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

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...

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Bibliographic Details
Published in:Journal of Food Science
Main Authors: Huang, Y., Cavinato, A.G., Mayes, D.M., Bledsoe, G.E., Rasco, B.A.
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2002
Subjects:
Atlantic salmon
Salmo salar
Online Access:http://dx.doi.org/10.1111/j.1365-2621.2002.tb08773.x
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1365-2621.2002.tb08773.x
http://onlinelibrary.wiley.com/wol1/doi/10.1111/j.1365-2621.2002.tb08773.x/fullpdf
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Summary: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: 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.

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