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Insights into the molecular mechanisms of lipid transformation in sturgeon fillets: Interplay between specific spoilage and dominant bacteria.

ABSTRACT: This study investigates spoilage bacteria's impact on lipid metabolism in sturgeon fillets using UHPLC-Q-Orbitrap-MS/MS-based untargeted lipidomic analysis. A total of 4041 lipid molecules across five classes and 42 subclasses were identified, including glycerophospholipids (GPs, 50.88%), glycerolipids (GLs, 36.08%), sphingolipids (SPs, 10.47%), fatty acyls (FAs, 2.45%), and sterol lipids (STs, 0.12%). Aeromonas sobria, a specific spoilage bacterium, reduced GPs and FAs while increasing GLs, SPs, and STs via extracellular lipases and esterases. Acinetobacter albensis, the dominant bacterium, mainly elevated SPs and FAs. Their interaction promoted lipid metabolism and oxidation while producing volatile organic compounds (VOCs). Ethyl isobutyrate, ethyl propionate, isobutyl formate, pentan-2-one, propan-2-one, 2-butanone, 3-methyl-3-buten-1-ol, and dimethyl sulfide were mainly associated with Acinetobacter albensis, while 1-hexanol, 1-pentanol, 1-penten-3-ol, 1-hydroxypropan-2-one, 3-methyl-1-butanol, 2-methylbutanal, 3-hydroxy-2-butanone, and propionaldehyde were mainly related to Aeromonas sobria. This work unveils the mechanism of lipid transformation in sturgeon fillets during refrigerated storage, offering insights for aquatic products quality control.

SUBMITTER: Tan C

PROVIDER: S-EPMC11369403 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature

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Insights into the molecular mechanisms of lipid transformation in sturgeon fillets: Interplay between specific spoilage and dominant bacteria.

Tan Chunming C Zhang Shiqi S Zou Fanglei F Gao Boya B Li Yujin Y Li Pinglan P Shang Nan N

Food chemistry: X 20240805

This study investigates spoilage bacteria's impact on lipid metabolism in sturgeon fillets using UHPLC-Q-Orbitrap-MS/MS-based untargeted lipidomic analysis. A total of 4041 lipid molecules across five classes and 42 subclasses were identified, including glycerophospholipids (GPs, 50.88%), glycerolipids (GLs, 36.08%), sphingolipids (SPs, 10.47%), fatty acyls (FAs, 2.45%), and sterol lipids (STs, 0.12%). <i>Aeromonas sobria</i>, a specific spoilage bacterium, reduced GPs and FAs while increasing G ...[more]

PMID: 39229613

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Project description:The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n = 215) were conducted at 0, 5, 10, and 15 °C for up to 480 h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm2 for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm2 for MSI data and 1.078 log CFU/cm2 for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm2) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm2. Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm2 in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast fillets.

Dataset Information

Insights into the molecular mechanisms of lipid transformation in sturgeon fillets: Interplay between specific spoilage and dominant bacteria.

Publications

Insights into the molecular mechanisms of lipid transformation in sturgeon fillets: Interplay between specific spoilage and dominant bacteria.

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