Camellia oleifera seed, one of the most important woody oilseeds, is being paid more and more attentions for the favorable functional characteristics of the oil. In order to obtain Camellia oleifera seed for oil production, a series of postharvest processing steps, including the shelling of Camellia oleifera fruits and drying of Camellia oleifera seeds are necessary. After postharvest processing, it is essential to maintain quality of the seeds through storage over a long time. This review focused on the recently published research on different techniques that could be applied in postharvest processing and storage of Camellia oleifera seeds. Based on the recent advances, the development trends were put forward for further research, which will provide references to improve the quality of stored Camellia oleifera seeds.

Lifetime and reliability seriously affect the applications of proton exchange membrane fuel cell (PEMFC). Performance degradation prediction of PEMFC is the basis for improving the lifetime and reliability of PEMFC. To overcome the lower prediction accuracy caused by uncertainty and nonlinearity characteristics of degradation voltage data, this paper proposes a novel deep belief network (DBN) and extreme learning machine (ELM) based performance degradation prediction method for PEMFC. A DBN based fuel cell degradation features extraction model is designed to extract high-quality degradation features in the original degradation data by layer-wise learning. To tackle the issues of overfitting and instability in fuel cell performance degradation prediction, an ELM with good generalization performance is introduced as a nonlinear prediction model, which can get some enhancement of prediction precision and reliability. Based on the designed DBN-ELM model, the particle swarm optimization (PSO) algorithm is used in the model training process to optimize the basic network structure of DBN-ELM further to improve the prediction accuracy of the hybrid neural network. Finally, the proposed prediction method is experimentally validated by using actual data collected from the 5-cells PEMFC stack. The results demonstrate that the proposed approach always has better prediction performance compared with the existing conventional methods, whether in the cases of various training phase or the cases of multi-step-ahead prediction.

This study established a logarithmic relationship between the thermal death time ( D -value) of S. Enteritidis PT 30 and the moisture content ( X _W ) of the bacterial cells by conducting thermal inactivation tests on freeze-dried S . Enteritidis PT 30.