Packaging Waste Classification Based on Hyperspectral Imaging Technology

ZHANG Peipei; WU Huajun; LI Xingzhi; LIU Siwen; ZHANG Liqing; ZHANG Yutong

doi:10.19554/j.cnki.1001-3563.2026.17.031

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (17) : 306-316. DOI: 10.19554/j.cnki.1001-3563.2026.17.031

Green Packaging and Circular Economy

Packaging Waste Classification Based on Hyperspectral Imaging Technology

ZHANG Peipei^1,2,*, WU Huajun¹, LI Xingzhi¹, LIU Siwen¹, ZHANG Liqing², ZHANG Yutong¹

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Abstract

To overcome the challenge of low classification accuracy caused by similar spectral characteristics of packaging waste materials and interference from varying surface conditions, the study employs hyperspectral imaging technology to investigate the discriminative performance and generalization stability of different classification models for typical packaging materials, breaking the limitations of single-material studies and conducting cross-material and cross-shape validation. Hyperspectral imaging technology was applied to collect spectral image data from four types of typical packaging waste with different shapes, namely paper, fabric, plastic and wood. The classification performance of three models, including Maximum Likelihood Classification (MLC), Back Propagation Neural Network (BPNN), and Support Vector Machine (SVM), was compared, and classification prediction was conducted accordingly. The first principal component contained the most of original information, and the cumulative contribution of the first two principal components reached 99.79%. All three classification models achieved valid classification of packaging waste based on the dimensionality-reduced features, among which SVM achieved the optimal performance. After post-processing optimization through Majority/Minority analysis, the overall classification accuracy reached 99.91% with a Kappa coefficient of 0.998 6. Validation with cross-shape samples demonstrated strong model generalization, suitable for sorting irregular waste. This work verifies that the hyperspectral imaging technology combined with machine learning models enables accurate identification of packaging waste, providing a technical reference for the precise and intelligent sorting of solid waste with modern technologies.

Key words

hyperspectral imaging technology / packaging waste / classification model / principal component analysis

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ZHANG Peipei, WU Huajun, LI Xingzhi, LIU Siwen, ZHANG Liqing, ZHANG Yutong. Packaging Waste Classification Based on Hyperspectral Imaging Technology[J]. Packaging Engineering. 2026, 47(17): 306-316 https://doi.org/10.19554/j.cnki.1001-3563.2026.17.031

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