Enhanced Sensitivity of Absorption Spectroscopy Glucose Detection by Machine Learning - Babińska, Maria; Władziński, Adam (Photonics Letters of Poland, 2025)
In this study, UV-VIS spectroscopy was used as a tool for detecting low glucose concentrations in urine. Measurements were performed on artificial urine samples and solutions with 0.1% and 0.2% glucose, covering both normal and pathological thresholds. Among the evaluated models, Random Forest reached 0.887 for the 0.1% glucose sample, while Logistic Regression achieved 0.7796 for the 0.2% glucose sample, demonstrating high effectiveness in distinguishing glucose levels. The results confirm that the integration of UV-VIS spectroscopy and machine learning has the potential to serve as a fast and non-invasive screening tool for the early detection of metabolic disorders. Full Text: PDF References S. L. Cowart and M. E. Stachura, Clinical Methods: The History, Physical, and Laboratory Examinations, 3rd edition, chapter 139, 1990. DirectLink P. Sokołowski, P. Wityk, K. Cierpiak, M. Babińska, W. Graczyk, B. Krawczyk, M. Markuszewski and M. Szczerska, "Optical method supported by machine learning for urinary tract infections discrimination and bladder cancer detection", Optical Sensing and Detection VIII, Proc. SPIE, 12999, 129992B (2024). CrossRef T.-T. Wang, K. Guo, X.-M .Hu, J. Liang, X.-D. Li, Z.-F. Zhang and J. Xie, "Label-Free Colorimetric Detection of Urine Glucose Based on Color Fading Using Smartphone Ambient-Light Sensor", Chemosensors, 8(1) (2020). CrossRef P. Sokołowski, K. Cierpiak, M. Szczerska, M. Wróbel, A. Łuczkiewicz, S. Fudala-Księżek and P. Wityk, "Optical method supported by machine learning for dynamics of C-reactive protein concentrations changes detection in biological matrix samples", J. Biophoton., e202300523 (2024). CrossRef M. Jędrzejewska-Szczerska, M. Gnyba, M. Sobaszek and E. Krystian, "Spectroscopic wireless sensor of hematocrit level", Sensors and Actuators A: Physical, 202, 8 (2013). CrossRef P. Wityk, P. Sokołowski, M. Szczerska, K. Cierpiak, B. Krawczyk and M. J. Markuszewski, " P. Wityk, P. Sokołowksi, M. Szczerska, K. Cierpiak, B. Krawczyk and M. J. Markuszewski, Journal of Biophotonics,16(9), e202300095 (2013).", J. Biophoton.,16(9), e202300095 (2013). CrossRef R.T.Yunardi, R. Apsari and M. Yasin, "Comparison of Machine Learning Algorithm For Urine Glucose Level Classification Using Side-Polished Fiber Sensor ", J.Electronic.Electromed.Eng.Med.Inform, 2(2), 33 (2020). CrossRef A. Althnian, D. AlSaeed, H. Al-Baity, A. Samha, A.B. Dris, N. Alzakari, A. Abou Elwafa and H. Kurdi, "Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain", Appl. Sci., 11(2), (2021). CrossRef S. Walford, M. McB Page and S. P. Allison, "The Influence of Renal Threshold on the Interpretation of Urine Tests for Glucose in Diabetic Patients", Diabetes Care, 3(9), 672 (1980). CrossRef Thermo Fisher Scientific, "NanoDrop One Microvolume UV-Vis Spectrophotometers Product Specifications". DirectLink A.M.C. Davies and T. Fearn,"Back to basics: the principles of principal component analysis", Spectroscopy Europe/World, (2004). DirectLink N. Sancar and S. S. Tabrizi, "Machine learning approach for the detection of vitamin D level: a comparative study", BMC Medical Informatics and Decision Making, 23(219) (2023). CrossRef T. Pranckevičius, V. Marcinkevičius, "Comparison of Naïve Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification", Baltic J. Modern Computing, 5(2), 221 (2017). CrossRef M. Marzejon, M. Kosowska, D. Majchrowicz, B. Buło-Piontecka, M. Wąsowicz and M. Jędrzejewska-Szczerska, "Label-free optical detection of cyclosporine in biological fluids", J. Biophoton., 12(4), e201800273 (2018). CrossRef S. Uddin, A. Khan, M. Hossain and M. Ali Moni, "Comparing different supervised machine learning algorithms for disease prediction", BMC Medical Informatics and Decision Making, 19, 281(2019). CrossRef