Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective.
food microbiology
machine learning
rapid methods
sensors
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
06 Apr 2022
06 Apr 2022
Historique:
received:
11
03
2022
revised:
31
03
2022
accepted:
02
04
2022
entrez:
12
4
2022
pubmed:
13
4
2022
medline:
14
4
2022
Statut:
epublish
Résumé
Unsafe food is estimated to cause 600 million cases of foodborne disease, annually. Thus, the development of methods that could assist in the prevention of foodborne diseases is of high interest. This review summarizes the recent progress toward rapid microbial assessment through (i) spectroscopic techniques, (ii) spectral imaging techniques, (iii) biosensors and (iv) sensors designed to mimic human senses. These methods often produce complex and high-dimensional data that cannot be analyzed with conventional statistical methods. Multivariate statistics and machine learning approaches seemed to be valuable for these methods so as to "translate" measurements to microbial estimations. However, a great proportion of the models reported in the literature misuse these approaches, which may lead to models with low predictive power under generic conditions. Overall, all the methods showed great potential for rapid microbial assessment. Biosensors are closer to wide-scale implementation followed by spectroscopic techniques and then by spectral imaging techniques and sensors designed to mimic human senses.
Identifiants
pubmed: 35408414
pii: s22072800
doi: 10.3390/s22072800
pmc: PMC9003504
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : HORIZON 2020
ID : 861915
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