An interdisciplinary approach to detect and grade ‘Mushy Halibut Syndrome’ in fillets of Greenland halibut (Reinhardtius hippoglossoides)

Abstract ‘Mushy halibut syndrome’ (MHS) affects the quality of Greenland halibut fillets. Fillets affected by MHS are jelly-like and turn mushy when cooked. Different severities of MHS occur in commercial catches, and cause food wastage and economic losses due to a lack of detection tools. In this study, we combined sensory evaluation, microscopy and chemical analysis to characterize and grade MHS in Greenland halibut fillets, and assessed hyperspectral imaging (HSI) as a non-invasive detection method. Fillets (n = 100) were sampled and sorted into five categories based on assessment of MHS severity using a sensory MHS grading system. Twenty fillets were assigned to each category. Fillets underwent HSI analysis (n = 100), chemical analysis (n = 40) and microscopy (n = 25). Microscopy revealed myofiber degeneration progressing with MHS severity. Average dry matter and oil content for normal fillets were 26.6%, and 10.4%, respectively, compared to 14.6% and 2.0% for fillets severely affected by MHS. The sensory evaluations demonstrated good consensus on MHS criteria but highlighted the challenges of relying on limited manual detection. Two hyperspectral models were tested: one model estimated chemical composition of fillets, while the other predicted the fillet area affected by MHS. Both showed high accuracy for detecting and grading MHS. Future work should involve larger data sets and sensory panels, fewer MHS categories and commercially relevant threshold values for dry matter and oil content. Effective grading of MHS-products could contribute to optimized utilization, development of new side-streams and reduced wastage in the fisheries.