Fabrication of a plasmonic-nose sensor for rapid identification of plant pathogenic bacteria based on optoelectronic analysis

Abstract

Plant pathogenic bacteria are one of the most important threats to agriculture production, diminishing the growth and development of host crops. Bacterial diagnostic is based on traditional microbiological methods, including isolation, purification, and a further confirmatory immunological or molecular test for accurate identification. In this work, we present the design and fabrication of a plasmonic optoelectronic sensor based on seven “olfactory receptors” formed by seven gold nanoparticle (AuNP) morphologies, including nano bones, nanospheres, nanorods, and nano shuttles with two sizes and nanostars. The AuNPs work as a central part of the sensor for color change analysis, the principle of which is based on the reduction of Tollens’ reagent with aldehydes produced by the tested phytopathogenic bacteria during their growth. Depending on the concentration and redox potential of the produced aldehydes, the reduction of Tollens’ reagent will be a critical step in differentiating between bacteria species. A photograph captures the colorimetric response, and then the RGB values are extracted with an image analysis algorithm designed and presented here. Our results show clear chemical discrimination between five plant pathogenic bacteria after 3 h of incubation in the sensor; no misclassification was observed after this incubation time using hierarchical cluster analysis and linear discrimination analysis. In an experimental model, the sensor correctly classified Pseudomonas savastanoi pv. phaseolicola isolated from halo blight symptomatic leaves and distinguished between other fluorescent bacteria isolated from bean leaves. In addition, the image analysis algorithm presented here can improve RGB extraction due to removing interferences in the sensor substrate compared with the already reported RGB color extraction methods.