Portable and reusable optofluidics-based biosensing platform for ultrasensitive detection of sulfadimidine in dairy products.

Sulfadimidine (SM2) is a highly toxic and ubiquitous pollutant which requires rapid, sensitive and portable detection method for environmental and food monitoring. Herein, the use for the detection of SM2 of a portable optofluidics-based biosensing platform, which was used for the accurate detection of bisphenol A, atrazine and melamine, is reported for the first time. The proposed compact biosensing system combines the advantages of an evanescent wave immunosensor and microfluidic technology. Through the indirect competitive immunoassay, the detection limit of the proposed optofluidics-based biosensing platform for SM2 reaches 0.05 µg·L(-1) at the concentration of Cy5.5-labeled antibody of 0.1 µg·mL(-1). Linearity is obtained over a dynamic range from 0.17 µg·L(-1) to 10.73 µg·L(-1). The surface of the fiber probe can be regenerated more than 300 times by means of 0.5% sodium dodecyl sulfate solution (pH = 1.9) washes without losing sensitivity. This method, featuring high sensitivity, portability and acceptable reproducibility shows potential in the detection of SM2 in real milk and other dairy products.

Two-dimensional maximum local variation based on image euclidean distance for face recognition.

Manifold learning concerns the local manifold structure of high dimensional data, and many related algorithms are developed to improve image classification performance. None of them, however, consider both the relationships among pixels in images and the geometrical properties of various images during learning the reduced space. In this paper, we propose a linear approach, called two-dimensional maximum local variation (2DMLV), for face recognition. In 2DMLV, we encode the relationships among pixels in images using the image Euclidean distance instead of conventional Euclidean distance in estimating the variation of values of images, and then incorporate the local variation, which characterizes the diversity of images and discriminating information, into the objective function of dimensionality reduction. Extensive experiments demonstrate the effectiveness of our approach.