Alcohol addiction is the third leading lifestyle-related cause of death in the United States. There are not enough support tools for alcoholics who want to quit alcohol consumption. The detection of drinking in a free-living environment may improve the just-in-time adaptive intervention to this behavior. Traditional methods to detect alcohol consumption suffer from long response time that hinders prompt intervention and prevention. This paper proposes to employ inertial sensors to automatically detect the drinking of alcohol in a natural environment by leveraging the hand gesture characteristics that are specific to drinking. Due to the lack of publicly available sensor dataset of alcohol drinking, this paper focused on the detection of general beverage drinking by exploiting the hand gestures. A public dataset containing seven daily activities (including hand-related activities such as eating, drinking, smoking, etc.) collected from 11 subjects in a controlled environment was adopted for this analysis. The detection model was developed using deep neural networks containing both convolutional and recurrent neural networks. The proposed approach achieved an F1-score accuracy of 0.87 in the Leave-One-Subject-Out (LOSO) cross-validation. We argue that the contributions of this paper would be useful when an alcohol-specific dataset becomes available.