食品领域白色可降解与不可降解塑料的识别：一种结合高光谱技术的动态残差网络

摘要：在食品领域，随着人们健康与环保意识的提高，可降解塑料替代不可降解塑料已成为趋势，但二者外观十分相似， 难以区分。本文提出了一种白色不可降解与可降解塑料的快速识别方法。首先，利用高光谱成像系统采集塑料在可见光 和近红外波段（380–1038 nm）的高光谱图像。其次，根据高光谱信息的特点设计残差网络（ResNet）。最后，在ResNet 中引入动态卷积模块，建立动态残差网络（Dy-ResNet），自适应地挖掘数据特征，实现可降解与不可降解塑料的分类。 Dy-ResNet比其他经典深度学习方法具有更好的分类性能。可降解与不可降解塑料的分类准确率为99.06%。综上所述， 高光谱成像技术与Dy-ResNet相结合可以有效识别白色不可降解与可降解塑料。

关键词：食品塑料分类 降解性 高光谱成像技术 残差网络 动态卷积

期刊：Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

DOI：https://DOI.org/10.1016/j.saa.2023.122686

设备：GaiaSorter

Identification of white degradable and non-degradable plastics in food f ield: A dynamic residual network coupled with hyperspectral technology

Abstract：In the food field, with the improvement of people’s health and environmental protection awareness, degradable plastics have become a trend to replace non-degradable plastics. However, their appearance is very similar, making it difficult to distinguish them. This work proposed a rapid identification method for white non- degradable and degradable plastics. Firstly, a hyperspectral imaging system was used to collect the hyper spectral images of the plastics in visible and near-infrared bands (380–1038 nm). Secondly, a residual network (ResNet) was designed according to the characteristics of hyperspectral information. Finally, a dynamic convolution module was introduced into the ResNet to establish a dynamic residual network (Dy-ResNet) to adaptively mine the data features and realize the classification of the degradable and non-degradable plastics. Dy-ResNet had better classification performance than the other classical deep learning methods. The classifi cation accuracy of the degradable and non- degradable plastics was 99.06%. In conclusion, hyperspectral imaging technology was combined with Dy-ResNet to identify the white non-degradable and degradable plastics effectively.

Keywords：Food plastic classification Degradability Hyperspectral imaging technology Residual network Dynamic convolutional