Analytical Evaluation of Simulated Kidney-Disease Profiles: Comparison of a Portable Near-Infrared Spectrophotometer with a Benchtop FT-NIR

Abstract

Chronic kidney disease (CKD) is commonly assessed using blood biomarkers such as creatinine, urea and albumin. This study compared a portable six-channel near-infrared (NIR) analyzer (miniNIR; 1300–2100 nm) with a benchtop Fourier-transform near-infrared (FT-NIR) instrument for the discrimination and quantification of fifty-four independent concentration-defined profiles in composed fetal bovine serum solutions spanning six simulated KDIGO categories. SVM-RBF, Random Forest, XGBoost, Logistic regression and partial least-squares discriminant analysis (PLS-DA) were evaluated. For miniNIR binary ‘healthy/disease’ profile discrimination, SVM-RBF yielded a validation AUC of 1.00, accuracy of 0.91, sensitivity of 1.00 and specificity of 0.83. The six-class profile assignment was substantially more difficult: PLS-DA was selected by grouped nested cross-validation, but validation accuracy was 0.18, macro-F1 was 0.11 and macro-AUC was 0.77 across the evaluable classes. For miniNIR regression, validation R2 values were 0.48 for creatinine, 0.30 for urea and 0.10 for albumin. The strongest FT-NIR validation results were R2 = 0.64 for creatinine, 0.79 for urea and 0.89 for albumin, depending on the spectral window. For FT-NIR binary ‘healthy/disease’ profile discrimination, SVM-RBF yielded a validation AUC of 0.99, accuracy of 0.71 and a specificity of 0.47. Although the high binary AUC in this controlled concentration design for the miniNIR, it should not be interpreted as evidence of CKD-specific recognition or clinical performance. The comparison quantifies the information retained and lost when continuous FT-NIR sampling is reduced to six broad discrete channels; validation in clinically characterized human samples is required before any screening application can be considered.