Image-Based Characterization and Statistical Optimization of Silver Nanoparticles Biosynthesized Using Pasteurized Milk

Abstract

Background: Green synthesis of silver nanoparticles (AgNPs) has garnered attention for its sustainability, yet few studies have integrated digital imaging for nanoparticle characterization. Objective: This study aimed to synthesize AgNPs using pasteurized milk as a natural reducing and stabilizing agent, and to optimize synthesis conditions using a rotatable central composite design (CCD), coupled with spectroscopic and image-based analytical methods. Methods: AgNPs were synthesized under varying conditions of milk dilution, AgNO₃ concentration, and reaction time. Response variables-SPR wavelength, absorbance, particle size, and imaging-derived parameters (∆RGB, ∆Lab, MGL) were modeled using second-order polynomial regression. Digital imaging under forward, backward, and transmitted light geometries were used to quantify nanoparticle-induced optical changes. Results: Most models showed high predictive power (adjusted R² > 0.80), with image-based variables (∆RGB, ∆ASM, MGL) strongly correlated with particle concentration and optical density. Optimal conditions (milk:DI 1:15, 2.00 mM AgNO₃, 2 h) yielded AgNPs with a strong SPR response (412 nm), small size (95.8 nm), and distinct visual signatures. Predicted responses matched closely with experimental data, validating the model. Conclusion: This study presents a reproducible, low-cost platform for sustainable AgNP synthesis. The incorporation of digital imaging enhances real-time monitoring and offers promising applications in diagnostics, food safety, and green nanotechnology.