Data fusion and hyperspectral imaging in tandem with least squares-support vector machine for prediction of sensory quality index scores of fish fillet

- HSI was first developed to predict fish sensory quality index scores.
- LS-SVM was used to establish the calibration models.
- SPA and GLGCM were used to select the informative variables.
- Data fusion of spectra and texture data showed great advantage in modelling.
- The visualization of QIS distribution map was obtained.

The study of visible and near-infrared hyperspectral imaging (400-1000 nm) in tandem with data fusion technique was conducted to predict sensory quality index scores (QIS) of grass carp fish fillet for the first time. Five characteristic wavelength variables were selected by successive projections algorithm (SPA) and 13 textural feature variables were also extracted by grey-level gradient cooccurrence matrix (GLGCM) method. Least squares-support vector machine (LS-SVM) was used to build calibration models for predicting QIS estimated by traditional quality index method (QIM) based on full spectra, optimal spectra, image texture parameters and their combined data. The LS-SVM model established by data fusion of optimal spectra and texture data showed the best prediction performance with residual predictive deviation (RPD) of 4.23, coefficient of determination (RP2) of 0.944 and root mean square error in prediction (RMSEP) of 0.703. Image processing algorithm was then developed to transfer the best LS-SVM model to each pixel for visualizing the spatial distribution of QIS. The results showed that integration of hyperspectral imaging and data fusion coupled with LS-SVM analysis provides a successful quantitative ability for predicting and visualizing the spatial distribution of QIS in grass carp fish muscle.