Browsing by Author "Zorgani, Tarek"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- A lab-made e-nose-MOS device for assessing the bacterial growth in a solid culture mediumPublication . Dias, Teresa; Santos, Vitor S.; Zorgani, Tarek; Ferreiro, Nuno Manuel; Rodrigues, Ana Isabel; Zaghdoudi, Khalil; Veloso, Ana C.A.; Peres, António M.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.
- Microorganisms’ discrimination using an electronic nose-chemometric approachPublication . Zorgani, Tarek; Peres, António M.; Dias, Teresa; Zaghdoudi, KhalilThe detection/identification of microorganisms is of major relevance for food quality and safety. Traditional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reaction), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Sensor-based instruments have evolved as quicker and sensitive complementary identification tools for yeasts, bacteria and fungi. Electronic noses (E-noses), in combination with chemometrics, have been effectively employed for the detection/discrimination of different microorganisms, providing a green, quick, cost-effective, and non-destructive/non-invasive assessment. The successful use of the E-noses may be related to the generation of distinctive olfactory fingerprints of certain volatile organic compounds (VOCs) during the microorganism's growth. These devices have already been used to detect/discriminate fungi and bacteria (e.g.,Enterococcus faecalis, Escherichia coli, Klebsiella pneumonia, Listeria monocytogenes, Pseudomonas aeruginosa), namely in milk, juice, soups,goat and pork meat,fruits and vegetables. Thus, a lab-made E-nose, with nine metal oxide semiconductor sensors, was applied to detect, differentiate, andquantify four common food contamination/quality indicator bacteria, including two Gram positive (E. faecalisand S. aureus) and two Gram negative (E. coli and P. aeruginosa). Besides, to support the E-nose performance the volatile profiles generated by these bacteria were also assessed by headspace solid-phase micro extraction gas-chromatography-mass spectrometry. The volatile profiles comprised 15 identified VOCs, being 10 of them emitted by at least one of the four bacteria evaluated, namely two alcohols (1-butanol, and 1-nonanol), three pyrazines (2-ethyl-6-methyl-pyrazine, 3-ethyl-2,5-dimethylpyrazine,and trimethylpyrazine), three terpenes (camphene, D-limonene, and ƒÒ-pinene), and two other compounds (2,4-thujadiene and indole). The four bacteria could be distinguished using the electrical resistance signals produced by the E-nose in combination with linear discriminate analysis (90% of correct classifications for leave-one-out cross-validation). Additionally, multiple linear regression models, with root mean square errors lower than 4 colony forming units, were successfully established (0.9428 . R2.0.9946). Overall, the E-nose proved to be an effective qualitative-quantitative tool for analyzing bacteria in solid matrices, being foreseen it possible application to solid food matrices.