Deep Neural Networks for the Classification of Pure and Impure Strawberry Purees

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number1223
Journal / PublicationSensors (Switzerland)
Volume20
Issue number4
Online published23 Feb 2020
Publication statusPublished - Feb 2020

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Abstract

In this paper, a comparative study of the effectiveness of deep neural networks (DNNs) in the classification of pure and impure purees is conducted. Three different types of deep neural networks (DNNs)—the Gated Recurrent Unit (GRU), the Long Short Term Memory (LSTM), and the temporal convolutional network (TCN)—are employed for the detection of adulteration of strawberry purees. The Strawberry dataset, a time series spectroscopy dataset from the UCR time series classification repository, is utilized to evaluate the performance of different DNNs. Experimental results demonstrate that the TCN is able to obtain a higher classification accuracy than the GRU and LSTM. Moreover, the TCN achieves a new state-of-the-art classification accuracy on the Strawberry dataset. These results indicates the great potential of using the TCN for the detection of adulteration of fruit purees in the future.

Research Area(s)

  • Adulteration detection, Deep neural networks, Fruit purees, GRU, LSTM, TCN

Citation Format(s)

Deep Neural Networks for the Classification of Pure and Impure Strawberry Purees. / Zheng, Zhong; Zhang, Xin; Yu, Jinxing et al.
In: Sensors (Switzerland), Vol. 20, No. 4, 1223, 02.2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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