Fig. 1. Size distribution and morphology of ZnO and MgO nanoparticles (NPs). A and B, Hydrodynamic size distributions of ZnO NPs (A) measured by dynamic light scattering and MgO NPs (B) analyzed by the instrument’s fitting software. Histograms represent particle count versus mean diameter, with red curves indicating log-normal distribution fits. The zeta potential (ζ) values are noted, reflecting colloidal stability: 28.62 mV for ZnO NPs and 15.08 mV for MgO NPs. C and D, Scanning electron microscopy images of ZnO (C) and MgO (D) NPs at different magnifications, showing the structure of ZnO and the dispersed, irregular morphology of MgO particles.

Fig. 2. Fourier transform infrared spectroscopy (FTIR) spectral analysis of different rice samples from various geographical origins and processing states. A, FTIR spectra of brown rice (a) and white rice (b) from Mexico, brown rice (c) and white rice (d) from the United States of America, white rice from Thailand (e), and white rice from India (f). B, A representative FTIR spectrum highlighting key functional group assignments.

Fig. 3. Comparison of elemental concentrations in different rice types from various countries, including white rice (WR) and brown rice (BR) sourced from India (Ind), Thailand (Tha), the United States of America (USA), and Mexico (Mex). A-I show concentrations (mg/kg dry weight) of selenium (Se, A), potassium (K, B), zinc (Zn, C), copper (Cu, D), calcium (Ca, E), magnesium (Mg, F), iron (Fe, G), lead (Pb, H), and arsenic (As, I). Data are mean ± SE (n = 3). Different lowercase letters above bars indicate statistically significant differences at the 0.05 level among rice types for each element based on ANOVA followed by the post-hoc test.

Fig. 4. Lipid (A) and protein (B) contents measured in white rice (WR) and brown rice (BR) sourced from India (Ind), Thailand (Tha), the United States of America (USA), and Mexico (Mex). Data are mean ± SE (n = 3). Different letters above bars indicate statistically significant differences at the 0.05 level among rice types for each element based on ANOVA followed by post-hoc test.

Fig. 5. Growth response of Escherichia coli in presence of different rice types. A and B, Growth curves of E. coli (A) and maximum E. coli growth (B) (measured by OD600) over an 8-h inoculation period in media supplemented with various white (WR) and brown (BR) rice types from India (Ind), Thailand (Tha), the United States of Ameirica (USA), and Mexico (Mex). Data are mean ± SE (n = 3). Different lowercase letters above bars indicate statistically significant differences at the 0.05 level among rice types for each element based on ANOVA followed by post-hoc test.

Fig. 6. Inhibition of Escherichia coli growth by nanoparticles (NPs) and ionic treatments over an 8-h period. A, Growth curves of E. coli in rice treated with ZnO NPs, ZnSO4, and control. B, Growth curves of E. coli in rice treated with MgO NPs, MgSO4, and control.

Fig. 7. Comparative Escherichia coli growth in rice samples treated with ZnO and MgO nanoparticles (NPs) versus their ionic counterparts on white (WR) and brown (BR) rice from India (Ind), Thailand (Tha), the United States of Ameirica (USA), and Mexico (Mex). A, Effect of ZnO NPs and ZnSO4 on E. coli growth across different rice types. B, Effect of MgO NPs and MgSO4 on E. coli growth across different rice types. Statistical significance (P < 0.05) among rice types is indicated by different lowercase letters above bars.

Fig. 8. Protein concentration in various rice types after exposure to Escherichia coli under different nanoparticles (NPs) and ionic treatments on white (WR) and brown (BR) rice from India (Ind), Thailand (Tha), the United States of America (USA), and Mexico (Mex). A, Protein levels in rice samples treated with ZnO NPs, ZnSO4, and control. B, Protein levels in rice samples treated with MgO NPs, MgSO4, and control. Statistical significance (P < 0.05) among rice types is indicated by different lowercase letters above bars.