Characterization of the microbiota in lightly salted bighead carp ( Aristichthys nobilis ) fillets stored at 4 °C

Liu, Xiaochang; Zhang, Yuemei; Li, Dapeng; Luo, Yongkang

doi:10.1016/j.fm.2016.10.007

Cited by 54 publications

(22 citation statements)

References 23 publications

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“…Among them, Flavobacterium and Lachnoclostridium overlapped almost completely with Kocuria and Lachnospiraceae_UCG.006 , respectively. Similarly, in the current study, Flavobacterium and Kocuria are potential spoilage organisms and were identified in contaminated bighead carp ( Aristichthys nobilis ) fillets stored at 4°C (Liu et al, ). Lachnoclostridium and Lachnospiraceae_UCG.006 belong to the family Lachnospiraceae in the phylum Firmicutes .…”

Section: Resultssupporting

confidence: 58%

“…The cause of this change may have been that the Proteobacteria phylum is the largest phylum of bacteria and that fish, poultry, and fermented seafood are colonized by this phylum (Ji et al, ). Some studies indicated that the proportions of Proteobacteria and Firmicutes , which are correlated with white muscle and dark muscle during chilled storage (4°C), increased greatly in spoiled common carp ( Cyprinus carpio ) (Liu, Zhang, Li, & Luo, ). There were no clear changes among the samples at the phylum level at the beginning of storage.…”

Section: Resultsmentioning

confidence: 99%

“…The dominant genera changed dramatically because the tuna was subject to temperature fluctuations (Stanbridge & Davies, ). Bacteria of the genus Acinetobacter have been isolated from a wide variety of fish samples and are the major cause of fish spoilage (Carvalheira, Ferreira, Silva, & Teixeira, ; Gennari, Parini, Volpon, & Serio, ; Kämpfer, ; Liu et al, ). According to the literature, in the case of temperature abuse, Pseudomonas and Acinetobacter are predominant organisms in the spoilage microbiota (Gill & Newton, ).…”

Section: Resultsmentioning

confidence: 99%

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Water dynamics and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics

Wang

Xie

2020

Journal of Food Safety

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This study investigated changes in the physicochemical indexes, water dynamics, and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics. With increasing storage time, the color and water activity decreased gradually, while drip loss gradually increased, and pH increased with a fluctuating pattern. The trend of bound water content was not obvious, trapped water declined, and free water increased with fluctuations over time, according to low‐field nuclear magnetic resonance (LF‐1H NMR). High‐throughput sequencing results indicated that fluctuating temperatures inhibited Acinetobacter and Pseudomonas and promoted Pelomonas, Psychrobacter, and Shewanella. Principal component analysis and redundancy analysis revealed significantly different microbial community interactions among the four groups, and environmental factors had distinct effects on bacterial community assemblages. LF‐NMR and high‐throughput sequencing can be used to monitor dynamic quality changes in bigeye tuna during simulated cold chain logistics with fluctuating temperatures.

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Section: Resultssupporting

confidence: 58%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Water dynamics and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics

Wang

Xie

2020

Journal of Food Safety

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“…; Liu et al . ). For example, temperature may vary throughout the complete production and distribution chain (Koutsoumanis ).…”

Section: Introductionmentioning

confidence: 97%

“…and pathogenic Escherichia coli must also be considered as risk factors because of their regular presence in raw or lightly preserved seafood and vegetables (Wan Norhana et al 2010;Xanthopoulos et al 2010;Di Pinto et al 2012;Park et al 2012;Lunestad et al 2013;Hoel et al 2015;Oliveira et al 2015). During storage, the microbiota of a food product is not static, but affected by several intrinsic and extrinsic factors (den Besten et al 2017;Liu et al 2017). For example, temperature may vary throughout the complete production and distribution chain (Koutsoumanis 2001).…”

Section: Introductionmentioning

confidence: 99%

Effects of storage temperature on bacterial growth rates and community structure in fresh retail sushi

2017

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12Aims: This study was conducted to assess the effects of different storage temperatures (4 to 13 20 °C), on bacterial concentrations, growth rates and community structure in fresh retail 14 sushi, a popular retail product with a claimed shelf life of two to three days. 15 Methods and Results:The maximum specific growth rate based on aerobic plate count 16 (APC) at 4 °C was 0.06 h -1 , and displayed a 6-fold increase (0.37 h -1 ) at 20 °C. Refrigeration 17 resulted in no growth of hydrogen sulfide (H2S)-producing bacteria, but this group had the 18 strongest temperature response. The bacterial community structure was determined by 19 PCR/DGGE (Denaturing gradient gel electrophoresis). Multivariate analysis based on Bray-20Curtis similarities demonstrated that temperature alone was not the major determinant for 21 the bacterial community structure. The total concentration of aerobic bacteria was the 22 variable that most successfully explained the differences between the communities. The 23 dominating organisms, detected by sequencing of DNA bands excised from the DGGE gel, 24were Brochothrix thermosphacta and genera of lactic acid bacteria (LAB). 25 Conclusion:The relationship between growth rates and storage temperatures clearly 26 demonstrates that these products are sensitive to deviations from optimal storage 27 temperature, possibly resulting in loss of quality during shelf life. Regardless of the storage 28 temperature, the bacterial communities converged towards a similar structure and density, 29 but the storage temperature determined how fast the community reached its carrying 30 capacity. 31Significance and Impact of the Study: Little information is available on the microbial 32 composition of ready-to-eat food that are prepared with raw fish, subjected to 33 contamination during handling, and susceptible to microbial growth during cold storage. 34Moreover, the data is a good first possibility to simulate growth of APC, H2S-producing 35 3 bacteria and LAB under different temperature scenarios that might occur during production, 36 distribution or storage. 37 38

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Growth Kinetics and Spoilage Potential of Co-culturing Acinetobacter johnsonii and Pseudomonas fluorescens from Bigeye Tuna (Thunnus obesus) During Refrigerated Storage

Wang

Xie

2020

Curr Microbiol

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Characterization of the microbiota in lightly salted bighead carp ( Aristichthys nobilis ) fillets stored at 4 °C

Cited by 54 publications

References 23 publications

Water dynamics and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics

Water dynamics and microbial communities of bigeye tuna (Thunnus obesus) during simulated cold chain logistics

Effects of storage temperature on bacterial growth rates and community structure in fresh retail sushi

Growth Kinetics and Spoilage Potential of Co-culturing Acinetobacter johnsonii and Pseudomonas fluorescens from Bigeye Tuna (Thunnus obesus) During Refrigerated Storage

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