期刊
ENTROPY
卷 23, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/e23101293
关键词
pork freshness; near-infrared spectroscopy; residual network; squeeze-and-excitation block; deep learning
资金
- Fundamental Research Funds for the Central Universities [2019ZDPY17]
The study developed a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) for rapid assessment of pork freshness using NIRS technique, demonstrating the potential of NIRS analysis technique in pork freshness detection.
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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