{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T14:05:43Z","timestamp":1774274743817,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFF1104100"],"award-info":[{"award-number":["2023YFF1104100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32202380"],"award-info":[{"award-number":["32202380"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of Guangdong Academy of Sciences","award":["2022GDASZH-2022010101"],"award-info":[{"award-number":["2022GDASZH-2022010101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Microorganisms"],"abstract":"<jats:p>Hepatitis B virus (HBV) remains a major global public health challenge, and its early screening is essential for controlling transmission and improving treatment outcomes. We analyzed serum samples from 422 participants via Matrix-assisted laser desorption\/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to establish a screening model for hepatitis B surface antigen (HBsAg)-positive status. Following multi-bin preprocessing and single-sample spectral aggregation, we assessed three machine learning algorithms\u2014random forest, deep neural network, and light gradient boosting machine (LightGBM). Among them, the LightGBM model achieved the best performance, with an optimized F1 score of 0.87 and an area under the receiver operating characteristic curve (AUC) of 0.94. A 100-iteration ensemble feature stabilization strategy identified twelve distinct m\/z peaks as stable biomarkers for HBsAg-positive screening. Independent validation yielded sensitivity of 77.7% and specificity of 76.0%\u2014insufficient for individual diagnosis but potentially suitable for population-level surveillance programs combined with confirmatory testing, particularly in resource-limited settings where conventional methods are impractical. Notably, the method offers a detection time of approximately one minute, a per-sample cost of ~$0.14. In conclusion, the combination of MALDI-TOF MS and machine learning enables a rapid, low-cost screening tool for large-scale HBV detection.<\/jats:p>","DOI":"10.3390\/microorganisms14030702","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T08:49:28Z","timestamp":1774255768000},"page":"702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Enhanced MALDI-TOF Mass Spectrometry for Screening HBsAg-Positive Patients"],"prefix":"10.3390","volume":"14","author":[{"given":"Tiantian","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"},{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5850-3425","authenticated-orcid":false,"given":"Shixuan","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0636-9506","authenticated-orcid":false,"given":"Junxun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuwei","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingshuang","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lulu","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3943-7755","authenticated-orcid":false,"given":"Xinqiang","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxia","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4012-6954","authenticated-orcid":false,"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6503-359X","authenticated-orcid":false,"given":"Qingping","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"},{"name":"State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Food and Drug Laboratory, Guangdong Detection Center of Microbiology, Guangzhou 510070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s00535-023-02070-y","article-title":"A Novel, Small Anti-HBV Compound Reduces HBsAg and HBV-DNA by Destabilizing HBV-RNA","volume":"59","author":"Watanabe","year":"2024","journal-title":"J. Gastroenterol."},{"key":"ref_2","unstructured":"World Health Organization (2017). Global Hepatitis Report 2017."},{"key":"ref_3","unstructured":"WHO (2024, May 04). Hepatitis B, Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/hepatitis-b."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2629088","DOI":"10.1080\/21505594.2026.2629088","article-title":"Machine learning prediction of functional cure to pegylated interferon-alpha therapy in two HBV populations: Advantaged populations and HBeAg-negative patients","volume":"17","author":"Li","year":"2026","journal-title":"Virulence"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wu, D., Zhang, K., Ren, R., Liu, Y., Zhang, S., Zhang, X., Cheng, J., Chen, L., and Huang, J. (2024). Detection Technology and Clinical Applications of Serum Viral Products of Hepatitis B Virus Infection. Front. Cell. Infect. Microbiol., 14.","DOI":"10.3389\/fcimb.2024.1402001"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"121722","DOI":"10.1016\/j.talanta.2020.121722","article-title":"Recent Improvements in Enzyme-Linked Immunosorbent Assays Based on Nanomaterials","volume":"223","author":"Zhao","year":"2021","journal-title":"Talanta"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1042\/BIO20200034","article-title":"A Beginner\u2019s Guide to RT-PCR, qPCR and RT-qPCR","volume":"42","author":"Adams","year":"2020","journal-title":"Biochemist"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1093\/cid\/ciad738","article-title":"Integrated Genomic and Social Network Analyses of SARS-CoV-2 Transmission in the Healthcare Setting","volume":"78","author":"Keehner","year":"2024","journal-title":"Clin. Infect. