Abstract
ABSTRACT: 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: R2= 0.824, RMS = 0.55; Moisture: R2= 0.946, RMS = 2.44) than PLS (Salt: R2= 0.775, RMS = 0.63; Moisture: R2= 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.
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