Tarek Zorgani
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Tarek Zorgani has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Ana C. A. Veloso
Instituto Politécnico de Coimbra, ISEC, DEQB, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal
1 shared publication - Ana I. Rodrigues
Centre for the Research and Technology of Agroenvironmental and Biological Sciences, CITAB, Inov4Agro, Universidadede Trás-os-Montes e Alto Douro, UTAD, Vila Real, Portugal
1 shared publication - António Manuel Peres
CIMO, LA SusTEC, Instituto Politécnico de Bragança, Bragança, Portugal
1 shared publication - Khalil Zaghdoudi
Département Génie Chimique, Université Libre de Tunis, Avenue Khéreddine—Pacha Tunis, 30, Tunis 1002, Tunisia
1 shared publication - Nuno Ferreiro
Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
1 shared publication - Teresa Dias
Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-252 Bragança, Portugal
1 shared publication
Latest Publications
- Open Access
- 24 December 2022
- Published by MDPI AG in Biosensors
- Vol. 13 (1) , 19
A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium
The detection and level assessment of microorganisms is a practical quality/contamination indicator of food and water samples. Conventional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reactions), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Electronic noses (E-noses), combined with chemometrics, provide a direct, green, and non-invasive assessment of the volatile fraction without the need for sample pre-treatments. The unique olfactory fingerprint generated during each microorganism’s growth can be a vehicle for its detection using gas sensors. A lab-made E-nose, comprising metal oxide semiconductor sensors was applied, to analyze solid medium containing Gram-positive (Enterococcus faecalis and Staphylococcus aureus) or Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria. The electrical-resistance signals generated by the E-nose coupled with linear discriminant analysis allowed the discrimination of the four bacteria (90% of correct classifications for leave-one-out cross-validation). Furthermore, multiple linear regression models were also established allowing quantifying the number of colony-forming units (CFU) (0.9428 ≤ R2 ≤ 0.9946), with maximum root mean square errors lower than 4 CFU. Overall, the E-nose showed to be a powerful qualitative–quantitative device for bacteria preliminary analysis, being envisaged its possible application in solid food matrices.
- 24 December 2022
- Published by MDPI AG in Biosensors
- Vol. 13 (1) , 19
Open AccessA Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium
The detection and level assessment of microorganisms is a practical quality/contamination indicator of food and water samples. Conventional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reactions), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Electronic noses (E-noses), combined with chemometrics, provide a direct, green, and non-invasive assessment of the volatile fraction without the need for sample pre-treatments. The unique olfactory fingerprint generated during each microorganism’s growth can be a vehicle for its detection using gas sensors. A lab-made E-nose, comprising metal oxide semiconductor sensors was applied, to analyze solid medium containing Gram-positive (Enterococcus faecalis and Staphylococcus aureus) or Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria. The electrical-resistance signals generated by the E-nose coupled with linear discriminant analysis allowed the discrimination of the four bacteria (90% of correct classifications for leave-one-out cross-validation). Furthermore, multiple linear regression models were also established allowing quantifying the number of colony-forming units (CFU) (0.9428 ≤ R2 ≤ 0.9946), with maximum root mean square errors lower than 4 CFU. Overall, the E-nose showed to be a powerful qualitative–quantitative device for bacteria preliminary analysis, being envisaged its possible application in solid food matrices.