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In order to definite the effects of the extraction and cold pressing methods on the nutrition of Camellia oleifera oil. The fatty acid composition, micronutrient, nutrient elements of Camellia oleifera oil were analyzed, then the quality of oil was evaluated by principal component factor analysis to study the effect of cold pressing and extraction methods on the nutritional components of Camellia oleifera oil, which from five main producing were as of Guizhou province were studied in this paper. The results showed that fatty acid composition and content of Camellia oleiformis oil prepared by pressing and extraction method were not significantly different(P>0.05), the unsaturated fatty acids accounted for about 90% of the total fatty acids, and oleic acid was the highest, about 79%~81%, followed by linoleic acid, about 5%~8%. The average content of five main producing were as oil made by pressing method, its squalene and Vitamin E was higher about 12.56%, 115.76% than that of extraction method, respectively. There was a significant difference between different regions of Camellia oleifera oil content of nutrient elements(PK>Na>Mg>Fe>Mn>Zn>Ni>Cu>Se. The contents of Ca, Fe, Ni, Cu, Zn and Se in the cold pressed oil were higher than extraction method oil, including content of Ca, Fe, Zn were higher 18.77%, 461.66%, 78.09% and Ni, Cu and Se were not detected in the extraction method. The extraction method was beneficial to obtain higher 66.75%, 48.24%, 79.16%, 75.46% contents of K, Na, Mg and Mn than cold pressing method, respectively. The results indicated that the local Hongqiu oleotea of Ceheng and Camellia meiocarpa of Songtao had the best quality of the combined scores were 0.902 and 0.560, respectively, followed by the local Camellia of Liping and Tianzhu Xianglin series, followed by Xianglin 210 Camellia of Yuping. Principal component factor analysis showed that the comprehensive score of pressing method was higher than extraction method that of when only the fatty acid composition, squalene, vitamin E and elements nutritions were considered. This study enriched the basic theory of quality analysis of Camellia oleifera and provided new methods and ideas for scientific evaluation of quality of Camellia oleifera in Guizhou.

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L1/2 norm framework for feature extraction, and uses L2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46–21.81%.