High-Throughput Barcode Fluorescence Sensor Signal Rapid Processing: Integration of Multiple Algorithms and Visualization Implementation

The field of fluorescence barcode sensor signal processing is the focus, with the development of a high-throughput fluorescence detection visualization platform that integrates multiple efficient fluorescence array localization algorithms, including manual mask positioning, an optimized k-means++ algorithm, and a projection iterative algorithm designed based on fluorescence image projection data. Comprehensive monitoring, real-time adjustment, and integrated data analysis are enabled by the platform, significantly enhancing the accuracy, speed, and convenience of fluorescence image processing. Experimental results demonstrate that the projection iterative algorithm excels in both accuracy and processing speed, achieving a mean squared error (mse) of 6.42130, which is 20.97% lower than that of the k-means++ algorithm, and a processing time of 1095 ms, which is 80.21% and 94.37% faster than the mask positioning and k-means++ algorithms, respectively. Furthermore, evaluation of different fitting algorithms shows that sigmoid curve fitting performs best across various metrics, with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> value as high as 0.987480, indicating excellent data interpretability and fitting accuracy. Overall, precise, rapid, and efficient signal processing in high-throughput fluorescence detection is achieved, providing robust technical support and innovative tools for related research fields.