Study on the Volatile Oxidation Compounds and Quantitative Prediction of Oxidation Parameters in Walnut (Carya cathayensis Sarg.) Oil

Walnut ( Carya cathayensis Sarg.) oil contains over 85% unsaturated fatty acids, which are easily oxidized during storage. As a result, a large number of volatile oxidation compounds (VOCs) are formed during oxidation. The qualitative composition of VOCs in walnut oil and quantitative prediction of the oxidation parameters (peroxide value [POV], acid value [AV], and p -anisidine value [ p -AnV]) by VOCs are investigated through SPME/GC-MS combined with partial least-squares (PLS) regression analysis. Eighteen VOCs including aldehydes, alcohols, and acids are detected by SPME/GC-MS. According to the comprehensive scores of principal component analysis (PCA), 2-octenal, hexanal, 2-heptenal, 1-octen-3-ol, hexanoic acid, and nonanal are the main products formed during oxidation. Then PLS regression is applied to developing quantitative prediction models of oxidation parameters (POV, AV, and p -AnV) by VOCs. The PLS prediction models have a good performance, with determination coefficients (R 2 p ) of 0.993–0.997 for the prediction sets of the three oxidation parameters. Practical Applications : The quantitative relationship between VOCs and oxidation parameters is developed in this study, which provided a new method for monitoring the quality of walnut oil. The SPME/GC-MS combined with PLSR is a feasible and potential method for simultaneous qualitative and quantitative analysis of oxidation process. This method is proven to have a precise predictive ability and provided a potential application in the quality assessment of other nut products. Volatile oxidation compounds (VOCs) are detected by SPME/GC-MS in oxidized walnut ( Carya cathayensis Sarg.) oil. Following this determination, partial least-squares regression (PLSR) is applied to developing quantitative prediction models of oxidation parameters by VOCs.