Hyperspectral imaging in combination with data fusion for rapid evaluation of tilapia fillet freshness

The potential of two different hyperspectral imaging systems (visible near infrared spectroscopy (Vis-NIR) and NIR) was investigated to determine TVB-N contents in tilapia fillets during cold storage. With Vis-NIR and NIR data, calibration models were established between the average spectra of tilapia fillets in the hyperspectral image and their corresponding TVB-N contents and optimized with various variable selection and data fusion methods. Superior models were obtained with variable selection methods based on low-level fusion data when compared with the corresponding methods based on single data blocks. Mid-level fusion data achieved the best model based on CARS, in comparison with all others. Finally, the respective optimized models of single Vis-NIR and NIR data were employed to visualize TVB-N contents distribution in tilapia fillets. In general, the results showed the great feasibility of hyperspectral imaging in combination with data fusion analysis in the nondestructive evaluation of tilapia fillet freshness.

Automated summary

The study investigated the potential of two different hyperspectral imaging systems, Vis-NIR and NIR, in determining TVB-N contents in tilapia fillets during cold storage. By comparing the calibration models established with variable selection and data fusion methods, it was found that low-level fusion data based variable selection methods resulted in superior models. Mid-level fusion data, particularly based on CARS, achieved the best model. Ultimately, the study demonstrated the great feasibility of hyperspectral imaging combined with data fusion analysis for nondestructive evaluation of tilapia fillet freshness.