• Acta Optica Sinica
  • Vol. 41, Issue 17, 1730002 (2021)
Zhuo Wei1, Wenwen Li1, Min Lin1、*, Wensong Jiang1, and Xinqi Zhou2
Author Affiliations
  • 1College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
  • 2Hangzhou Puyu Technology Inc., Hangzhou, Zhejiang 310023, China
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    DOI: 10.3788/AOS202141.1730002 Cite this Article
    Zhuo Wei, Wenwen Li, Min Lin, Wensong Jiang, Xinqi Zhou. Near-Infrared Spectroscopy Detection of Cotton/Polyester Content Based on Dropout Deep Belief Network[J]. Acta Optica Sinica, 2021, 41(17): 1730002 Copy Citation Text show less


    We combined near-infrared spectroscopy with the deep learning theory to propose a method for rapidly detecting the content of each component in cotton-polyester blended fabric based on the Dropout deep belief network (DBN). Firstly, wavelet transform was used to compress the original spectral data. Then, a DBN model with a Gaussian restricted Boltzmann machine (GRBM) as the core was constructed, which could ensure the integrity of input data. Finally, Dropout was used to effectively prevent the model from overfitting and the interdependence between nodes was reduced by hiding some hidden layer nodes. As a result, network sparsification was achieved and the ability of nonlinear modeling and network model generalization was enhanced. The experimental results indicate that in the analytical model of the content of each component in cotton-polyester blended fabric built by the Dropout-DBN method, the correlation coefficients of prediction set for cotton and polyester contents are respectively 0.9927 and 0.9903, and the root mean square errors of prediction set are 0.0792 and 0.0869, respectively. The model proposed in this paper has much higher accuracy and adaptability than other modeling methods, which is conducive to model transfer and sharing and improves the intelligentization of the model.