Chemical-Based Surface Plasmon Resonance Imaging of Fingerprints

Fingerprints are extremely useful in personal identification; however, they are usually based on physical rather than chemical images because it remains a challenge to reveal a clear chemical fingerprint easily and sensitively. Herein, a surface plasmon resonance imaging (SPRi) method, combined with a chemically selective stepwise signal amplification (CS(3)A) strategy, is proposed to chemically image fingerprints with adjustable sensitivity and clarity. High-fidelity glucose-associated fingerprint images were obtained at five to seven cycles of CS(3)A based on the recognition reaction of concanavalin A (ConA) with dextran. The method is also extendable to image substances that possess and/or can be tagged with ConA- or dextran-recognizable groups. For demonstration, SPRi of carboxylic substances was conducted by amidating the carboxyl group with glucosamine to enable the ConA-based CS(3)A. Glucose- and carboxyl-based fingerprints were simultaneously and clearly imaged, allowing us to perform quantitative analysis of the representative of either glucose or amino acid (e.g., serine) or both. The curves measured from the standard spots were linear in the ranges of 1-4000 mu M for glucose and 3.2-4000 mu M for serine, with linear correlated coefficients of 0.9979 and 0.9962, respectively. It was then applied to the study of metabolic secretions in fingerprints during running exercise, yielding variation tendencies similar to those measured from sweat samples in the literature. As a noninvasive tool, the CS(3)A-coupled SPRi reveals both clear images of fingerprints and quantitative chemical information, and it is anticipated to become a competitive new method for chemically imaging fingerprints.

Automated summary

The study proposes a method that combines surface plasmon resonance imaging (SPRi) and chemically selective stepwise signal amplification (CS(3)A) to chemically image fingerprints with adjustable sensitivity and clarity. The method is effective in obtaining high-fidelity glucose-associated fingerprint images and can also be extended to image substances that possess recognizable chemical groups. Additionally, the method allows for quantitative analysis and reveals metabolic secretions in fingerprints during physical activity.