Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution

[Display omitted] •Demonstrated a low cost reusable sensor platform for detection of trypsin and plasmin based on self-assembled layer of casein.•Demonstrated a sub-nanomolar detection level of protease, which is 25x better than a commercial fluorescence sensor or 3x better than any research sensor reported in the literature.•Integrated machine learning algorithms with analysis of multi- frequency QCMresponse. We demonstrate a machine learning enabled low-cost acoustic detection of protease which may find application in assuring quality and safety of dairy products, drug screening, molecular profiling, and disease diagnostics. A hydrophilic SiO2-coated quartz crystal microbalance (QCM) acts as a substrate to assemble α-, β-, and ĸ-casein layers (protease reporters) and as a transducer for measuring changes in frequency as casein is removed by protease. We demonstrate that α-, β-, and ĸ-caseins can form stable assembly on SiO2 from phosphate-buffered solution (PBS) solution. Exposure to protease results in cleaving of casein which changes the frequency of the 1st–11th odd harmonics of QCM. Monitoring β-casein cleavage allows ∼0.2 nM detection of trypsin and ∼0.5 nM detection of plasmin and enables differentiation between trypsin and plasmin after