The effects of thermal oxidation at 65 °C for 24 days on oxidation indices, fatty acid positional distribution, thermal properties, vitamin E composition and sterol composition of kenaf seed oil are investigated. The results showed that total oxidation value (TOTOX) of the oil increased from initial 8.83 to 130.74 at the end of 24 days storage. Linoleic acid at sn‐1, 3 positon of kenaf seed oil was less stable than the one at sn‐2 positon. Oxidative degradation changed the melting profile of kenaf seed oil, the value of endothermic enthalpy reduced from 58.17 to 20.25 J/g after 24 days of storage. Moreover, the content of vitamin E and total sterol decreased by 84.26% and 38.47%, respectively. Tocotrienols were more stable than tocopherols during the accelerated storage. Correlation analysis indicated vitamin E content was significantly related to p‐anisidine value, while sterol content was significantly related to peroxide value.
Practical Application
Kenaf seed oil is rich in polyunsaturated fatty acids and bioactive compounds. Heating process and long‐term storage cause oil oxidation and bioactive compounds degradation. The oxidation process of kenaf seed oil is simulated with accelerated storage. The study evaluates fatty acid composition and distribution, vitamin E and sterol content, melting thermal characteristics of kenaf seed oil at different oxidation levels. The research shows the stability of fatty acid is related with its type and position in backbone of triacylglycerol molecule. There are good correlation among oxidation level, vitamin E and sterol content, and melting enthalpy value of kenaf seed oil.
In order to forecast the corporate finance performance, we must choose the appropriate forecast method. The forecast model widely used at present lacks generalization ability and the accuracy is not approving. In this paper, we propose an improved version of support vector machines (named AdaBoost support vector machine) to forecast financial performance of Chinese listed companies. In the choice of kernel function of support vector machine, we compare forecast results for each kernel function and its associated parameters in order to identify the most appropriate forecasting model. The experiment results show that AdaBoost-support vector machine model with rbf kernel function behaves quite well than other methods (such as probabilistic neural network and decision tree model).
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