Standard Curve Fitting of CCD Fluorescence Scanner Based on Neural Dynamics

The substantial relative error encountered in fitting the standard curve of charge coupled device (CCD) measurements, particularly in microarray probes with low responses, significantly undermines the overall accuracy. To mitigate this challenge, we introduce a novel approach that leverages the neural dynamics optimization algorithm (NDOA) to address the constraint of minimizing relative error. This method demonstrates superior performance compared to the least squares algorithm (LSA), effectively overcoming the limitations posed by the larger relative errors associated with LSA. The experimental results, including comparative fitting from all probes and linear region’s probes, reveal that the new NDOA-optimized fitting method has good performance for the standard curve of CCD scanner. We could draw a conclusion that there are notable fitting advantages by NDOA, which presents the superior fitting accuracy compared to LSA. Meanwhile, the fitting performance in the linear region is better than that of responds in all probes. Additionally, the maximum relative error decreases to 0.53% at
${N} = 3$
with NDOA, particularly in the linear region. Especially, the simple linear fitting (
${N} = 1$
) method excluding saturated probes exhibits more excellent accuracy, decreases to 3.24% at
${N} = 1$
, than that of LSA. These exceptional metrics indicate that this newly method demonstrates excellent capability in fitting the standard curve of CCD scanner so as to improve the detection precision for microarray.