In their work, Yu et al. explored the generation of AgNP@ZIF-8 later integrated in a paper substrate for detecting the odor intensity generated by kitchen waste. The authors combined Ag nanoparticles encapsulated in ZIF-8 shells with controllable thicknesses (from 20 to 100 nm). As schematized in Figure , the SERS-active core-shell particles were transferred to a filter paper from a nanofilm generated in an organic solvent -water interface. This MOF-based SERS substrate was then exposed to odor sources and analyzed by SERS in conjunction with deep-learning (DL) model to classify the odor intensity into four categories: Odorless, Faint, Strong, and Disgusting odor. Specifically, kitchen waste odor was simulated by collecting different typical residues, from rice, eggshells, meat, vegetables, paper and plastic, and placed into a container with a rubber plug (see Figure ). The MOF-based SERS substrates were then exposed to the gas generated from this mixture and the SERS spectra were recorded after different exposure times (Figure -ii). By recording the Raman intensity of different peaks for each sample and performing a PCA (Figure -iii-iv), the authors shown that the system possess a good autograding capability towards the distinction of each of the odor intensity levels. The SERS data from known samples was then employed to train the DL model (Figure ); using 96 SERS spectral data as training set, 25 SERS spectral data as verification set, and 31 SERS spectral data as test set, DL approach succeeded on the identification of smell intensity with a >99% accuracy. This is a promising result that highlights the potential of MOF-based SERS substrates for VOC detection in different fields, which can be translated to solving daily-life-problems.