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Oil stability prediction by high-resolution (13)c nuclear magnetic resonance spectroscopy.

机译:通过高分辨率(13)c核磁共振波谱预测油的稳定性。

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13C NMR spectra of oil fractions obtained chromatographically from 66 vegetable oils were obtained and analyzed to evaluate the potential use of those fractions in predicting oil stabilities and to compare those results with oil stability prediction by using chemical determinations. The oils included the following: virgin olive oils from different cultivars and regions of Europe and north Africa; "lampante" olive, refined olive, refined olive pomace, low-erucic rapeseed, high-oleic sunflower, corn, grapeseed, soybean, and sunflower oils. Oils were analyzed for fatty acid and triacylglycerol composition, as well as for phenol and tocopherol contents. By using stepwise linear regression analysis (SLRA), the chemical determinations and the (13)C NMR data that better explained the oil stability determined by the Rancimat were selected. These selected variables were related to both the susceptibility of the oil to be oxidized and the content of minor components that most contributed to oil stability. Because (13)C NMR considered many more variables than those determined by chemical analysis, the predicted stabilities calculated by using NMR data were always better than those obtained by using chemical determinations. All these results suggest that (13)C NMR may be a powerful tool to predict oil stabilities when applied to chromatographically enriched oil fractions.
机译:获得并分析了从66种植物油中色谱获得的油馏分的13 C NMR光谱,并进行了分析,以评估这些馏分在预测油稳定性中的潜在用途,并将这些结果与通过化学测定预测的油稳定性进行比较。这些油包括:来自欧洲和北非不同品种和地区的纯橄榄油; “兰潘特”橄榄,精制橄榄,精制橄榄果渣,低芥酸菜籽油,高油酸向日葵油,玉米,葡萄籽油,大豆油和向日葵油。分析了油中的脂肪酸和三酰基甘油组成,以及苯酚和生育酚的含量。通过使用逐步线性回归分析(SLRA),选择了化学测定值和(13)C NMR数据,可以更好地解释由Rancimat确定的油稳定性。这些选择的变量与被氧化的油的敏感性和最有助于油的稳定性的次要成分的含量有关。因为(13)C NMR考虑的变量比化学分析确定的变量多得多,所以使用NMR数据计算出的预测稳定性始终好于通过化学测定获得的稳定性。所有这些结果表明,(13)C NMR在应用于色谱富集的油馏分时,可能是预测油稳定性的有力工具。

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