Real time detection and identification of fish quality using low-power multimodal artificial olfaction system

Single gas quantification and mixed gas identification have been the major challenges in the field of gas detection. To address the shortcomings of chemo-resistive gas sensors, sensor arrays have been the subject of recent research. In this work, the research focused on both optimization of gas-sensing materials and further analysis of pattern recognition algorithms. Four bimetallic oxide-based gas sensors capable of operating at room temperature were first developed by introducing different modulating techniques on the sensing layer, including constructing surface oxygen defects, polymerizing conducting polymers, modifying Nano-metal, and compositing flexible substrates. The signals derived from the gas sensor array were then processed to eliminate noise and reduce dimension with the feature engineering. The gases of were qualitatively identified by support vector machine (SVM) model with an accuracy of 98.86 %. Meanwhile, a combined model of convolutional neural network and long short-term memory network (CNN-LSTM) was established to remove the interference samples and quantitatively estimate the concentration of the target gases. The combined model based on deep learning, which avoids the overfitting with local optimal solutions, effectively boosts the performance of concentration recognition with the lowest root mean square error (RMSE) of 2.3. Finally, a low-power artificial olfactory system was established by merging the multi-sensor data and applied for real-time and accurate judgment of the food freshness.