Computational 1H nuclear magnetic resonance chemical shifts for furan fatty acid discovering in oxidation process of plant oil

文献类型： 外文期刊

第一作者： Pan, Yin-Xu

作者： Pan, Yin-Xu;Xiao, Hua-Ming;Pan, Yin-Xu;Xu, Qiu-Hui;Hussain, Dilshad;Zhang, Qin-Feng;Chen, Jian-Li;Luo, Dan

作者机构：

关键词： Plant oil; Furan fatty acids; Density functional theory; 1 H NMR; Oxidative stability

期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：5.1； 五年影响因子：4.7 ）

ISSN： 0026-265X

年卷期： 2025 年 209 卷

页码：

收录情况： SCI

摘要： Furan fatty acids (FuFAs) demonstrate superior antioxidant properties compared to singlet oxygen and free radical scavengers. Identifying FuFAs in potential raw materials for biodiesel production and converting them to corresponding methyl esters can enhance the auto-antioxidant properties, thereby improving biodiesel's oxidative stability. However, current methods for identifying FuFAs are constrained by their instability and the lack of chemical standards. In this study, we present a density functional theory (DFT) assisted 1H nuclear magnetic resonance (NMR) spectroscopy method to identify and quantify potential FuFAs during the oxidation process of plant oil samples, including coconut oil (Cocos nucifera L.), cotton seed oil (Gossypium barbadense L.), and palm oil (Elaeis guineensis). The characteristic hydrogen chemical shift (delta H) at 2.47-2.66 ppm and 6.02-6.23 ppm for FuFA discovering were obtained from 18 putative FuFAs with varying chain lengths and methyl substituents. These predicted chemical shifts were utilized to detect FuFAs in plant oil samples and straight-chain long-chain fatty acids following a ten-day oxidation process. The results showed that no FuFAs were detected using our predicted 1H NMR method. Additionally, the differences in oxidation patterns among the three plant oil samples were examined to underscore the impact of distinct fatty acid compositions on FuFA formation and other oxidation products. Our methodology circumvents the limitations of complex pretreatment and the scarcity of chemical standards for FuFAs detection, making it applicable to other real samples.

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