Articles producció científica> Enginyeria Electrònica, Elèctrica i Automàtica

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

  • Identification data

    Identifier: imarina:9325524
  • Authors:

    Zaim, O
    Bouchikhi, B
    Motia, S
    Abelló, S
    Llobet, E
    El Bari, N
    Chemosensors (Basel)
    10.3390/chemosensors11060350
    Chemosensors (Basel). 11 (6):
  • Others:

    Author, as appears in the article.: Zaim, O; Bouchikhi, B; Motia, S; Abelló, S; Llobet, E; El Bari, N
    Department: Enginyeria Electrònica, Elèctrica i Automàtica
    URV's Author/s: ABELLÓ CROS, SÒNIA / Llobet Valero, Eduard
    Keywords: 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
    Abstract: 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).
    Thematic Areas: Química Physical and theoretical chemistry Materiais Instruments & instrumentation Farmacia Electrochemistry Ciencias sociales Ciência de alimentos Chemistry, analytical Astronomia / física Analytical chemistry
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: eduard.llobet@urv.cat
    Author identifier: 0000-0001-6164-4342
    Record's date: 2024-07-20
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.mdpi.com/2227-9040/11/6/350
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Chemosensors (Basel). 11 (6):
    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
    Article's DOI: 10.3390/chemosensors11060350
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2023
    Publication Type: Journal Publications
  • Keywords:

    Analytical Chemistry,Chemistry, Analytical,Electrochemistry,Instruments & Instrumentation,Physical and Theoretical Chemistry
    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
    Química
    Physical and theoretical chemistry
    Materiais
    Instruments & instrumentation
    Farmacia
    Electrochemistry
    Ciencias sociales
    Ciência de alimentos
    Chemistry, analytical
    Astronomia / física
    Analytical chemistry
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