Prediction of fatty acid composition in camellia oil by 1H NMR combined with PLS regression

A rapid method for the determination of fatty acid (FA) composition in camellia oils was developed based on the 1 H NMR technique combined with partial least squares (PLS) method. Outliers detection, LVs optimization and data pre-processing selection were explored during the model building process. The results showed the optimal models for predicting the content of C18:1, C18:2, C18:3, saturated, unsaturated, monounsaturated and polyunsaturated FA were achieved by Pareto scaling (Par) pretreatment, with correlation coefficient ( R 2 ) above 0.99, the root mean square error of estimation and prediction (RMSEE, RMSEP) lower than 0.954 and 0.947, respectively. Mean-centering (Ctr) was more suitable for the model of C16:0 and C18:0 with the best performance indicators ( R 2  ≥ 0.945, RMSEE ≤ 0.377, RMSEP ≤ 0.212). This study indicated that 1 H NMR has the potential to be applied as a rapid and routine method for the analysis of FA composition in camellia oils.