Creating added-value filet product from rainbow trout (Oncorhynchus mykiss) by salting and smoking method: physicochemical and textural attributes

Rainbow trout (Oncorhynchus mykiss) are currently consumed as live fish, primarily for catering or consumers, as an alternative to salmon in sashimi or dishes. However, Covid-19 has hampered store and restaurant operations. Therefore, developing suitable processing conditions to extend its shelf life, such as online distribution specifications while enhancing the filets' commercial value, would raise its production value. In this study, we investigated the fish filets salted in a 5% salt solution for 2 days and then smoked at 65 degrees C for 4 h under different storage conditions. As result, the higher rate of salt penetration and water loss in the resolved rigor mortis group was associated with tenderization of the meat compared to the rigor mortis group. Thermal-shrinkage and thermal-induced tissue destruction of the smoked fish filets during processing which affects the appearance, flavor, chewiness and overall acceptability. Nevertheless, according to the results of a consumer-type evaluation, the product characteristics of the fish filets from the resolution of rigor mortis group were consistent with those of the rigor mortis group, except for a weaker aroma. Thus, these results explain the relationship between frozen stored fish and the quality of processed products. The economic concept of regulating and distributing scheduling production between raw materials and finished products in the food industry conveys promising findings that will contribute to developing sustainable food processing systems.

A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification

To this day, the prevention of coronavirus disease is still an arduous battle. Medical imaging technology has played an important role in the fight against the epidemic. This paper is to perform feature selection on the CT image feature sets used for COVID-19 detection to improve the speed and accuracy of detection. In this work, the population-based intelligent optimization algorithm Aquila optimizer is used for feature selection. This feature selection method uses an S-shaped transfer function to process continuous values and convert them into binary form. And when the performance of the updated solution is not good, a new mutation strategy is proposed to enhance the convergence effect of the solution. Through the verification of two CT image sets, the experimental results show that the use of the S-shaped transfer function and the proposed mutation strategy can effectively improve the effect of feature selection. The prediction accuracy of the features selected by this method on the two open datasets is 99.67% and 99.28%, respectively. © 2022, IFIP International Federation for Information Processing.