Sebastian Hauschild , Jan-Philipp Schreiter and Horst Hellbruck , Luebeck University of Applied Sciences, Germany

A novel approach for determining the freshness of fish and meat involves the use of cantileversensors, which analyse the concentration of cadaverine on the surface. The cantilever sensor isexcited with a voltage sweep around its resonance frequency and the frequency shift due to depositson the sensor is measured. In this work, we present a draft of a distributed system and compareAI-based analysis of the stored cantilever sensor data with raw sweep data without preprocessing.We defined a meat quality index (mqi) range for the measurements, which depends on the frequency shiftbetween a reference and cadaverine measurement. We investigated, that the best practice to predictthe mqi value is to use classical machine learning models such as Random Forest, LightGBM,XGBoost where Random Forest performs best with anval. / test accuracy of up to 72.01 % / 71.67%, precisionof 72.37 % / 72.53%, recall of72.01 % / 71.67 % and F1-Score of72.06 % / 71.72 %.

Cantilever, Machine Learning, Database, Distributed Systems, Sustainability