In the modern world, the use of alcohol is increasing day by day in a variety of products across different industries like medical and pharmaceutical, food and beverage, cosmetics and personal care, automotive industries, etc. As a consequence, it becomes significantly important to classify the presence of these alcohols to reduce their harmful effects in a very sophisticated way. Hence, the significance of the research in developing an intelligent system to recognize the presence of alcohol is drawing the researchers’ attention in modern times. On the other hand, the development of dictionary learning (DL) based approach and sparse modeling has emerged as a precursor in the machine learning based research domain over the past few years. Numerous DL based sparse solutions have been successfully proposed for solving different signal processing and image processing problems in recent times. In some of our previous work, we have demonstrated how supervised and unsupervised DL based approaches can effectively be employed for solving different problems in the domain of ambient assisted living. Advancing the scope of our studies, in this work, we have shown how DL based approach can be successfully employed for effectively classifying different alcohol categories, based on sensory signals acquired from five Quartz Crystal Microbalance (QCM) sensors. In this work, we first investigate the effectiveness of one prominent category of supervised DL based approach, called dictionary learning with structured incoherence and shared atoms (DLSI) for an available benchmark alcohol dataset, in detail. To improve the performance of the DLSI approach further, we propose a modified version of DLSI by introducing an intelligent threshold selection strategy for handling the sharing features among all dictionaries in a novel manner, which is the most significant issue in maintaining incoherence. The performance evaluation establishes that our proposed novel threshold selection strategy based on a modified DLSI approach outperforms other recent competing approaches.