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

Chang Lin Tang, Ting I. Chou, Sang Ren Yang, Yi Jhen Lin, Zhong Kai Ye, Shih Wen Chiu, Sheng Wei Lee, Kea Tiong Tang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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%.

Original languageEnglish
Article number6001204
JournalIEEE Sensors Letters
Volume7
Issue number2
DOIs
StatePublished - 1 Feb 2023

Keywords

  • Sensor applications
  • classification algorithm
  • coffee beans
  • dimensionality reduction algorithm
  • electronic nose (E-nose) system
  • mold degree
  • sensor application

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