Application of spectra cross-correlation for Type II outliers screening during multivariate near-infrared spectroscopic analysis of whole blood

In this study, a simple screening algorithm was developed to prevent the occurrence of Type II errors or samples with high prediction error that are not detected as outliers. The method is used to determine “good” and “bad” spectra and to prevent a false negative condition where poorly predicted samples appear to be within the calibration space, yet have inordinately large residual or prediction errors. The detection and elimination of this type of sample, which is a true outlier but not easily detected, is extremely important in medical decisions, since such erroneous data can lead to considerable mistakes in clinical analysis and medical diagnosis. The algorithm is based on a cross-correlation comparison between samples spectra measured over the region of 4160–4880 cm− 1. The correlation values are converted using the Fisher's z-transform, while a z-test of the transformed values is performed to screen out the outlier spectra. This approach allows the use of a tuning parameter used to decrease the percentage of samples with high analytical (residual) errors. The algorithm was tested using a dataset with known reference values to determine the number of false negative and false positive samples. The cross-correlation algorithm performance was tested on several hundred blood samples prepared at different hematocrit (24 to 48%) and glucose (30 to 500 mg/dL) levels using blood component materials from thirteen healthy human volunteers. Experimental results illustrate the effectiveness of the proposed algorithm in finding and screening out Type II outliers in terms of sensitivity and specificity, and the ability to predict or estimate future or validation datasets ensuring lower error of prediction. To our knowledge this is the first paper to introduce a statistically useful screening method based on spectra cross-correlation to detect the occurrence of Type II outliers (false negative samples) for routine analysis in a clinically relevant application for medical diagnosis.