Compton scatter-modulated fluorescence and multivariate chemometric (artificial neural network (ANN) and principal component regression (PCR)) calibration strategy was explored for direct rapid trace biometals (Mn, Fe, Cu, Zn, Se) analysis in "complex" matrices (model soft tissues). This involved spectral feature selection (multiple fluorescence signatures) normalized to or in conjunction with Compton scatter. ANN model resulted in more accurate trace biometal determination (R>0.9) compared to PCR. Hybrid nested (ANN and PCR) approach led to optimized accurate biometals' concentrations in Oyster tissue (≤ ± 10%).
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