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Odor profiles of three grades of Jinhua, Xuanwei, and Rugao dry-cured hams were analyzed and distinguished by both the electronic nose and the sensory evaluation. The odor was absorbed by bamboo sticks, which is the most traditional absorption method to classify different ham grades. Then data from electronic nose was analyzed by discriminant function analysis (DFA) and cluster analysis (CA), compared with that from sensory evaluation by principal component analysis (PCA). Results showed that different grades of Jinhua, Xuanwei, and Rugao dry-cured hams could be distinguished effectively by the DFA results of electronic nose. However, sensory evaluation could not perform as well as electronic nose. It was demonstrated that intelligent sensory technology has higher sensitivity and reliability in classifying producing regions and grades of dry-cured ham.

The robustness and real-time performance are of the greatest significance for the navigation of the patrol robot in the transformer substation. To meet this demand, a navigation and position approach is presented in this paper based on color vision and RFID(Radio Frequency Identification) technology. In the presented system, the position information is provided by RFID tags and navigation is completed by the extraction of guidelines. Based on the deep analysis of the advantages and shortcoming of different color space, a new approach integrating the good real-time performance of grayscale image process and rich information in color images process is presented to improve the robustness and real-time performance of navigation. Fast Hough transform is selected and combined with least square method to detect the navigation line. Experimental results show that the presented method can meet the real-time and robust demand of the navigation of patrol robot.