Although odor classification and odor quantification by e-nose have been studied for a long time, the next stage is to express a detected scent using language. The methods used to map molecular structure parameters, mass spectra, and sensor responses onto language expression are reviewed first. NLP (Natural Language Processing) is useful for that purpose. Conversely, the linguistic expression of the scent can be transformed into sensing data. The odor mixture can be generated so that the measured response pattern can be identical to that of the scent to be created. Two methods, optimization-based and generative AI-based ones, to search for the recipe of the created scent, are explained. Finally, the intended odor is generated using an olfactory display. We provide the latest information on the emerging technology of scent creation.
Keywords: deep neural network; diffusion model; mass spectrum; natural language processing; odor descriptor; odor reproduction; olfactory display; optimization; scent creation.