Biomimetic Electronic Nose for Coffee DetectionBased on Nanosensor Arrays and MachineLearning Algorithms

With the continuous development of electronic nose technology, the application of electronic nose in detecting and recognizing odors is becoming increasingly widespread. The electronic nose consists of a series of sensors that convert odor data into analog data for further processing. Machine learning (ML)technology has been used to analyze electronic nose data in various fields. People also have different requirements for the different characteristics of coffee, so testing in the coffee industry is necessary. This article studies the mainstream odor detection methods in current food detection and classification, and plans to use support vector machines for coffee classification and detection. Support vector machines are suitable for small sample data, canhandle nonlinear problems, and have good robustness and interpretability. The results indicate that support vector machines are feasible. However, a single machine learning model has limitations, and future research will focus on the effectiveness offusion models in odor classification and detection.