Background:
Fishery is an important economic industry in coastal countries and areas. Fish meat and many other seafoods are known of their fresh tastes and high nutrition, e.g. abundant proteins and unsaturated fatty acids. The improvement of life and food structure have spotlighted the white meat categories, taking fish as an example, rather than the red meat. This trend comes with higher requirements of meat quality and longer storage.
Microbial growth and metabolism are the direct factor of food spoilage. The texture of fish meat, rich in water and protein, is highly suitable for microbial proliferation, therefore fresh fish usually goes bad within 24 hours at room temperature. Microorganisms consume the nutrients and produce stinky metabolites such as amine, sulfide, alcohol, ketone and acid to make the fish distasteful. Meanwhile some bacterial metabolites are human toxins which cause diarrhea and vomiting after eating [1]. Freezing, vacuum and preservatives are common methods to extend the preservation but these methods inevitably increase the cost and affect the taste of fish.
Key Issues:
Fish meat quality test requires counting of microbial colonies. The traditional culturing method provides accurate estimations but takes 2-3 days at least. Usually a batch of fish is not allowed to enter the market until the test result is good. Fish has a poor shelf life so the test can be a burden. In combination of microbiology, math and bioinformatics, this study aims to establish a software that predicts the microbial growth on fish based on given preserving conditions, thereby reducing the test time and spoilage.
Research Feasibility:
1. The dominant microbial species during fish decomposition vary from fish to fish, but members of genus Pseudomonas and Shewanella are frequently reported to dominate the decomposing microbial communities of several fish species [2]. In this study, sea bass and salmon are selected as subject fishes to validate their dominance. If true, Pseudomonas and Shewanella will be selected as probe species whose abundance will be used to represent the total biomass or microorganisms.
2. The dynamics of microbial growth has been well studied. The primary model describes the correlation of biomass and time, can be constructed by the Logistic equation and Gompertz model. The secondary model describes microbial responses to environmental factors, can be constructed by Arrhenius equation and Square-root model. The tertiary model can be used to predict microbial growth at any time, any condition or to compare growth of different species under the same condition. Its construction requires a background of programming, luckily there are now databases like Combase and Growth Predictor [3] that enable non-professionals to construct a tertiary model by loading in parameters of the first two models. Therefore this project is technically feasible.
Research Objectives:
1. Use high-throughput sequencing to confirm the dominance of Pseudomonas and Shewanella in microbial communities during decomposition of sea bass and salmon.
2. Establish the primary and secondary models of microbial growth based on experimental data; then use bioinformatic tools to establish the tertiary model.
3. Optimize parameters of the tertiary model, then use it to predict the shelf life of fish preserved in a specific market at different temperatures.
4. Simulate a fish preservation in a market and monitor the number of microbial colonies by the traditional way. Then use the experimental data to evaluate the predictive precision of the tertiary model.
Project Schedule:
This project is expected to complete within 2 years. Detailed schedule is as following:
2020.1-6 | Monitor the changes of microbial communities in a fish preservation by high-throughput sequencing, to determine the probe species; |
2020.7-9 | Establish the primary model of microbial growth at different temperatures by the traditional culturing method; |
2020.10-12 | Quantify microbial metabolites in fish preservation that may affect the taste and nutrition of fish meat, e.g. amine and total-volatile-base nitrogen; |
2021.1-3 | Merge multiple primary models into a secondary model, then use bioinformatic tool to establish a tertiary model; |
2021.4-6 | Use the tertiary model to predict the shelf life and meat quality of fish preserved in a specific market at different temperatures. |
2021.7-12 | Simulate fish preservation in market and quantify microbial colonies by the traditional way. Then evaluate the predictive precision of the tertiary model. |
Expected Achievements:
•A multi-disciplinary international cooperation;
•Publication of 2 SCI articles;
•Application of 1 utility potent;
•Training of 1 Ph.D. and 1 M.Sc. student.
References:
1. Baranyi, J., & Tamplin, M. L. (2004). ComBase: A Common Database on Microbial Responses to Food Environments. Journal of Food Protection, 67(9), 1967–1971. doi: 10.4315/0362-028x-67.9.1967
2. Gram, L., & Dalgaard, P. (2002). Fish spoilage bacteria – problems and solutions. Current Opinion in Biotechnology, 13(3), 262–266. doi: 10.1016/s0958-1669(02)00309-9
3. Gram, L. (2009). Microbiological Spoilage of Fish and Seafood Products. Compendium of the Microbiological Spoilage of Foods and Beverages, 87–119. doi: 10.1007/978-1-4419-0826-1_4