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Bimetallic nanocolloidal plasmonic array for polyphenols characterization and calibration-free antioxidant capacity evaluation
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Abstract
Phenolic compounds (PC) are natural molecules with antioxidant capacity (AoC) with recognized technological and health properties; rapid and easy-to-use approaches for PC class and AoC evaluation are highly required. Herein, a bimetallic plasmonic nanoparticles-based approach for the untargeted evaluation of PC-pattern and AoC is proposed. The rationale relies on the PC's ability to drive the formation of bimetallic silver/gold nanocolloidal ‘probes’ with different conformations. Ag/Au bimetallic nanostructures, according to the PCs’ amount and class, return characteristic plasmonic and colorimetric tags. Plasmonic indexes are proposed to assess the dominant PC classes, while the colorimetric response, analyzed simply by a smartphone, is employed to obtain an AoC score, with no calibration. The methods were tested with PCs belonging to different chemical classes, and challenged to classify different food samples; the proposed approach allows PC-dominant class identification and AoC-evaluation consistent with HPLC-MS/MS and conventional photometric assays.
Springer Science and Business Media LLC
Title: Bimetallic nanocolloidal plasmonic array for polyphenols characterization and calibration-free antioxidant capacity evaluation
Description:
Abstract
Phenolic compounds (PC) are natural molecules with antioxidant capacity (AoC) with recognized technological and health properties; rapid and easy-to-use approaches for PC class and AoC evaluation are highly required.
Herein, a bimetallic plasmonic nanoparticles-based approach for the untargeted evaluation of PC-pattern and AoC is proposed.
The rationale relies on the PC's ability to drive the formation of bimetallic silver/gold nanocolloidal ‘probes’ with different conformations.
Ag/Au bimetallic nanostructures, according to the PCs’ amount and class, return characteristic plasmonic and colorimetric tags.
Plasmonic indexes are proposed to assess the dominant PC classes, while the colorimetric response, analyzed simply by a smartphone, is employed to obtain an AoC score, with no calibration.
The methods were tested with PCs belonging to different chemical classes, and challenged to classify different food samples; the proposed approach allows PC-dominant class identification and AoC-evaluation consistent with HPLC-MS/MS and conventional photometric assays.
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