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"131711","DOI":"10.1016\/j.snb.2022.131711","article-title":"Metal Nanoclusters Combined with CRISPR-Cas12a for Hepatitis B Virus DNA Detection","volume":"361","author":"Tao","year":"2022","journal-title":"Sens. Actuators B Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29169","DOI":"10.1038\/s41598-025-14451-9","article-title":"Serum peptide biomarkers by MALDI-TOF MS coupled with machine learning for diagnosis and classification of hepato-pancreato-biliary cancers","volume":"15","author":"Prajumwongs","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1038\/s41591-021-01619-9","article-title":"Direct Antimicrobial Resistance Prediction from Clinical MALDI-TOF Mass Spectra Using Machine Learning","volume":"28","author":"Weis","year":"2022","journal-title":"Nat. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5893","DOI":"10.1021\/acsami.4c17563","article-title":"Porous Silicon Particle-Assisted Mass Spectrometry Technology Unlocks Serum Metabolic Fingerprints in the Progression from Chronic Hepatitis B to Hepatocellular Carcinoma","volume":"17","author":"Jiang","year":"2025","journal-title":"ACS Appl. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.3892\/ol.2020.11727","article-title":"Serum Profile of Low Molecular Weight Fucosylated Glycoproteins for Early Diagnosis of Hepatocellular Carcinoma","volume":"20","author":"Yao","year":"2020","journal-title":"Oncol. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"16982","DOI":"10.1038\/s41598-021-96581-4","article-title":"4210 Da and 1866 Da Polypeptides as Potential Biomarkers of Liver Disease Progression in Hepatitis B Virus Patients","volume":"11","author":"Ren","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1111\/j.1365-2893.2010.01267.x","article-title":"Serum Peptide Profiles during Progression of Chronic Hepatitis B Virus Infection to Liver Failure","volume":"17","author":"Han","year":"2010","journal-title":"J. Viral Hepat."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1309\/AJCPA6C6NOGFLYIR","article-title":"MALDI-TOF MS Combined with Magnetic Beads for Detecting Serum Protein Biomarkers and Establishment of Boosting Decision Tree Model for Diagnosis of Hepatocellular Carcinoma","volume":"134","author":"Liu","year":"2010","journal-title":"Am. J. Clin. Pathol."},{"key":"ref_17","first-page":"916","article-title":"Machine Learning Algorithms\u2014A Review","volume":"9","author":"Mahesh","year":"2018","journal-title":"Int. J. Sci. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_19","unstructured":"Lundberg, S., and Lee, S.I. (2017, May 22). A Unified Approach to Interpreting Model Predictions. Available online: http:\/\/arxiv.org\/abs\/1705.07874."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"536","DOI":"10.21037\/atm-22-2119","article-title":"The Relationship between Serum Exosome HBV-miR-3 and Current Virological Markers and Its Dynamics in Chronic Hepatitis B Patients on Antiviral Treatment","volume":"10","author":"Gan","year":"2022","journal-title":"Ann. Transl. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.jcv.2019.08.001","article-title":"Improved Detection of Early Acute, Late Acute, and Occult Hepatitis B Infections by an Increased Sensitivity HBsAg Assay","volume":"118","author":"Kuhns","year":"2019","journal-title":"J. Clin. Virol."},{"key":"ref_22","first-page":"176","article-title":"Comparative Assessment of Immunochromatography and ELISA Diagnostic Tests for HBsAg Detection in PCR-Confirmed HBV Infection","volume":"87","author":"Navvabi","year":"2022","journal-title":"Rev. Gastroenterol. M\u00e9xico Engl. Ed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.jfda.2019.01.001","article-title":"Current Status of MALDI-TOF Mass Spectrometry in Clinical Microbiology","volume":"27","author":"Hou","year":"2019","journal-title":"J. Food Drug Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"121331","DOI":"10.1016\/j.talanta.2020.121331","article-title":"Evaluation of Prostate Cancer Based on MALDI-TOF MS Fingerprinting of Nanoparticle-Treated Serum Proteins\/Peptides","volume":"220","author":"Sun","year":"2020","journal-title":"Talanta"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4782","DOI":"10.1021\/acs.analchem.0c04590","article-title":"Rapid Detection of COVID-19 Using MALDI-TOF-Based Serum Peptidome Profiling","volume":"93","author":"Yan","year":"2021","journal-title":"Anal. Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s40537-019-0219-y","article-title":"Effectiveness Analysis of Machine Learning Classification Models for Predicting Personalized Context-Aware Smartphone Usage","volume":"6","author":"Sarker","year":"2019","journal-title":"J. Big Data"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1186\/s13059-021-02492-y","article-title":"LightGBM: Accelerated Genomically Designed Crop Breeding through Ensemble Learning","volume":"22","author":"Yan","year":"2021","journal-title":"Genome Biol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2023). Neural Networks and Deep Learning: A Textbook, Springer International Publishing.","DOI":"10.1007\/978-3-031-29642-0"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ymeth.2015.03.001","article-title":"Quantification of Serum Apolipoproteins A-I and B-100 in Clinical Samples Using an Automated SISCAPA-MALDI-TOF-MS Workflow","volume":"81","author":"Nouta","year":"2015","journal-title":"Methods"}],"container-title":["Microorganisms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2607\/14\/3\/702\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T13:21:46Z","timestamp":1774272106000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2607\/14\/3\/702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,20]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["microorganisms14030702"],"URL":"https:\/\/doi.org\/10.3390\/microorganisms14030702","relation":{},"ISSN":["2076-2607"],"issn-type":[{"value":"2076-2607","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,20]]}}}