Near-infrared (NIR) spectroscopy combined with the partial least-squares (PLS) regression was successfully applied for the rapid quantitative analysis of hemoglobin (HGB) based on human whole blood samples. Based on the varied divisions for the calibration and prediction sets, an appropriate waveband with stability was selected through a rigorous modeling process. Among 215 samples, 80 were randomly selected as the validation set. The remaining 135 samples were divided into the calibration set (80 samples) and the prediction set (55 samples) for a total of 200 times with certain similarities. The selected waveband was the combination of visible and short-wave NIR region at 400 nm to 1100 nm. The optimal PLS factor was 8, the modeling effect M-SEPAve, M-RP,Ave, M-SEPStd and M-RP,Std were 2.88gL-1, 0.986, 0.34gL-1 and 0.003, respectively, the validation effect V-SEP, V-RP and V-RSEP were 2.92gL-1, 0.986 and 2.26%, respectively. The results indicated that the method has high prediction precision and well stability. It is hopeful to be applied to clinic.
Glucose Sensing in Human Whole Blood Based on Near-Infrared Phosphors and Outlier Treatment with the Programming Language “R”
