"The handbook of infrared\nand Raman characteristic frequencies of organic molecules","author":"Lin-Vien","year":"1991"},{"key":"2026081110193701100_cit36","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/S1296-2074(02)01214-1","article-title":"Study of Raman\nspectra of pigment\nmixtures","volume":"4","author":"Breitman","year":"2003","journal-title":"J. Cult. Heritage"},{"key":"2026081110193701100_cit37","doi-asserted-by":"publisher","first-page":"80448","DOI":"10.1109\/ACCESS.2022.3192514","article-title":"Why Is Multiclass\nClassification\nHard?","volume":"10","author":"Moral","year":"2022","journal-title":"IEEE Access"},{"key":"2026081110193701100_cit38","doi-asserted-by":"publisher","first-page":"138316","DOI":"10.1016\/j.snb.2025.138316","article-title":"Machine learning-enhanced\nRaman spectroscopy for fast nanoplastic\ndetection at low SNR","volume":"443","author":"Lim","year":"2025","journal-title":"Sens Actuators, B"},{"issue":"20","key":"2026081110193701100_cit39","doi-asserted-by":"publisher","first-page":"4232","DOI":"10.1039\/c1an15636e","article-title":"Standardization\nof Raman spectra for transfer of spectral libraries across different\ninstruments","volume":"136","author":"Rodriguez","year":"2011","journal-title":"Analyst"},{"issue":"8","key":"2026081110193701100_cit40","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1039\/b503624k","article-title":"Evaluation of an accurate calibration\nand spectral standardization procedure for Raman spectroscopy","volume":"130","author":"Hutsebaut","year":"2005","journal-title":"Analyst"},{"issue":"2","key":"2026081110193701100_cit41","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1366\/000370207779947585","article-title":"Relative Intensity Correction of\nRaman Spectrometers:\nNIST SRMs 2241 through 2243 for 785 nm, 532 nm, and 488 nm\/514.5 nm\nExcitation","volume":"61","author":"Choquette","year":"2007","journal-title":"Appl. Spectrosc."},{"issue":"12","key":"2026081110193701100_cit42","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1366\/0003702934066226","article-title":"Intensity\nCalibration and Sensitivity\nComparisons for CCD\/Raman Spectrometers","volume":"47","author":"Fryling","year":"1993","journal-title":"Appl.\nSpectrosc."},{"issue":"1","key":"2026081110193701100_cit43","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1038\/s42003-018-0093-8","article-title":"Raman spectral signature\nreflects transcriptomic features\nof antibiotic resistance in Escherichia coli","volume":"1","author":"Germond","year":"2018","journal-title":"Commun. Biol."},{"issue":"3\u20134","key":"2026081110193701100_cit44","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1002\/ansa.202200066","article-title":"Recent advances of\nRaman spectroscopy\nfor the analysis of bacteria","volume":"4","author":"Rodriguez","year":"2023","journal-title":"Anal. Sci. Adv."},{"key":"2026081110193701100_cit45","doi-asserted-by":"publisher","first-page":"936726","DOI":"10.3389\/fmicb.2022.936726","article-title":"Single-cell Raman spectroscopy\nidentifies Escherichia coli persisters\nand reveals their enhanced metabolic activities","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Microbiol."},{"key":"2026081110193701100_cit46","doi-asserted-by":"publisher","first-page":"120300","DOI":"10.1016\/j.saa.2021.120300","article-title":"Raman spectroscopy\nand machine learning for the classification of breast cancers","volume":"264","author":"Zhang","year":"2022","journal-title":"Spectrochim. Acta, A"},{"key":"2026081110193701100_cit47","doi-asserted-by":"publisher","first-page":"121654","DOI":"10.1016\/j.saa.2022.121654","article-title":"Raman spectroscopy and machine learning for the classification\nof esophageal squamous carcinoma","volume":"281","author":"Huang","year":"2022","journal-title":"Spectrochim.\nActa, A"},{"issue":"8","key":"2026081110193701100_cit48","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1038\/s41416-021-01659-5","article-title":"Raman spectroscopy:\ncurrent applications in breast\ncancer diagnosis, challenges and future prospects","volume":"126","author":"Hanna","year":"2022","journal-title":"Br. J. Cancer"},{"issue":"1","key":"2026081110193701100_cit49","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s12934-016-0509-4","article-title":"Lycopene overproduction in Saccharomyces\ncerevisiae through combining\npathway engineering with host engineering","volume":"15","author":"Chen","year":"2016","journal-title":"Microbial.\nCell Fact."},{"issue":"13","key":"2026081110193701100_cit50","doi-asserted-by":"publisher","first-page":"4342","DOI":"10.1128\/AEM.02759-06","article-title":"High-Level Production of Beta-Carotene\nin Saccharomyces cerevisiae by Successive Transformation with Carotenogenic\nGenes from Xanthophyllomyces dendrorhous","volume":"73","author":"Verwaal","year":"2007","journal-title":"Appl.\nEnviron. Microbiol."},{"issue":"46","key":"2026081110193701100_cit51","doi-asserted-by":"publisher","first-page":"25888","DOI":"10.1039\/C8RA04491K","article-title":"Challenges in application of Raman\nspectroscopy to biology and materials","volume":"8","author":"Kuhar","year":"2018","journal-title":"RSC Adv."},{"issue":"1","key":"2026081110193701100_cit52","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1038\/s41598-018-35927-x","article-title":"Intrinsic\nand extrinsic noise of gene expression in lineage trees","volume":"9","author":"Thomas","year":"2019","journal-title":"Sci. Rep."},{"issue":"11","key":"2026081110193701100_cit53","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.1039\/b507715j","article-title":"Micro-Raman spectroscopic identification\nof bacterial cells of the genus Staphylococcus and dependence on their\ncultivation conditions","volume":"130","author":"Harz","year":"2005","journal-title":"Analyst"},{"issue":"20","key":"2026081110193701100_cit54","doi-asserted-by":"publisher","first-page":"9610","DOI":"10.1021\/ac401806f","article-title":"Culture Independent Raman Spectroscopic\nIdentification of Urinary\nTract Infection Pathogens: A Proof of Principle Study","volume":"85","author":"Klo\u00df","year":"2013","journal-title":"Anal. Chem."}],"container-title":["ACS Omega"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acsomega.6c06456","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/31\/47122\/66334831\/acsomega.6c06456.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/31\/47122\/66334831\/acsomega.6c06456.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T15:01:04Z","timestamp":1786460464000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/acsodf\/article\/11\/31\/47122\/5237028\/Evaluating-Limits-of-Machine-Learning-Assisted"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,30]]},"references-count":54,"journal-issue":{"issue":"31","published-online":{"date-parts":[[2026,7,30]]},"published-print":{"date-parts":[[2026,8,11]]}},"URL":"https:\/\/doi.org\/10.1021\/acsomega.6c06456","relation":{},"ISSN":["2470-1343"],"issn-type":[{"value":"2470-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,30]]}}}