Circadian rhythm is an inherent endogenous biological rhythm in living organisms. However, with the improvement of modern living standards, many factors such as prolonged artificial lighting, sedentarism, short sleep duration, intestinal flora and high-calorie food intake have disturbed circadian rhythm regulation on various metabolic processes, including GLP-1 secretion, which plays an essential role in the development of various metabolic diseases. Herein, we focused on GLP-1 and its circadian rhythm to explore the factors affecting GLP-1 circadian rhythm and its potential mechanisms and propose some feasible suggestions to improve GLP-1 secretion.

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This work presented an approach for color and texture classification of green tea using Least Squares Support Vector Machine (LSSVM). Color features extracted from histogram of every channel in RGB and HSI color space, texture features computed from Grey Level Co-occurrence Matrix (GLCM) of every channel in RGB and HSI color space, and different combinations of the color and texture features, were used respectively as input data set for the LSSVM classifiers. The classification performances of these different methods were compared. The results show that the combined color and texture features from HSI color space give the best performance with accuracy of 96.33% for prediction unknown samples in testing set. Based on the results, it can be concluded that combined color and texture features coupled with a LSSVM classifier can be a fast and non-destructive technique efficiently utilized to classify green tea.

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