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 (250–700 nm) PLS, interval PLS ( i PLS), backward interval PLS ( bi PLS), and synergy interval PLS ( si PLS) 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 i PLS, bi PLS and si PLS 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 bi PLS (140 variables, 5 LVs, RMSECV: 0.4438, RMSEP: 0.2936) and si PLS (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 (414 nm, 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 si PLS models. Finally, applicability of the selected PLS regression models in real NP toxicity assay is demonstrated.

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