Does the fish rot from the head? Hyperspectral imaging and machine learning for the evaluation of fish freshness

Salmon fillet was analysed via absorption spectroscopy with a hyperspectral camera (400–1000 nm) for four consecutive storage days. Optimised K-Nearest Neighbours analyses was performed on all variables (wavelengths) returning an average classification accuracy of 77.0 % for storage day prediction (across all days). A five principal component (PC) model returned an average accuracy of 73.7 % (all days). A histogram analysis of spectral pixels in the hyperspectral images matching reference spectra displayed increases in spoilt regions as the fillet aged for a Tail Bottom fillet section. Overall, five out of 12 of the fillet areas showed a monotonic increase in spoilage (p-value≈0.01). While Principal Component Analysis showed minimal separation in foremost PCs between days, this was improved by using ‘fresh’ and ‘spoilt’ class labels prescribed by the hyperspectral data. Via mean spectrum analysis, the dampening of an absorbance band around 600 nm was identified as the main discriminator between fresh and spoilt datasets. Therefore, spatial inhomogeneity of sample freshness should be considered in fish freshness studies, and hyperspectral imaging can be useful as tool to do so.