An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy |
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Authors: | Liang Zou Weinan Liu Meng Lei Xinhui Yu |
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Institution: | 1.School of Information and Electrical Control Engineering, China University of Mining and Technology, Xuzhou 221116, China; (L.Z.); (W.L.); (M.L.);2.Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada |
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Abstract: | Effective and rapid assessment of pork freshness is significant for monitoring pork quality. However, a traditional sensory evaluation method is subjective and physicochemical analysis is time-consuming. In this study, the near-infrared spectroscopy (NIRS) technique, a fast and non-destructive analysis method, is employed to determine pork freshness. Considering that commonly used statistical modeling methods require preprocessing data for satisfactory performance, this paper presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIRS. The developed model enhances the one-dimensional residual network (1D-ResNet) with squeeze-and-excitation (SE) blocks. As a deep learning model, the proposed method is capable of extracting features from the input spectra automatically and can be used as an end-to-end model to simplify the modeling process. A comparison between the proposed method and five popular classification models indicates that the 1D-SE-ResNet achieves the best performance, with a classification accuracy of 93.72%. The research demonstrates that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection and therefore is helpful for ensuring food safety. |
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Keywords: | pork freshness near-infrared spectroscopy residual network squeeze-and-excitation block deep learning |
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