Mature longan (Dimocarpus longan Lour.) fruit has a succulent, edible and white aril, which has gained popularity as an exotic fruit in temperate regions. It is prized on the international market resulting in an increased production with significant contributions to local economic development. Longan fruit contains significant amounts of bioactive compounds such as corilagin, ellagic acid and its conjugates, 4-O-methylgallic acid, flavone glycosides, glycosides of quercetin and kaempferol, ethyl gallate 1-??-O-galloyl-d-glucopyranose, grevifolin and 4-O-??-l-rhamnopyranosyl-ellagic acid. The fruit has been used in the traditional Chinese medicinal formulation, serving as an agent in relief of neural pain and swelling. The application of ultrasonic-assisted extraction or high pressure-assisted extraction greatly increases the yield from longan pericarp or seeds. In recent years, some pharmacological activities such as anti-tyrosinase, anti-glycated and anticancer activities, and memory-enhancing effects of longan aril, pericarp or seed extract have been found, implicating a significant contribution to human health. Regarding the increasing cultivation area and increasing quantity of longan fruit in the world, further utilization of this fruit is expected in an effort to use more efficiently the inherent bioactive compounds. The paper reviews the recent advances in the extraction and pharmacological activities of bioactive compounds from longan fruit. Some novel pharmacological potential of longan fruit is also discussed in this paper. 相似文献
The harmonic reducer is an essential kinetic transmission component in the industrial robots. It is easy to be fatigued and resulted in physical malfunction after a long period of operation. Therefore, an accurate in-situ fault diagnosis for the harmonic reducers in an industrial robot is especially important. This paper proposes a fault diagnosis method based on deep learning for the harmonic reducer of industrial robots via consecutive time-domain vibration signals. Considering the sampling signals from industrial robots are long, narrow, and channel-independent, this method combined a 1-dimensional convolutional neural network with matrix kernels (1-D MCNN) adaptive model. By adjusting the size of the convolution kernels, it can concentrate on the contextual feature extraction of consecutive time-domain data while retaining the ability to process the multi-channel fusion data. The proposed method is examined on a physical industrial robot platform, which has achieved a prediction accuracy of 99%. Its performance is appeared to be superior in comparison to the traditional 2-dimensional CNN, deep sparse automatic encoding network (DSAE), multilayer perceptual network (MLP), and support vector machine (SVM).