AI有味觉了：分辨可乐和咖啡，只需“尝一尝”丨Nature

Core Viewpoint - The development of an artificial taste system based on graphene, capable of accurately sensing flavors with a success rate of up to 90%, represents a significant advancement in technology that could aid patients with neurological disorders in regaining their sense of taste [1][20][26]. Group 1: Technology Overview - The artificial taste system, referred to as GO-ISMD, utilizes graphene-based sensors to simulate human taste responses [4][11]. - Graphene's unique properties, including excellent electrical conductivity and high sensitivity to various molecules, make it an ideal material for creating artificial taste systems [9][12]. - The system operates by detecting specific changes in electrical conductivity when exposed to different chemical substances, which are then interpreted using machine learning to identify various tastes [13][14]. Group 2: Research Methodology - The researchers selected four chemical substances representing different tastes: acetic acid (sour), magnesium sulfate (bitter), sodium chloride (salty), and lead acetate (sweet) for testing [20]. - The classification process was simplified by breaking it down into four independent binary classification tasks, with a dataset comprising 160 training samples and 40 testing samples [21][22]. - The system achieved a classification accuracy of approximately 90% when identifying previously unencountered chemical substances [23]. Group 3: Performance and Implications - The artificial taste system demonstrated exceptional performance in recognizing complex beverages, such as coffee and cola, with a classification accuracy of 92.3% [25]. - This research is considered a crucial step forward, with the potential to help individuals suffering from neurological diseases recover their sense of taste [26].