RESUMEN
Direct analysis of biometals in biomedical samples by energy dispersive X-ray fluorescence (EDXRF) for disease diagnostics has hardly been fully explored due to dark matrix analytical challenges. In this study, we exploited multivariate chemometrics modeling of cancer diagnostics in model human tissue simulates and cultures using selected biometals' (Mn, Fe, Cu, Zn and Se) fluorescence and Compton scatter profiles. PCA successfully reduced the correlated data dimension to uncorrelated datasets for the characterization of the cell cultures. Artificial neural network (ANN) enhanced the classification of cancer staging and the development of a multivariate calibration strategy for the quantification of trace elements. ANN characterized cancer into early, intermediate, and advanced stages of development. Low concentrations of Fe (101 ± 28 ppm), Zn (59 ± 4 ppm) and Cu (21 ± 1 ppm) were evident in SV10 due to the lag phase stage of cancer development. Further, strong correlation (0.976) was evident in early-stage cancer between Zn and Se but with strong negative correlations between Mn and Se (-0.973) and between Mn and Zn (-0.900) probably due to their antioxidant effects. The results show predictable and systematic associations between the concentrations of Fe, Cu, Zn, Se and Mn as cancer biomarkers with the potential to be used for cancer diagnosis at the early stage of development.