RESUMO
In recent years, smartphones have been integrated into rapid colorimetric sensors for heavy metal ions, but challenges persist in accuracy and efficiency. Our study introduces a novel approach to utilize biogenic gold nanoparticle (AuNP) sensors in conjunction with designing a lightbox with a color reference and machine learning for detection of Fe3+ ions in water. AuNPs were synthesized using the aqueous extract of Eleutherine bulbosa leaf as reductants and stabilizing agents. Physicochemical analyses revealed diverse AuNP shapes and sizes with an average size of 19.8 nm, with a crystalline structure confirmed via SAED and XRD techniques. AuNPs exhibited high sensitivity and selectivity in detection of Fe3+ ions through UV-vis spectroscopy and smartphones, relying on nanoparticle aggregation. To enhance image quality, we developed a lightbox and implemented a reference color value for standardization, significantly improving performance of machine learning algorithms. Our method achieved approximately 6.7% higher evaluation metrics (R 2 = 0.8780) compared to non-normalized approaches (R 2 = 0.8207). This work presented a promising tool for quantitative Fe3+ ion analysis in water.