目的 针对常见塑料包装材料外观相似、成分差异微小且传统检测方法效率低、易受人为因素影响等问题,探索近红外光谱技术结合深度学习模型在塑料包装材料快速识别中的应用潜力。方法 首先利用近红外光谱仪对PET、PE、PP、PVC、PLA和PBS等典型塑料包装材料进行光谱数据采集。采用Savitzky-Golay平滑、标准正态变量变换（SNV）及多元散射校正（MSC）多步策略对原始光谱进行预处理,以消除噪声与散射干扰。在此基础上,构建了一种基于多尺度Mamba（Multi-scale mamba,MS-Mamba）的分类模型,该模型利用状态空间模型（SSM）的线性复杂度优势,通过多尺度卷积支路与门控融合机制,同时捕捉光谱序列的局部纹理特征与全局长程依赖。为验证模型性能,将所提方法与传统机器学习模型（PCA-SVM、PLS-DA）及深度学习模型（LSTM、Transformer等）进行对比,并以准确率、精确率、召回率和F1分数作为评价指标。结果 实验结果表明,MS-Mamba模型在分类精度与稳定性方面均显著优于传统机器学习（PCA-SVM,PLS-DA）及主流深度学习模型（ResNet,Transformer）。其中测试集准确率、精确率、召回率和F1分数分别达到99.91%、99.89%、99.88%以及99.88,且在区分PE与PP等高度相似材料时表现出极高的鲁棒性。结论 本文方法能够实现塑料包装材料的快速、无损和高精度识别,为包装材料的自动化检测与绿色回收提供了一种可行的技术路径。
Abstract
The work aims to explore the potential of combining near-infrared (NIR) spectroscopy with a deep learning model for rapid material identification, to address the limitations of traditional detection methods for plastic packaging materials, such as low efficiency, high human dependency, and difficulty in distinguishing visually similar materials with subtle compositional differences. NIR spectral data were collected from representative plastic packaging materials, including PET, PE, PP, PVC, PLA, and PBS. The raw spectra were preprocessed using a multi-step strategy involving Savitzky-Golay smoothing, Standard Normal Variate Transformation (SNV), and Multiplicative Scatter Correction (MSC) to eliminate noise and scattering interference. Based on this, a classification model based on Multi-Scale Mamba (MS-Mamba) was constructed. This model leveraged the linear complexity advantage of the State Space Model (SSM), employing multi-scale convolutional branches and a gated fusion mechanism to simultaneously capture local texture features and global long-range dependencies in spectral sequences. To assess performance, the proposed method was compared with traditional machine learning models (PCA-SVM and PLS-DA) and deep learning models (LSTM and Transformer), with accuracy, precision, recall, and F1-score as evaluation metrics. The experimental results demonstrated that the MS-Mamba model significantly outperformed traditional machine learning (PCA-SVM, PLS-DA) and mainstream deep learning models (ResNet, Transformer) in both classification accuracy and stability. The test set accuracy, precision, recall, and F1 score reached 99.91%, 99.89%, 99.88%, and 99.88%, respectively, while exhibiting exceptional robustness in distinguishing highly similar materials such as PE and PP. The proposed approach enables rapid, non-destructive, and highly accurate identification of plastic packaging materials, providing a feasible and effective solution for automated inspection and sustainable recycling applications.
关键词
近红外光谱 /
塑料包装分类 /
多尺度Mamba /
深度学习 /
无损识别
Key words
near-infrared spectroscopy /
plastic packaging classification /
multi-scale Mamba /
deep learning /
non-destructive identification
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基金
2024年广东省教育科学规划项目（高等教育专项）（2024GXJK1148）
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