Development of a Nondestructive Moldy Coffee Beans Detection System Based on Electronic Nose

• Published in: IEEE sensors letters Vol. 7; no. 2; pp. 1 - 4
• Main Authors: Tang, Chang-Lin, Chou, Ting-I, Yang, Sang-Ren, Lin, Yi-Jhen, Ye, Zhong-Kai, Chiu, Shih-Wen, Lee, Sheng-Wei, Tang, Kea-Tiong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; classification algorithm; Classification algorithms; Coffee; coffee beans; Data collection; dimensionality reduction algorithm; Discriminant analysis; electronic nose (E-nose) system; Electronic noses; Environmental control; Feature extraction; Gas detectors; Gas sampling; Green products; mold degree; Principal components analysis; sensor application; Sensor applications; Sensor arrays; Sensor systems; Sensors; Smell; Support vector machines; Temperature sensors
• ISSN: 2475-1472; 2475-1472
• DOI: 10.1109/LSENS.2023.3241943

Coffee drinks prepared from moldy coffee beans can adversely affect human health. No convenient screening method for detecting the smell of stale coffee beans exists. Accordingly, this study developed an electronic nose (E-nose) system for detecting the smell of coffee beans. This system comprises an environmental control system, a sensor array, and a data signal readout system. The system can distinguish various degrees of mold on coffee beans through the recognition of the smell of the coffee beans. In this study, we established a standard operating procedure to collect gas samples from coffee beans in a temperature- and humidity-controlled environment and recorded changes in the signals by using the sensor array after introducing the target gas. Features were first extracted from the collected data, then dimensionality reduction methods, such as principal component analysis and linear discriminant analysis, were applied to these features. Thus, their complexity was reduced, and the noise was eliminated. K-nearest neighbor and support vector machine were adopted as classification algorithms, and the classification accuracy of the proposed system reached 91.77%.