Spectral Analysis Methods Based on Background Subtraction and Curvature Calculation Used in the Detection or Quantification of Hemolysis and Icterus in Blood-derived Clinical Samples.

Objective We aimed to find new methods to detect and quantify hemolysis and icterus which may cause assay biases. These methods need to determine each of these interferents in the presence of various other interferents. They also need to have less stringent requirements in development and implementation than those conventional analyzers currently must satisfy. Design and methods We developed two spectral analysis methods that obtain absorption signals of interest by background subtraction or by calculating the spectral curvatures near the peaks of interest. We optimized and tested the performance of these methods using a plasma sample set with permutations of the levels of hemolysis, icterus, and lipemia (using 510 samples in total). Results The processed signals correlated well with concentrations of hemoglobin and bilirubin, indicators of hemolysis and icterus, respectively. Through iterations of randomly splitting the samples for calibration and testing, the two new methods performed as well as those used on conventional analyzers. We demonstrated that the two methods can lessen the application requirements of 1) prior knowledge of the absorption spectra of individual interferents, 2) calibration over a wide concentration range for each interferent, and 3) the need for full-range spectrophotometers spanning most of the ultraviolet/visible spectrum. We also proposed a hardware setup to detect and quantify hemolysis or icterus with a camera and two optical filters. Conclusions This work indicates that new methods of spectral analysis can reduce practical constraints in the development of interference screening systems. These methods could also benefit other assays that rely on reading spectral signals.