{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T19:34:23Z","timestamp":1775244863376,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Foundation for Science and Technology","doi-asserted-by":"publisher","award":["UIDB\/04436\/2020"],"award-info":[{"award-number":["UIDB\/04436\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Foundation for Science and Technology","doi-asserted-by":"publisher","award":["UIDB\/05549\/2020"],"award-info":[{"award-number":["UIDB\/05549\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Foundation for Science and Technology","doi-asserted-by":"publisher","award":["UIDP\/05549\/2020"],"award-info":[{"award-number":["UIDP\/05549\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Foundation for Science and Technology","doi-asserted-by":"publisher","award":["2020. 00215.CEECIND"],"award-info":[{"award-number":["2020. 00215.CEECIND"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"FCT","award":["UIDB\/04436\/2020"],"award-info":[{"award-number":["UIDB\/04436\/2020"]}]},{"name":"FCT","award":["UIDB\/05549\/2020"],"award-info":[{"award-number":["UIDB\/05549\/2020"]}]},{"name":"FCT","award":["UIDP\/05549\/2020"],"award-info":[{"award-number":["UIDP\/05549\/2020"]}]},{"name":"FCT","award":["2020. 00215.CEECIND"],"award-info":[{"award-number":["2020. 00215.CEECIND"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient\u2013provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019\u20132023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles\u2019 search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019\u20132023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.<\/jats:p>","DOI":"10.3390\/mi14061145","type":"journal-article","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T03:39:36Z","timestamp":1685331576000},"page":"1145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells"],"prefix":"10.3390","volume":"14","author":[{"given":"Ahmed","family":"Fadlelmoula","sequence":"first","affiliation":[{"name":"Center for Microelectromechanical Systems (CMEMS-UMinho), Campus de Azur\u00e9m, University of Minho, 4800-058 Guimar\u00e3es, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8962-0710","authenticated-orcid":false,"given":"Susana O.","family":"Catarino","sequence":"additional","affiliation":[{"name":"Center for Microelectromechanical Systems (CMEMS-UMinho), Campus de Azur\u00e9m, University of Minho, 4800-058 Guimar\u00e3es, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2460-0556","authenticated-orcid":false,"given":"Gra\u00e7a","family":"Minas","sequence":"additional","affiliation":[{"name":"Center for Microelectromechanical Systems (CMEMS-UMinho), Campus de Azur\u00e9m, University of Minho, 4800-058 Guimar\u00e3es, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4658-5844","authenticated-orcid":false,"given":"V\u00edtor","family":"Carvalho","sequence":"additional","affiliation":[{"name":"2Ai, School of Technology, IPCA, 4750-810 Barcelos, Portugal"},{"name":"Algoritmi Research Center\/LASI, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101761","DOI":"10.1016\/j.tice.2022.101761","article-title":"A comparative assessment of deep object detection models for blood smear analysis","volume":"76","author":"Talukdar","year":"2022","journal-title":"Tissue Cell"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pfeil, J., Nechyporenko, A., Frohme, M., Hufert, F.T., and Schulze, K. (2022). Examination of blood samples using deep learning and mobile microscopy. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-04602-4"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lee, S.J., Chen, P.Y., and Lin, J.W. (2022). Complete Blood Cell Detection and Counting Based on Deep Neural Networks. Appl. Sci., 12.","DOI":"10.3390\/app12168140"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1959371","DOI":"10.1155\/2022\/1959371","article-title":"Nursing Care Systematization with Case-Based Reasoning and Artificial Intelligence","volume":"2022","author":"Alazzam","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1499546","DOI":"10.1155\/2022\/1499546","article-title":"Detection of WBC, RBC, and Platelets in Blood Samples Using Deep Learning","volume":"2022","author":"Alhazmi","year":"2022","journal-title":"Biomed Res. Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1186\/s13075-022-02800-2","article-title":"Identification of biomarkers by machine learning classifiers to assist diagnose rheumatoid arthritis-associated interstitial lung disease","volume":"24","author":"Qin","year":"2022","journal-title":"Arthritis Res. Ther."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101923","DOI":"10.1016\/j.pdpdt.2020.101923","article-title":"Fourier transform infrared spectroscopy combined with deep learning and data enhancement for quick diagnosis of abnormal thyroid function","volume":"32","author":"Yue","year":"2020","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wong, L.-W., Mak, S.-H., Goh, B.-H., and Lee, W.