Spectroscopic quantitation of tetrazolium formazan in nano-toxicity assay with interval-based partial least squares regression and genetic algorithm

Abstract

Spectrophotometric quantitation of formazan in tetrazolium-based nanoparticle (NP) toxicity assay requires a robust calibration model immune to optical interference. For the first time, variant of partial least squares (PLS) regression models, such as, full-spectrum (250–700nm) PLS, interval PLS (iPLS), backward interval PLS (biPLS), and synergy interval PLS (siPLS) models have been adopted for formazan quantitation. Models were evaluated based on root mean square error of cross-validation (RMSECV), and prediction (RMSEP). The spectral variables in optimal iPLS, biPLS and siPLS models, as well as variables retained above a selection frequency threshold (for all intervals), were further refined in a genetic algorithm (GA). The results suggest that the optimal biPLS (140 variables, 5 LVs, RMSECV: 0.4438, RMSEP: 0.2936) and siPLS (88 variables, 5 LVs, RMSECV: 0.4401, RMSEP: 0.316) models were superior either to the full-spectrum PLS (4 LVs, RMSECV: 0.9674, RMSEP: 0.4618) or traditional single wavelength calibration (414nm, RMSECV: 2.0864, RMSEP: 2.1628). Minimum RMSEP (0.2976) was observed when GA was performed on spectral variables retained (above a threshold frequency) from the cumulative frequency distribution of all siPLS models. Finally, applicability of the selected PLS regression models in real NP toxicity assay is demonstrated.