Abstract
This study explored the application of a deep neural network to the task of recognising tea types from their aroma. The aroma was measured from tea leaves using an array of quartz crystal resonators coated with plasma organic polymer films. Frequency analysis based on continuous wavelet transform, with the Morlet function as the mother wavelet, was applied to the sensor signals to construct the input vectors of the deep neural network. Experiments were conducted using oolong, jasmine and pu’erh teas as the samples and dehumidified indoor air as the base gas. The deep neural network achieved a recognition accuracy of 96.3% for the three tea types and the base gas. The experimental results demonstrated the effectiveness of applying a deep neural network to this task.
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Takahashi, K., Sugimoto, I. (2017). Remarks on Tea Leaves Aroma Recognition Using Deep Neural Network. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_14
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DOI: https://doi.org/10.1007/978-3-319-65172-9_14
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