Surface plasmon resonance (SPR) biosensors are an extremely sensitive optical technique used to detect the changes in refractive index occurring at the sensor interface. Fluorescence involves the emission of light by a substance that has absorbed light or other electromagnetic radiation, and the parameters of the absorbed and emitted radiation are used to identify the presence and the amount of specific molecules in a specimen. SPR biosensors and fluorescence analysis are both effective methods for real-time detection. The combination of these technologies would improve the quantitative detection sensitivity of fluorescence analysis and the specificity of SPR detection. We designed and developed an SPR and fluorescence synchronous detection system. The SPR module was based on two kinds of modulation methods, and the fluorescence module was capable of switching between four wavelengths. The fluorescence microspheres and A549 cells of different concentration were both detected by the SPR and fluorescence method synchronously in real time. The fluorescent signal and the optical signal of the SPR were shown to correlate. The correlation coefficient for fluorescent microspheres detection reached up to 0.9866. The system could be used in cell analysis and molecule diagnosis in the future.

Nonwoven fiber materials are materials with multifunctional purposes, and are widely used to make masks for preventing the new Coronavirus Disease 2019. Because of the complexity and particularity of their structure, it becomes difficult to model the penetration and flow characteristics of liquid in nonwoven fiber materials. In this paper, a novel seepage time soft sensor model of nonwoven fabric, based on Monte Carlo (MC), integrating extreme learning machine (ELM) (MCELM) is proposed. The Monte Carlo method is used to expand data samples. Then, an ELM method is used to establish the prediction model of the dyeing time of the nonwoven fiber material overlaps with the porous medium, as well as the insertion degree and height of the different quantity of hides. Compared with the back propagation (BP) neural network and radial basis function (RBF) neural network, the results show that the prediction model based on the MCELM method has significant power in terms of accuracy and prediction speed, which is conducive to the precise and rapid manufacture of nonwoven fiber materials in practical applications between liquid seepage characteristics and structural characteristics of porous media. Furthermore, the relationship between the proposed models has certain value for predicting the behavior and use of nonwoven fiber materials with different structural characteristics and related research processes.