Abstract
A unique optofluidic lab-on-a-chip device that can measure optically encoded forward scattering signals has been demonstrated. From the design of the spatial pattern, the position and velocity of each cell in the flow can be detected and then a spatial cell distribution over the cross section of the channel can be generated. According to the forward scattering intensity and position information of cells, a data-mining method, support vector machines (SVMs), is applied for cell classification. With the help of SVMs, the multi-dimensional analysis can be performed to significantly increase all figures of merit for cell classification.