-L. (2023). The Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery. Diagnostics, 13.","DOI":"10.3390\/diagnostics13010022"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.1039\/c3an36654e","article-title":"Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: A novel diagnostic approach for ovarian cancer","volume":"138","author":"Gajjar","year":"2013","journal-title":"Analyst"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.aca.2004.03.060","article-title":"Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning","volume":"514","author":"Ellis","year":"2004","journal-title":"Anal. Chim. Acta"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1089\/omi.2013.0157","article-title":"Mid-ATR-FTIR spectroscopic profiling of HIV\/AIDS sera for novel systems diagnostics in global health","volume":"18","author":"Sitole","year":"2014","journal-title":"OMICS"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"A256","DOI":"10.1149\/2.0861802jes","article-title":"A New Method for Determining the Concentration of Electrolyte Components in Lithium-Ion Cells, Using Fourier Transform Infrared Spectroscopy and Machine Learning","volume":"165","author":"Ellis","year":"2018","journal-title":"J. Electrochem. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1186\/s12936-019-2982-9","article-title":"Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis","volume":"18","author":"Mwanga","year":"2019","journal-title":"Malar. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1186\/s12936-019-2945-1","article-title":"Infrared spectroscopy coupled to cloud-based data management as a tool to diagnose malaria: A pilot study in a malaria-endemic country","volume":"18","author":"Heraud","year":"2019","journal-title":"Malar. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.3906\/elk-1801-259","article-title":"Classification of the likelihood of colon cancer with machine learning techniques using FTIR signals obtained from plasma","volume":"27","author":"Toraman","year":"2019","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e201900215","DOI":"10.1002\/jbio.201900215","article-title":"Rapid diagnosis of infection etiology in febrile pediatric oncology patients using infrared spectroscopy of leukocytes","volume":"13","author":"Agbaria","year":"2020","journal-title":"J. Biophotonics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"046501","DOI":"10.1117\/1.JBO.25.4.046501","article-title":"Potential of infrared microscopy to differentiate between dementia with Lewy bodies and Alzheimer\u2019s diseases using peripheral blood samples and machine learning algorithms","volume":"25","author":"Salman","year":"2020","journal-title":"J. Biomed Opt."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"118625","DOI":"10.1016\/j.saa.2020.118625","article-title":"Biochemical assay and spectroscopic analysis of oxidative\/antioxidative parameters in the blood and serum of substance use disorders patients. A methodological comparison study","volume":"240","author":"Guleken","year":"2020","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Korb, E., Ba\u011fc\u0131o\u011flu, M., Garner-Spitzer, E., Wiedermann, U., Ehling-Schulz, M., and Schabussova, I. (2020). Machine learning-empowered ftir spectroscopy serum analysis stratifies healthy, allergic, and sit-treated mice and humans. Biomolecules, 10.","DOI":"10.3390\/biom10071058"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6955","DOI":"10.1039\/D0AN00752H","article-title":"Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms","volume":"145","author":"Agbaria","year":"2020","journal-title":"Analyst"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5435","DOI":"10.1021\/acsnano.9b09119","article-title":"Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes","volume":"14","author":"Shin","year":"2020","journal-title":"ACS Nano"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113553","DOI":"10.1016\/j.jpba.2020.113553","article-title":"Investigation of the discrimination and characterization of blood serum structure in patients with opioid use disorder using IR spectroscopy and PCA-LDA analysis","volume":"190","author":"Guleken","year":"2020","journal-title":"J. Pharm. Biomed Anal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100004","DOI":"10.1016\/j.clispe.2020.100004","article-title":"Quantitative analysis of human blood serum using vibrational spectroscopy","volume":"2","author":"Byrne","year":"2020","journal-title":"Clin. Spectrosc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e202000025","DOI":"10.1002\/tbio.202000025","article-title":"Fourier-transform infrared spectroscopy of biofluids: A practical approach","volume":"3","author":"Theakstone","year":"2021","journal-title":"Transl. Biophotonics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102308","DOI":"10.1016\/j.pdpdt.2021.102308","article-title":"Human serum mid-infrared spectroscopy combined with machine learning algorithms for rapid detection of gliomas","volume":"35","author":"Chen","year":"2021","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"119259","DOI":"10.1016\/j.saa.2020.119259","article-title":"Identification of Aspergillus species in human blood plasma by infrared spectroscopy and machine learning","volume":"248","author":"Elkadi","year":"2021","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tomas, R.C., Sayat, A.J., Atienza, A.N., Danganan, J.L., Ramos, M.R., Fellizar, A., Notarte, K.I., Angeles, L.M., Bangaoil, R., and Santillan, A. (2022). Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0262489"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"12737","DOI":"10.1007\/s10489-022-03203-1","article-title":"Machine learning characterization of cancer patients-derived extracellular vesicles using vibrational spectroscopies: Results from a pilot study","volume":"52","author":"Uthamacumaran","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111258","DOI":"10.1016\/j.measurement.2022.111258","article-title":"Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level","volume":"196","author":"Guleken","year":"2022","journal-title":"Measurement"},{"key":"ref_30","first-page":"277","article-title":"Artificial intelligence to classify human lung carcinoma using blood plasma FTIR spectra","volume":"20","author":"Gasymov","year":"2022","journal-title":"Appl. Comput. Math."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121715","DOI":"10.1016\/j.saa.2022.121715","article-title":"Breast cancer early detection by using Fourier-transform infrared spectroscopy combined with different classification algorithms","volume":"283","author":"Du","year":"2022","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Praja, R.K., Wongwattanakul, M., Tippayawat, P., Phoksawat, W., Jumnainsong, A., Sornkayasit, K., and Leelayuwat, C. (2022). Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Discriminates the Elderly with a Low and High Percentage of Pathogenic CD4+ T Cells. Cells, 11.","DOI":"10.3390\/cells11030458"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"122916","DOI":"10.1016\/j.talanta.2021.122916","article-title":"Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications","volume":"237","author":"Guleken","year":"2022","journal-title":"Talanta"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"El Khoury, Y., Gebelin, M., de S\u00e8ze, J., Patte-Mensah, C., Marcou, G., Varnek, A., Mensah-Nyagan, A.-G., Hellwig, P., and Collongues, N. (2022). Rapid Discrimination of Neuromyelitis Optica Spectrum Disorder and Multiple Sclerosis Using Machine Learning on Infrared Spectra of Sera. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23052791"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Guo, S., Wei, G., Chen, W., Lei, C., Xu, C., Guan, Y., Ji, T., Wang, F., and Liu, H. (2022). Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers. Biomolecules, 12.","DOI":"10.3390\/biom12121815"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"di Santo, R., Vaccaro, M., Roman\u00f2, S., Di Giacinto, F., Papi, M., Rapaccini, G.L., De Spirito, M., Miele, L., Basile, U., and Ciasca, G. (2022). Machine Learning-Assisted FTIR Analysis of Circulating Extracellular Vesicles for Cancer Liquid Biopsy. J. Pers. Med., 12.","DOI":"10.3390\/jpm12060949"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8341","DOI":"10.1007\/s00216-022-04370-3","article-title":"Blood serum lipid profiling may improve the management of recurrent miscarriage: A combination of machine learning of mid-infrared spectra and biochemical assays","volume":"414","author":"Guleken","year":"2022","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103177","DOI":"10.1016\/j.pdpdt.2022.103177","article-title":"Rapid and sensitive detection of esophageal cancer by FTIR spectroscopy of serum and plasma","volume":"40","author":"Chen","year":"2022","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"120355","DOI":"10.1016\/j.saa.2021.120355","article-title":"Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing","volume":"265","author":"Chen","year":"2022","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"115021","DOI":"10.1016\/j.jpba.2022.115021","article-title":"Using infrared spectroscopy of serum and chemometrics for diagnosis of paracoccidioidomycosis","volume":"221","author":"Koehler","year":"2022","journal-title":"J. Pharm. Biomed Anal."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"166473","DOI":"10.1016\/j.bbadis.2022.166473","article-title":"Rapid diagnosis of malignant pleural mesothelioma and its discrimination from lung cancer and benign exudative effusions using blood serum","volume":"1868","author":"Yonar","year":"2022","journal-title":"Biochim. Biophys. Acta Mol. Basis Dis."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103027","DOI":"10.1016\/j.pdpdt.2022.103027","article-title":"Rapid discrimination of hepatic echinococcosis patients\u2019 serum using vibrational spectroscopy combined with support vector machines","volume":"40","author":"Zheng","year":"2022","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"117231","DOI":"10.1016\/j.cca.2023.117231","article-title":"Discrimination of dyslipidemia types with ATR-FTIR spectroscopy and chemometrics associated with multivariate analysis of the lipid profile, anthropometric, and pro-inflammatory biomarkers","volume":"540","author":"Machado","year":"2023","journal-title":"Clin. Chim. Acta"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"123858","DOI":"10.1016\/j.talanta.2022.123858","article-title":"Rapid and low-cost liquid biopsy with ATR-FTIR spectroscopy to discriminate the molecular subtypes of breast cancer","volume":"254","author":"Machado","year":"2023","journal-title":"Talanta"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"124457","DOI":"10.1016\/j.talanta.2023.124457","article-title":"Rapid Detection of Serological Biomarkers in Gallbladder Carcinoma Using Fourier Transform Infrared Spectroscopy Combined with Machine