MoleSen: From Macro Sensing to Micro Molecular-level Taste Sensing

Taste perception plays an essential role in promoting human health and maintaining nutritional balance. Current taste perception techniques usually require expensive equipment and delicate storage conditions, which restrict their use primarily to laboratory settings. In this paper, we show that terahertz (THz) signals generate unique fingerprint spectra when interacting with different taste molecules in aqueous solutions. Building on this finding, we propose Molecular-level Taste Sensing (MoleSen), a contact-free gustatory sensing method aimed at achieving wireless human-like perception. Specifically, MoleSen emits terahertz signals towards the aqueous solution, captures the reflected signals, and then determines the type and concentration of the tastes by analyzing the unique fingerprint spectra of the reflected signals influenced by the taste molecules. In MoleSen, we design a bio-inspired deep learning model (DTB, Digital Taste Bud) to identify the subtle taste features diluted by water molecules. Additionally, we incorporate domain adaptive learning to address the issue of feature distribution shifts when multiple tastes are mixed. Through extensive experiments involving over 247,000 samples, we demonstrate that MoleSen can accurately differentiate the five basic tastes—sour, bitter, salty, sweet, and umami—with an accuracy of 98.5% for taste type determination and 96.9% for concentration detection. Moreover, MoleSen outperforms the human's taste sensitivity and achieves a highly accurate perception even for mixed tastes.