Rapid detection of waste cooking oil using low-field nuclear magnetic resonance

Abstract

The illegal circulation of waste cooking oil (WCO) in the market makes the development of an efficient detection method an urgent task. Therefore, 20 batches of WCOs were collected and compared with 15 kinds of edible oils using low-field nuclear magnetic resonance (LF-NMR) combined with the chemometrics method. LDA, PLS-DA and SVM-DA models were proven to totally discriminate WCO from 15 kinds of edible oils, and T 22 -related parameters were crucial factors in the discrimination processes. WCO was blended at a ratio from 5% to 50% into corn oil (CO), olive oil (OO), peanut oil (PO), rapeseed oil (RO) and soybean oil (SO) in the following adulteration assay. The SVM-DA model showed a better classification effect than the LDA and PLS-DA models in differentiating edible oils from corresponding WCO-adulterated oils, and WCO-adulterated CO was relatively more difficult (80%<validation accuracy<88.6%) to classify than the other groups (82.9%<validation accuracy<100%). Finally, four quantitative models of WCO-adulterated OO, PO, RO and SO were well established using the PLSR method, with R 2 > 0.956, RMSEC<0.035 and RMSEP<0.038. These results illustrate that LF-NMR combined with chemometrics analysis can provide promising rapid screening of WCO. • LF-NMR detection distinguished 20 batches of WCOs from 15 kinds of edible oils. • The parameters of T 22 fraction were crucial factors to discriminate WCOs from edible oils. • SVM-DA model showed better classification effect than the LDA and PLS-DA models. • PLSR model was established to calculate the adulteration level of WCOs.