Tuna are highly priced fishes that are often used in processed products. For effective fishery management and protection of consumers’ rights, it is important to develop a molecular method to identify the species of the tuna products. In this study we have developed a molecular method based on real-time polymerase chain reaction (real-time PCR) technology for the rapid identification of four tuna species. Four species-specific TaqMan probes were designed to identify bigeye tuna (Thunnus obesus), Pacific bluefin tuna (Thunnus orientalis), southern bluefin tuna (Thunnus maccoyii), and yellowfin tuna (Thunnus albacares). A SYBR green system was also designed to enhance the authentication of T. obesus. Both systems can distinguish target species from others in an efficient and high-throughput manner and can be applied to species identification of tuna products. 相似文献
Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control.
The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties.
This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods. 相似文献