Discrimination of Truffles and Truffle Oils by a Metal–Organic‐Framework‐Based Electronic Nose

Truffles are valuable gourmet fungi with complex aromas, but their scarcity and high cost make them frequent targets of adulteration, often through the use of synthetic compounds such as dimethyl sulfide (DMS). Current laboratory‐based detection methods are accurate but impractical for field applications. In this study, a portable electronic nose (e‐nose) system based on quartz crystal microbalance (QCM) sensors coated with seven different chiral and achiral metal‐organic framework (MOF) thin films are presented. These films are grown directly on the substrate, mainly in a layer‐by‐layer fashion, resulting in surface‐mounted MOFs (SURMOFs). The MOF‐e‐nose successfully classifies various fresh truffles as well as self‐made and commercial truffle oils. In addition, oil samples adulterated with DMS are clearly distinguished from authentic truffle oils. Using simple machine learning algorithms such as k‐nearest neighbors (kNN) and linear discriminant analysis (LDA), the sensor array achieves classification accuracies of up to 100%. The study shows that a MOF‐based e‐nose can be a fast, low‐cost, and reliable tool for truffle authentication and quality control.