Learning","volume":"259","author":"Dou","year":"2023","journal-title":"Talanta"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"121839","DOI":"10.1016\/j.saa.2022.121839","article-title":"Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method","volume":"285","author":"Leng","year":"2023","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103529","DOI":"10.1016\/j.vibspec.2023.103529","article-title":"Label-free discrimination of T and B lymphocyte activation based on vibrational spectroscopy\u2014A machine learning approach","volume":"126","author":"Ramalhete","year":"2023","journal-title":"Vib. Spectrosc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"122135","DOI":"10.1016\/j.saa.2022.122135","article-title":"Detection of metabolic syndrome with ATR-FTIR spectroscopy and chemometrics in blood plasma","volume":"288","author":"Machado","year":"2023","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"103529","DOI":"10.1016\/j.pdpdt.2023.103529","article-title":"Use of ATR-FTIR spectroscopy to differentiate between cirrhotic\/non-cirrhotic HCV patients","volume":"42","author":"Ali","year":"2023","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_50","first-page":"785","article-title":"XGBoost: A scalable tree boosting system","volume":"Volume 13\u201317","author":"Chen","year":"2018","journal-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3526","DOI":"10.1039\/C8AN00599K","article-title":"Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: A review of contemporary practice strategies and knowledge gaps","volume":"143","author":"Lee","year":"2018","journal-title":"Analyst"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/s11306-019-1612-4","article-title":"A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification","volume":"15","author":"Mendez","year":"2019","journal-title":"Metabolomics"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/00401706.1999.10485951","article-title":"Statistical Learning Theory","volume":"41","author":"Wu","year":"2020","journal-title":"Technometrics"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Balan, V., Mihai, C.T., Cojocaru, F.D., Uritu, C.M., Dodi, G., Botezat, D., and Gardikiotis, I. (2019). Vibrational Spectroscopy Fingerprinting in Medicine: From Molecular to Clinical Practice. Materials, 12.","DOI":"10.3390\/ma12182884"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1080\/05704928.2016.1230863","article-title":"Advances in Fourier transform infrared (FTIR) spectroscopy of biological tissues","volume":"52","author":"Talari","year":"2017","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1080\/05704920701829043","article-title":"Fourier Transform Infrared (FTIR) Spectroscopy of Biological Tissues","volume":"43","author":"Movasaghi","year":"2008","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chaber, R., Kowal, A., Jakubczyk, P., Arthur, C., \u0141ach, K., Wojnarowska-Nowak, R., Kusz, K., Zawlik, I., Paszek, S., and Cebulski, J. (2021). A Preliminary Study of FTIR Spectroscopy as a Potential Non-Invasive Screening Tool for Pediatric Precursor B Lymphoblastic Leukemia. Molecules, 26.","DOI":"10.3390\/molecules26041174"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Fadlelmoula, A., Pinho, D., Carvalho, V.H., Catarino, S.O., and Minas, G. (2022). Fourier Transform Infrared (FTIR) Spectroscopy to Analyse Human Blood over the Last 20 Years: A Review towards Lab-on-a-Chip Devices. Micromachines, 13.","DOI":"10.3390\/mi13020187"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1039\/C3LC50878A","article-title":"SU-8 bonding protocol for the fabrication of microfluidic devices dedicated to FTIR microspectroscopy of live cells","volume":"14","author":"Mitri","year":"2014","journal-title":"Lab Chip"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Landari, H., Roudjane, M., Messaddeq, Y., and Miled, A. (2018). Pseudo-Continuous Flow FTIR System for Glucose, Fructose and Sucrose Identification in Mid-IR Range. Micromachines, 9.","DOI":"10.3390\/mi9100517"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1039\/C5LC01460C","article-title":"IR-Live: Fabrication of a low-cost plastic microfluidic device for infrared spectromicroscopy of living cells","volume":"16","author":"Birarda","year":"2016","journal-title":"Lab Chip"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1039\/c001889a","article-title":"Attenuated total reflection Fourier transform infrared spectroscopy for on-chip monitoring of solute concentrations","volume":"10","author":"Greener","year":"2010","journal-title":"Lab Chip"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Chai, J., Zhang, K., Xue, Y., Liu, W., Chen, T., Lu, Y., and Zhao, G. (2020). Review of MEMS Based Fourier Transform Spectrometers. Micromachines, 11.","DOI":"10.3390\/mi11020214"}],"container-title":["Micromachines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-666X\/14\/6\/1145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:44:11Z","timestamp":1760125451000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-666X\/14\/6\/1145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,29]]},"references-count":65,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["mi14061145"],"URL":"https:\/\/doi.org\/10.3390\/mi14061145","relation":{},"ISSN":["2072-666X"],"issn-type":[{"value":"2072-666X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,29]]}}}