Comparison of Infrared Spectroscopy and Nuclear Magnetic Resonance Techniques in Tandem with Multivariable Selection for Rapid Determination of ω-3 Polyunsaturated Fatty Acids in Fish Oil

This study investigated the potential of using four spectroscopic techniques including visible–short-wave near infrared, long-wave near infrared (LNIR), mid-infrared, and nuclear magnetic resonance (NMR) spectroscopy in tandem with multivariable selection and calibration for rapid determination of three important ω-3 polyunsaturated fatty acids (PUFA), namely eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and docosapentaenoic acid (DPA) in fish oil. Quantitative models were established between the spectral data and reference PUFA contents of samples based on partial least squares regression (PLSR) algorithm. Successive projections algorithm (SPA) and uninformative variable elimination (UVE) were used to select the most important variables for prediction. The average decrements of 23.20 % for root mean square error of cross-validation (RMSECV) and 64.90 % for the absolute value between root mean square error of calibration and RMSECV (AV_RMSE) in all 12 cases achieved after over 90 % variables were eliminated. UVE was also found to be helpful to improve the efficiency of SPA’s variable selection in 8/12 cases. The best predictions for EPA, DHA, and DPA were all achieved by NMR spectroscopy (determination coefficients of cross-validation ( r CV 2 ) of 0.970, 0.982, and 0.983 and the RMSECV of 11.48, 4.73, and 0.77 mg/g for the EPA, DHA, and DPA predictions, respectively). LNIR spectra also did good predictions similar to NMR. The results demonstrated that the laborious and time-consuming gas chromatography method could be replaced by spectroscopic techniques in tandem with PLSR modeling and variable selection in order to provide a rapid and reliable inspection of PUFA in fish oil.