Autor segons l'article: Zaim, O; Bouchikhi, B; Motia, S; Abelló, S; Llobet, E; El Bari, N
Departament: Enginyeria Electrònica, Elèctrica i Automàtica
Autor/s de la URV: ABELLÓ CROS, SÒNIA / Llobet Valero, Eduard
Paraules clau: Urine analysis Type 2 diabetes mellitus Type 1 diabetes mellitus Time Mass spectrometry Lung-cancer Ion mobility spectrometry Identification Gc-ms Electronic sensing system Electronic nose Disease Diagnosis Data fusion Breath analysis Biomarkers
Resum: Studies suggest that breath and urine analysis can be viable non-invasive methods for diabetes management, with the potential for disease diagnosis. In the present work, we employed two sensing strategies. The first strategy involved analyzing volatile organic compounds (VOCs) in biological matrices, such as exhaled breath and urine samples collected from patients with diabetes mellitus (DM) and healthy controls (HC). The second strategy focused on discriminating between two types of DM, related to type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), by using a data fusion method. For this purpose, an electronic nose (e-nose) based on five tin oxide (SnO2) gas sensors was employed to characterize the overall composition of the collected breath samples. Furthermore, a voltametric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. To evaluate the diagnostic performance of the electronic sensing systems, algorithm tools including principal component analysis (PCA), discriminant function analysis (DFA) and receiver operating characteristics (ROC) were utilized. The results showed that the e-nose and VE-tongue could discriminate between breath and urine samples from patients with DM and HC with a success rate of 99.44% and 99.16%, respectively. However, discrimination between T1DM and T2DM samples using these systems alone was not perfect. Therefore, a data fusion method was proposed as a goal to overcome this shortcoming. The fusing of data from the two instruments resulted in an enhanced success rate of classification (i.e., 93.75% for the recognition of T1DM and T2DM).
Àrees temàtiques: Química Physical and theoretical chemistry Materiais Instruments & instrumentation Farmacia Electrochemistry Ciencias sociales Ciência de alimentos Chemistry, analytical Astronomia / física Analytical chemistry
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: eduard.llobet@urv.cat
Identificador de l'autor: 0000-0001-6164-4342
Data d'alta del registre: 2024-01-14
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.mdpi.com/2227-9040/11/6/350
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Chemosensors (Basel). 11 (6):
Referència de l'ítem segons les normes APA: Zaim, O; Bouchikhi, B; Motia, S; Abelló, S; Llobet, E; El Bari, N (2023). Discrimination of Diabetes Mellitus Patients and Healthy Individuals Based on Volatile Organic Compounds (VOCs): Analysis of Exhaled Breath and Urine Samples by Using E-Nose and VE-Tongue. Chemosensors (Basel), 11(6), -. DOI: 10.3390/chemosensors11060350
DOI de l'article: 10.3390/chemosensors11060350
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2023
Tipus de publicació: Journal Publications
Repositori URV