Surface-enhanced Raman spectroscopy (SERS) has great potential in the early diagnosis of diseases by detecting the changes of volatile biomarkers in exhaled breath, because of its high sensitivity, rich chemical...

As few odorous molecules in a gas mixture make a great contribution to its odor intensity, autograding in odor intensity is the key process and of great challenge in the prevention of odor pollution. In this work, the surface-enhanced Raman spectroscopy (SERS) spectra of a gas mixture from kitchen waste are detected on plasmonic MOF nanoparticle (NP) films for autograding in odor intensity. To obtain plasmonic MOF NP films with high SERS sensitivity, AgNP@ZIF-8 NPs with different shell thicknesses and adsorbing times are investigated, which is further confirmed by the simulation of electric field distribution and airflow distribution. Then, a 45 nm shell thickness and 3H adsorbing time are selected to collect the SERS spectra of the gas mixture from kitchen waste with different odor intensities. These SERS spectra point out that the odorous molecules generated from the different statuses of kitchen waste dominate the odor intensity of the gas mixture. Furthermore, autograding in odor intensities with four levels based on deep learning (DL) is achieved here. The results exhibit excellent diagnostic accuracy (93.3%) for the external held-out overall dataset, which indicates that a highthroughput, rapid, and label-free tool for screening odor intensities could be developed. Our work not only enriches the research of gas detection by SERS spectra but also improves the prevention technique of odor pollution.

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