Nondestructive Prediction of Moisture and Sodium Chloride in Cold Smoked Atlantic Salmon (Salmo salar)

摘要

Salt and moisture contents in cold-smoked salmon were determined using short-wavelength near-infrared (SW-NIR) reflectance spectroscopy (600 to 1100 nm). Partial least square (PLS) regression models yielded the best results among 3 linear regression methods tested. Back-propagation neural networks (BPNN) exhibited a somewhat better capability to model salt and moisture concentrations (Salt: R~2 = 0.824, RMS = 0.55; Moisture: R~2 = 0.946, RMS = 2.44) than PLS (Salt: R~2 = 0.775, RMS = 0.63; Moisture: R~2 = 0.936, RMS = 2.65). Selection of samples from different axial locations on a fish did not affect the prediction error for salt or WPS but affected the prediction error for moisture.