Smartphone-based colorimetric detection of milk adulteration via an AgNP/AuNP/TMB-AuGel sensor array and multivariate analysis

Abstract

In this study, a novel colorimetric sensor array composed of three nanomaterial-based sensing elements—silver nanoprisms (Ag Prism), gold nanoparticles (Au NPs), and a TMB-Au hydrogel (TMB–Au Gel)—was developed to identify and discriminate between three major milk adulterants: hypochlorite (ClO⁻), hydrogen peroxide (H₂O₂), and dichromate (Cr(VI)). The sensing elements were optimized in terms of nanoparticle volume ratios, reagent concentrations, and reaction time to achieve distinct and reproducible color responses. Smartphone-based imaging captured these changes, and RGB values were extracted for analysis. The sensor responses exhibited good reproducibility, with relative standard deviations below 5% across five replicate measurements (Cr(VI): 2.8%, ClO⁻: 4.3%, H₂O₂: 1.2%). The limits of detection were 0.62, 0.02, and 0.03 mmol L⁻¹ for H₂O₂, ClO⁻, and Cr(VI), respectively. Recovery tests in spiked milk samples confirmed the reliability of the system (H₂O₂: 100–106%, ClO⁻: 99–112%, Cr(VI): 103–115%). Pattern recognition analysis using linear discriminant analysis and hierarchical cluster analysis revealed excellent discrimination between the three adulterants, with the first two discriminant functions explaining 100% of variance (LD1: 56%, LD2: 44%) and producing clearly separated clusters. Leave-one-out cross-validation yielded an overall classification accuracy of 81%, with sensitivities ranging from 0.74% to 0.94%, specificities from 0.84% to 0.97%, and precisions from 0.75% to 0.93%. Analysis of loading plots highlighted the significance of the green channel in Au NPs and the blue channel in TMB–Au Gel for differentiating adulterants. These findings demonstrate that by integrating smartphone-based imaging for RGB extraction, the method allows for on-site, real-time analysis without specialized equipment. This capability enhances practical food monitoring, enables early detection of contaminants, and offers a user-friendly platform to improve food safety and support public health initiatives.