Enhancing bighead carp cutting: Chilled storage insights and machine vision-based segmentation algorithm development

Abstract

To enhance market demand and fish utilization, cutting processing is essential for fish. Bighead carp were cut into four primary cuts: head, dorsal, belly, and tail, collectively accounting for 77.03% of the fish's total weight. These cuts were refrigerated at 4 °C for 10 days, during which the muscle from each cut was analyzed. Pseudomonas.fragi proliferated most rapidly and was most abundant in eye muscle (EM), while Aeromonas.sobria showed similar growth patterns in tail muscle (TM). Notably, EM exhibited the highest rate of fat oxidation. TM experienced the most rapid protein degradation. Furthermore, to facilitate the cutting applied in mechanical processing, a machine vision-based algorithm was developed. This algorithm utilized color threshold and morphological parameters to segment image background and divide bighead carp region. Consequently, each cut of bighead carp had a different storage quality and the machine vision-based algorithm proved effective for processing bighead carp.