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- W2564685610 abstract "This study investigates a method for recognition of tea aroma using a multilayer neural network. A gas-sensing system utilising an array of quartz crystal resonators coated with plasma-organic-polymer-films is designed. Three teas, oolong, jasmine and puerh, are used as the sources of sample gas. Wavelet transform frequency analysis is applied to the signals from the gas-sensing system, and the features of the sensor response are extracted. These features are used as input vectors to a neural network. The neural network achieves an average recognition rate of 96.7% for the three tea samples. Our experimental results demonstrate the effectiveness of the gas-sensing system and neural network in this application." @default.
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- W2564685610 date "2016-10-01" @default.
- W2564685610 modified "2023-09-24" @default.
- W2564685610 title "Remarks on neural network-based tea aroma recognition with a mass-sensitive chemical sensor using plasma-organic-polymer-film-coated quartz crystal resonators" @default.
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- W2564685610 doi "https://doi.org/10.1109/icfsp.2016.7802953" @default.
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