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.
Highlights
The adulteration of fruit purees or juices has long been a serious problem that needs to be carefully considered by manufacturers
The training criterions, evaluation metrics, as well as the training descriptions of three deep neural networks for time series classification (TSC) are described in detail
The network structures of the Gated Recurrent Unit (GRU), the Long Short Term Memory (LSTM) and the temporal convolutional network (TCN) for TSC are introduced in detail, respectively
Summary
The adulteration of fruit purees or juices has long been a serious problem that needs to be carefully considered by manufacturers. A variety of fruits, such as apple, raspberry, blackcurrant, blackberry, plum, cherry, apricot and grape, are usually used to adulterate strawberry purees. These fruit purees are always cheaper than strawberry purees and could be mixed with strawberry purees to make impure strawberry purees. To deal with the problem of adulteration detection, a number of quality control methods have been employed, such as high-performance liquid chromatography (HPLC), thin layer chromatography (TLC) enzymatic tests (e.g., sorbitol), and physical tests (e.g., pH) [2]. These extensive chemical analyses are always time-consuming and expensive [1], which motivates the adoption of spectroscopic techniques for detecting the adulteration. [3,4,5] have successfully utilized the Fourier transform infrared (FT-IR)
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