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Nondestructive Prediction of Moisture and Sodium Chloride in Cold Smoked Atlantic Salmon ( Salmo salar )
Author(s) -
Huang Y.,
Cavinato A.G.,
Mayes D.M.,
Bledsoe G.E.,
Rasco B.A.
Publication year - 2002
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1365-2621.2002.tb08773.x
Subject(s) - salmo , moisture , linear regression , fish <actinopterygii> , salt (chemistry) , water content , zoology , environmental science , chemistry , fishery , mathematics , statistics , biology , geology , geotechnical engineering , organic chemistry
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.