Comparison of hyperspectral imaging and computer vision for automatic differentiation of organically and conventionally farmed salmon

This study was carried out to explore the potential of computer vision system (CVS) and two hyperspectral imaging (HSI) systems covering the visible and short-wave near infrared range (400-1000 nm) and the long-wave near infrared range (897-1753 nm), respectively, for differentiation of organic and conventional farm-raised salmon fillets in fresh and chill-stored conditions. Partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and random forests (RF) classifiers were used to build classification models for recognition and authentication of the tested samples. The results suggested that hyperspectral discrimination performed much better than CVS. For the same validation set, the highest correct classification rate (CCR) equivalent to 98.2% was presented in two SVM models, one was built from full spectral variables in the 400-1000 nm and the other from four optimal wavelengths in the same spectral region. The best prediction for CVS was obtained by using PLS-DA with CCR of 83.6% for validation, while the most satisfying results for HSI in 897-1753 nm were achieved by applying SVM algorithm on full spectral region as well as 10 important wavelengths with both CCRs of 92.7% for validation. In a nutshell, hyperspectral imaging in the 400-1000 nm has presented the best predictive ability to differentiate between organic and conventional salmon fillets, while SVM classifier has been confirmed to be very powerful for multivariable analysis in our case. Overall, the outcome achieved from this research strongly suggested the capability of the hyperspectral imaging for objective and rapid categorization of the two salmon varieties under circumstances of fresh and chill-stored conditions.