Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system

Because the shape of prawn is not round, spectroscopy instruments cannot measure the spectra of the whole prawn without containing background information. In this study, an online hyperspectral imaging system in the spectral region of 380-1100 nm was developed to determine the moisture content of prawns at different dehydrated levels. Hyperspectral images of prawns were acquired at different dehydration periods. The spectra of prawns then were extracted from hyperspectral images based on 'Manual Prawn Mask' and 'Automatic Prawn Mask', respectively. Spectral data were analyzed using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM) to establish the calibration models, respectively. Successive projections algorithm (SPA) was first applied for the optimal wavelength selection in the hyperspectral image analysis. Out of 482 wavelengths, only twelve wavelengths (428, 445, 544, 569, 629, 672, 697, 760, 827, 917, 958, and 999 nm) were selected by SPA as the optimum wavelengths for moisture prediction. Based on these optimum wavelengths, a multiple linear regression (MLR) calibration model was established and used to obtain the moisture distribution of each prawn. The overall results of this study revealed the potentiality of hyperspectral imaging as an objective and non-destructive method to obtain the content and distribution of moisture of prawns whose shapes are not round.