The simulation of human sensory functions is a key trend in the field of sensor development. In taste sensing, taste biosensors emulate taste perception using biorecognition elements that participate in taste transduction, such as taste receptors, cells, tissues, etc. This approach obtains high selectivity and a wide detection range of human taste perception, making taste biosensors widely used in food analysis and taste perception studies. By combining biorecognition elements with suitable data processing and analysis techniques, the taste information generated during the process of taste transduction, obtained by the sensing elements of the sensor, can be accurately captured. In this paper, we explore current available solutions to stability and sensitivity, and other challenges in taste biosensors using taste receptors, cells, and tissues as sensing elements. We also outline the applied signal processing techniques based on the signal characteristics from different types of taste biosensors. Finally, it is proposed that the development of taste biosensing sensors will further promote the application of intelligent sensory evaluation and human perception analysis systems in food, medicine, and other fields.

End-tidal breath gases originate deep within the lungs, and their composition is an especially accurate reflection of the body’s metabolism and health status. Therefore, accurate collection of end-tidal breath gases is crucial to enhance electronic noses’ performance in breath detection. Regarding this issue, this study proposes a novel electronic nose system and employs a threshold control method based on exhaled gas flow characteristics to design a gas collection module. The module monitors real-time gas flow with a flow meter and integrates solenoid valves to regulate the gas path, enabling automatic collection of end-tidal breath gas. In this way, the design reduces dead space gas contamination and the impact of individual breathing pattern differences. The sensor array is designed to detect the collected gas, and the response chamber is optimized to improve the detection stability. At the same time, the control module realizes automation of the experiment process, including control of the gas path state, signal transmission, and data storage. Finally, the system is used for breath detection. We employ classical machine learning algorithms to classify breath samples from different health conditions with a classification accuracy of more than 90%, which is better than the accuracy achieved in other studies of this type. This is due to the improved quality of the gas we extracted, demonstrating the superiority of our proposed electronic nose system.