Zou Liang, Liu Weinan, Lei Meng, Yu Xinhui
School of Information and Electrical Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Entropy (Basel). 2021 Sep 30;23(10):1293. doi: 10.3390/e23101293.
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.
有效且快速地评估猪肉新鲜度对于监测猪肉品质具有重要意义。然而，传统的感官评价方法主观性强，物理化学分析耗时。在本研究中，采用近红外光谱（NIRS）技术这一快速且无损的分析方法来测定猪肉新鲜度。鉴于常用的统计建模方法需要对数据进行预处理才能获得满意的性能，本文提出了一种一维挤压激励残差网络（1D-SE-ResNet）来构建猪肉新鲜度与近红外光谱之间的复杂关系。所开发的模型通过挤压激励（SE）块增强了一维残差网络（1D-ResNet）。作为一种深度学习模型，该方法能够自动从输入光谱中提取特征，并且可以用作端到端模型来简化建模过程。所提方法与五种流行分类模型的比较表明，1D-SE-ResNet具有最佳性能，分类准确率达到93.72%。该研究表明基于深度学习的近红外光谱分析技术为猪肉新鲜度检测提供了一种有前景的工具，因此有助于确保食品安全。
