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Considering sophisticated optical path system, low efficiency and accuracy in the traditional screening of food safety, some improvement methods are proposed for the traditional screening equipment of food safety. The equipment is took rear spectroscopic measuring technique and linear array Charge-coupled Device (CCD) which can realize real-time detection within visible range. Automatic liquid surface sensing and tracking technology in the process of sample injection are accepted for improving the test accuracy. Verification experiment through the equipment to accurately determine multicomponent pigment measurement, the results indicate that the equipment showed potential for the rapid testing of adulterated food.

Chinese mitten crabs (Eriocheir Sinensis) are rich in resources, wide varieties, and extensive origins. Crabs in Yangcheng Lake are most famous. However, some businessmen are driven by profits to subvert the market. In this paper, the aroma of 12 crabs of three producer areas was described by using electronic nose, and the difference in crabs' flavor profile between different producing areas was explored. 7 sensors, response data of the electronic nose were analyzed by using kernel entropy component analysis (KECA), principal component analysis (PCA), and linear discriminant analysis (LDA). Results showed that KECA had a better accuracy than PCA and LDA in locality identification. The distinguish effect of electronic nose on the crabs from different areas was overlapped, such as weeping Tai Lake and Gucheng Lake, but Yangcheng Lake crab could be distinguished dramatically from other localities in KECA. Therefore, electronic nose can be used as an important method to identify the crab habitat based on the different flavor profile of crabs.

Traditional food safety detection has many short-coming such as the complex light path system, low detection efficiency and low detection precision. In this paper, a new rapid detection equipment of food safety based on rear spectroscopic technology and Charge-coupled Device (CCD) was presented. We the photoelectric colorimetric system was redesigned. The optical path system was simplified by the rear spectroscopic technology, thus the new equipment reduced the volume and weight of the instrument than traditional food safety detection. The flat field holographic concave grating and linear array CCD can collect the plane full spectrum signal at one time, so the new equipment can apply to multicomponent pigment measurement. Experimental results prove its stability which verifies the reasonableness of software and hardware designing.

The colloidal gold rapid testing technology is easy to operate, low cost and convenient. Because of the above advantages, it is widely used in various fields, such as food safety testing, clinical diagnosis and environment pollution detection, etc. However traditional testing results will bring greater visual error, an Android-based hand-held analytical colloidal gold instrument was presented in this paper. The instrument can automatically obtain the colloidal gold image based on Android smart phone image acquisition platform, and then identify the color information of the test-line and the control-line based on the image processing research, finally the function relation between the feature vector and the concentration of the test substance solution was found to achieve quantitative analysis. Experimental results prove its stability and error rate of in quantitatively testing the colloidal gold strips.

Identification of Chinese mitten crab freshness needs to establish an effective and special quality evaluation method. However, it is difficult to identify the quality for the complicated and variable physiological parameters of live crabs. In this paper, a method to rapidly determine living Chinese mitten crab freshness using an electronic nose (E-nose) and a Kernel-based technique is studied. Specifically, a novel kernel slow feature discriminant analysis (SFDA) as a nonlinear dimension reduction technique is adopted for feature extraction on the collected original data from E-nose to achieve the purpose of dimension reduction and denoising, In terms of support vector regression (SVR) learning technique, is used for modeling quality changes of crab during storage. Experiment shows that the regression model for crab TVB-N index, comparing with PCA and kernel PCA, kernel SFDA has higher accuracy with less training and testing. Therefore, the proposed approach can be used as an alternative tool for rapid detection of crab freshness.

This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed. Refereed/Peer-reviewed