Contribution of data acquired from spectroscopic, genomic and microbiological analyses to enhance mussels’ quality assessment

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dc.contributor.authorLytou, Anastasia
dc.contributor.authorSaxton, Léa
dc.contributor.authorFengou, Lemonia-Christina
dc.contributor.authorAnagnostopoulos, Dimitrios A.
dc.contributor.authorParlapani, Foteini F.
dc.contributor.authorBoziaris, Ioannis S.
dc.contributor.authorMohareb, Fady
dc.contributor.authorNychas, George-John
dc.date.accessioned2025-02-26T11:27:53Z
dc.date.available2025-02-26T11:27:53Z
dc.date.freetoread2025-02-26
dc.date.issued2024-12-01
dc.date.pubOnline2024-10-24
dc.description.abstractIn this study, a large amount of heterogeneous data (i.e., microbiological, spectral and Next Generation Sequencing data) were obtained analyzing mussels of different species and origin, to acquire a comprehensive view about the quality and safety of these products. More specifically, spectral data were collected through Fourier transform Infrared (FTIR) spectroscopy, while the overall profile of microorganisms present in these samples, affecting quality and safety of mussels throughout storage, was determined through Next Generation Sequencing (NGS) using 16S rRNA metabarcoding analysis. In parallel, conventional microbiological analysis for the estimation of culturable spoilage microorganisms (total aerobes, Pseudomonas spp., B. thermosphacta, Shewanella spp. and Enterobacteriaceae) was applied. Different machine learning algorithms, namely Partial Least Square (PLS), Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) Neural Networks (NN)) were applied accordingly, to assess the potential of FTIR and NGS data to provide useful information about mussels’ microbiological quality. Microbial counts ranged from 3.5 to 9.0 log CFU/g, while NGS revealed several bacterial genera such as Pseudoalteromonas, Psychrobacter, Acinetobacter, Pseudomonas, B. thermosphacta, Psychrobacter, Kistimonas, Psychrilyobacter to affect the quality of mussels, depending on the mussel species, batch and storage conditions. According to the performance metrics, the SVM algorithm in tandem with FTIR achieved the highest prediction accuracy for microbial counts in M. chilensis samples (Rsquared; 0.89, RMSE; 0,74), while in the case of predicting the abundance of microbial genera using spectroscopic data, the best performing algorithm varied by bacterial genus. Indicatively, in M. chilensis, RF, kNN and NN performed better in predicting Enterococcus, Enhydrobacterium and Pseudoalteromonas, respectively (Rsquared = 0.92, 0.93, 0.99). Associations between genomics data and specific spectral regions were further investigated, revealing certain spectral regions that are associated with mussels’ quality and safety. The application of “multi-omics” in seafood supply chain can provide insightful information about mussels’ quality and safety compared to the methodologies followed in current quality and safety management systems.
dc.description.journalNameFood Research International
dc.description.sponsorshipEuropean Commission
dc.format.mediumPrint-Electronic
dc.identifier.citationLytou A, Saxton L, Fengou L-C, et al., (2024) Contribution of data acquired from spectroscopic, genomic and microbiological analyses to enhance mussels’ quality assessment. Food Research International, Volume 197, Issue Pt 1, December 2024, Article number 115207
dc.identifier.eissn1873-7145
dc.identifier.elementsID555640
dc.identifier.issn0963-9969
dc.identifier.paperNo115207
dc.identifier.urihttps://doi.org/10.1016/j.foodres.2024.115207
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23518
dc.identifier.volumeNo197, Pt 1
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0963996924012778?via%3Dihub
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectNext generation sequencing
dc.subjectFTIR
dc.subjectSpoilage
dc.subjectMulti-omics
dc.subjectSeafood
dc.subjectMachine learning
dc.subject30 Agricultural, Veterinary and Food Sciences
dc.subject32 Biomedical and Clinical Sciences
dc.subject40 Engineering
dc.subject4004 Chemical Engineering
dc.subject3210 Nutrition and Dietetics
dc.subject3006 Food Sciences
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectBiotechnology
dc.subjectFTIR
dc.subjectMachine learning
dc.subjectMulti-omics
dc.subjectNext generation sequencing
dc.subjectSeafood
dc.subjectSpoilage
dc.subjectFood Science
dc.subject3006 Food sciences
dc.subject3210 Nutrition and dietetics
dc.subject4004 Chemical engineering
dc.subject.meshAnimals
dc.subject.meshSpectroscopy, Fourier Transform Infrared
dc.subject.meshBivalvia
dc.subject.meshRNA, Ribosomal, 16S
dc.subject.meshHigh-Throughput Nucleotide Sequencing
dc.subject.meshBacteria
dc.subject.meshFood Microbiology
dc.subject.meshShellfish
dc.subject.meshMachine Learning
dc.subject.meshFood Quality
dc.subject.meshGenomics
dc.subject.meshSupport Vector Machine
dc.subject.meshAnimals
dc.subject.meshBacteria
dc.subject.meshRNA, Ribosomal, 16S
dc.subject.meshSpectroscopy, Fourier Transform Infrared
dc.subject.meshGenomics
dc.subject.meshFood Microbiology
dc.subject.meshShellfish
dc.subject.meshBivalvia
dc.subject.meshHigh-Throughput Nucleotide Sequencing
dc.subject.meshFood Quality
dc.subject.meshMachine Learning
dc.subject.meshSupport Vector Machine
dc.subject.meshAnimals
dc.subject.meshSpectroscopy, Fourier Transform Infrared
dc.subject.meshBivalvia
dc.subject.meshRNA, Ribosomal, 16S
dc.subject.meshHigh-Throughput Nucleotide Sequencing
dc.subject.meshBacteria
dc.subject.meshFood Microbiology
dc.subject.meshShellfish
dc.subject.meshMachine Learning
dc.subject.meshFood Quality
dc.subject.meshGenomics
dc.subject.meshSupport Vector Machine
dc.titleContribution of data acquired from spectroscopic, genomic and microbiological analyses to enhance mussels’ quality assessment
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-10-17

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