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The use of computed tomography to estimate proximate body composition in yellowtail kingfish (Seriola lalandi)
Journal article   Open access   Peer reviewed

The use of computed tomography to estimate proximate body composition in yellowtail kingfish (Seriola lalandi)

D. Milotic, A.J. Lymbery, F. Anderson, G. Partridge, L. Pannier and G.E. Gardner
Aquaculture, Vol.625, 744363
2026
DOI: https://doi.org/10.1016/j.aquaculture.2026.744363
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Published6.42 MBDownloadView
Open Access CC BY V4.0

Abstract

Aquaculture FoV Houndsfield units Non-invasive Threshold Voltage
Yellowtail kingfish (Seriola lalandi, YTK) is growing in popularity as a production species in the Australian aquaculture industry and has potential in high-value, premium grade sashimi markets internationally. Non-invasive and chain-speed measures of carcase composition within the red meat industry have allowed effective phenotyping for genetic improvement of meat quality traits, which is yet to be developed in the aquaculture industry. Here, a computed tomography scanner (CT) was used to scan YTK at various machine settings, including voltage (100 kV, 120 kV, 135 kV), field of view (FoV) (500 mm and 320 mm) and slice width (0.5 mm, 1 mm, 3 mm and 4 mm). Four tissue segmentation thresholds were applied to determine the optimal machine settings to estimate chemical proximate composition (body fat %, fillet fat % and protein %) of whole, bulleted and skinned fillets. CT accuracy was verified through Soxhlet fat extraction and protein extraction. The best precision of estimation of body fat % and body protein % was found using fat density (−115 to 50 HU) and protein density (51 to 200 HU), at voltage 100 kV and FoV of 500 mm, while slice width had minimal effect on accuracy. While the best precision was seen in the bullet scans, the addition of whole fish weight as a model covariate substantially improved precision seen in whole fish scans. •Quantification of tissue depended on Hounsfield unit cutoffs for fat and protein.•Fish weight improved precision of CT to predict fat and protein content.•Voltage affected precision; field of view and slice width had minimal influence.•Cluster analysis patterns suggested extensive fat and protein tissue overlap.

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