Artificial nose of scalable plasmonic array gas sensor for Multi-Dimensional SERS recognition of volatile organic compounds

Abstract

Gas sensing is one kind of promising analytical technique for high-flux screening and evaluation. Volatile organic compounds (VOCs) analysis by surface-enhanced Raman spectroscopy (SERS) is arising but usually focuses on a single species of VOCs due to the limitations of complexity, stability, and accuracy in practical applications. Canine animals have a sensitive sense of smell due to their large number of olfactory cells. Inspired by this, here we innovatively integrate scalable plasmonic arrays and multi-dimensional chemometrics for simultaneous SERS detection of multiple food-borne VOCs. Both direct and indirect SERS strategies are integrated by coating the array surface with MOFs or different monolayers to provide multi-dimensional recognition and readouts. Four species of VOCs including bacterial metabolites, hydrogen sulfide, aldehyde, and biogenic amine were selectively captured and produced unique SERS spectral profiles. The multi-dimensional outputs greatly increase the dimensionality of VOC fingerprints, thereby significantly improving the sensitivity, reliability, and accuracy for freshness discrimination and forecasting in real food smell evaluation. By virtue of multi-dimensional SERS recognition and machine learning, the array gas sensor shows higher accuracy than traditional SERS analysis and is a promising platform for in situ, real-time, and high-flux gas sensing and inspection.