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

Mu, Honglei, Gao, Haiyan, Chen, Hangjun, Fang, Xiangjun, Zhou, Yongjun, Wu, Weijie, Han, Qiang
European journal of lipid science and technology 2019 v.121 no.6 pp. e1800521
Carya cathayensis, acid value, aldehydes, gas chromatography-mass spectrometry, hexanoic acid, least squares, monitoring, octenol, oxidation, p-anisidine value, peroxide value, prediction, principal component analysis, quantitative analysis, unsaturated fatty acids, volatile organic compounds, walnut oil, walnuts
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 (R²ₚ) 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.