A New Strategy for Microbial Taxonomic Identification through Micro‐Biosynthetic Gold Nanoparticles and Machine Learning

Abstract Microorganisms can serve as biological factories for the synthesis of inorganic nanomaterials that can become useful as nanocatalysts, energy‐harvesting–storage components, antibacterial agents, and biomedical materials. Herein, the development of biosynthesis of inorganic nanomaterials into a simple, stable, and accurate strategy for distinguishing microorganisms from multiple classification levels (i.e., kingdom, order, genus, and species) without gene amplification, biochemical testing, or target recognition is reported. Gold nanoparticles (AuNPs) biosynthesized by different microorganisms differ in color of the solution, and their features can be characterized, including the particle size, the surface plasmon resonance (SPR) spectrum, and the surface potential. The inter‐relation between the features of micro‐biosynthetic AuNPs and the classification of microorganisms are exploited at different levels through machine learning to establish a taxonomic model. This model agrees well with traditional classification methods that offers a new strategy for microbial taxonomic identification. The underlying mechanism of this strategy is related to the biomolecules produced by different microorganisms including glucose, glutathione, and nicotinamide adenine dinucleotide phosphate‐dependent reductase that regulate the features of micro‐biosynthetic AuNPs. This work broadens the application of biosynthesis of inorganic materials through micro‐biosynthetic AuNPs and machine learning, which holds great promise as a tool for biomedical research.