A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium

Author:

Dias TeresaORCID,Santos Vítor S.,Zorgani Tarek,Ferreiro Nuno,Rodrigues Ana I.,Zaghdoudi Khalil,Veloso Ana C. A.ORCID,Peres António M.ORCID

Abstract

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.

Funder

Foundation for Science and Technology

Publisher

MDPI AG

Subject

Clinical Biochemistry,General Medicine,Analytical Chemistry,Biotechnology,Instrumentation,Biomedical Engineering,Engineering (miscellaneous)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine learning-assisted electronic nose and gas sensors;Machine Learning and Artificial Intelligence in Chemical and Biological Sensing;2024

2. A review on machine learning–powered fluorescent and colorimetric sensor arrays for bacteria identification;Microchimica Acta;2023-10-25

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