Classification of Kraft Paper Bags Using DRS-PCA Deep Forest Architecture

JIANG Hong; YANG Qiyu; ZHANG Xinyi

doi:10.19554/j.cnki.1001-3563.2025.17.027

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Packaging Engineering ›› 2025, Vol. 46 ›› Issue (17) : 265-270. DOI: 10.19554/j.cnki.1001-3563.2025.17.027

Automatic and Intelligent Technology

Classification of Kraft Paper Bags Using DRS-PCA Deep Forest Architecture

JIANG Hong^1,2, YANG Qiyu², ZHANG Xinyi³

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Abstract

The work aims to solve the problems of strong fluorescence interference, high data dimensionality, and insufficient model generalization ability in the classification of kraft paper bags. A three-level joint classification framework based on "DRS-PCA-Deep Forest" was built. Firstly, differential Raman spectroscopy technology was used to collect spectral data of 54 kraft paper bags samples, which were then classified into six categories according to the filler composition. Secondly, principal component analysis was used to reduce the original 1 912 dimensional spectral data to 13 dimensions, effectively reducing data complexity. Under the conditions of hierarchical division of training and validation sets, the model accuracy reached 93.2%, and the weighted F₁ value was 0.932, significantly better than traditional methods such as SVM and random forest. Meanwhile, the noise experiment verified that the model could still maintain a high accuracy rate in the presence of noise interference. This method provides an efficient and non-destructive solution for kraft paper bag classification and can be extended to the judiciary. It can also be used in the fields of physical evidence tracing and waste paper recycling.

Key words

differential Raman spectroscopy / kraft paper bag / principal component analysis / deep forest model

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JIANG Hong, YANG Qiyu, ZHANG Xinyi. Classification of Kraft Paper Bags Using DRS-PCA Deep Forest Architecture[J]. Packaging Engineering. 2025, 46(17): 265-270 https://doi.org/10.19554/j.cnki.1001-3563.2025.17.027

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