Fish product quality evaluation based on temperature monitoring in cold chain

As one kind of perishable food, fish product is at risk of suffering various damages during cold chain and temperature is the most important factor to affect the product quality. This research work on frozen tilapia fillet was aimed at evaluating the fish product quality and predict shelf-life through monitoring temperature change inside the refrigerated vehicle with the radio frequency identification (RFID) technology and analyze the effect of temperature experience and profile on quality. The contrast experiment under different temperature condition was designed, namely: -18°C stable, -18°C fluctuated with ±2°C and room temperature. Temperature data were collected by RFID record at different points in the vehicle, and then product quality of three corresponding groups was evaluated according to sensory analysis and total volatile base-nitrogen (TVB-N) value. The result shows that product temperature in different point has no significant difference (P > 0.05) and product shelf- life of the same group also has a little difference between sensory analysis and TVB-N value. Shelf-life of product along with temperature fluctuated within 0.5°C was two months longer than within 2.0°C, meanwhile, the rate of quality deterioration is an accelerated process with the passage of storage time.Key words: Frozen tilapia fillet, temperature, cold chain, shelf-life, total volatile base-nitrogen, sensory evaluation

Modeling growth of specific spoilage organisms in tilapia: Comparison Baranyi with chi-square automatic interaction detection (CHAID) model

Tilapia is an important aquatic fish, but severe spoilage of tilapia is most likely related to the global aquaculture. The spoilage is mostly caused by specific spoilage organisms (SSO). Therefore, it is very important to use microbial models to predict the growth of SSO in tilapia. This study firstly verified Pseudomonas and Vibrio as the SSO of tilapia, then established microbial growth models based on Baranyi and chi-square automatic interaction detection (CHAID) models and compared their effectiveness. The results showed that both Baranyi model and CHAID model are appropriate for predicting the growth of microorganism. Baranyi model fits the microorganism growth better than CHAID model overall though CHAID model fits well at stationary phase. CHAID model predicts the microorganism growth accurately when the rate of change of the experiment data is big.Key words: Specific spoilage organisms (SSO), tilapia, chi-square automatic interaction detection (CHAID), Baranyi, shelf-life