Artificial intelligence predictability of moisture, fats and fatty acids composition of fish using low frequency Nuclear Magnetic Resonance (LF-NMR) relaxation.

Moisture, fats and fatty acids of 14 pelagic and demersal fishes were measured by conventional chemical analysis to relate these with the proton relaxation using Low Frequency Nuclear Magnetic Resonance (LF-NMR). Artificial intelligence was used to assess the predictability of composition using six relaxation parameters of LF-NMR. Multiple linear regression showed significant prediction for moisture (W) (P < 0.00001), total fat (F) (P < 0.0001), ω-6 fatty acid (O6) (P < 0.001), saturated fats (SF), fatty acids (FA), mono-unsaturated fatty acids (MU) and ω-3 fatty acid (O3) (P < 0.01). However, the highest regression coefficient was observed for water (R²: 0.490) and the lowest was observed for SF (R²: 0.224). The low regression coefficients indicated strong non-linear relationships exited between LF-NMR parameters and composition. However, decision tree showed higher regression coefficients for all compositions considered in this study (R²:0.780–0.694). In addition, it provided simple decision rules for the prediction of composition. General Regression Neural Network provided the highest prediction capability (R²:0.847–1.000 for training and 0.506–0.924 for validation). [ABSTRACT FROM AUTHOR]