Rapid classification of copper concentrate by portable laser-induced breakdown spectroscopy combined with transfer learning and deep convolutional neural network

Haochen Li; Tianyuan Liu; Yuchao Fu; Wanxiang Li; Meng Zhang; Xi Yang; Di Song; Jiaqi Wang; You Wang; Meizhen Huang

doi:10.3788/COL202321.043001

Journals >Chinese Optics Letters >Volume 21 >Issue 4 >Page 043001 > Article

Chinese Optics Letters
Vol. 21, Issue 4, 043001 (2023)

Rapid classification of copper concentrate by portable laser-induced breakdown spectroscopy combined with transfer learning and deep convolutional neural network

Haochen Li¹, Tianyuan Liu^2、*, Yuchao Fu¹, Wanxiang Li¹, Meng Zhang³, Xi Yang³, Di Song³, Jiaqi Wang³, You Wang³, and Meizhen Huang¹

Author Affiliations

¹Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

²Department of Electrical Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

³Southwest Institute of Technical Physics, Chengdu 610041, China

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DOI: 10.3788/COL202321.043001 Cite this Article Set citation alerts

Haochen Li, Tianyuan Liu, Yuchao Fu, Wanxiang Li, Meng Zhang, Xi Yang, Di Song, Jiaqi Wang, You Wang, Meizhen Huang. Rapid classification of copper concentrate by portable laser-induced breakdown spectroscopy combined with transfer learning and deep convolutional neural network[J]. Chinese Optics Letters, 2023, 21(4): 043001 Copy Citation Text

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Abstract

This paper investigates the combination of laser-induced breakdown spectroscopy (LIBS) and deep convolutional neural networks (CNNs) to classify copper concentrate samples using pretrained CNN models through transfer learning. Four pretrained CNN models were compared. The LIBS profiles were augmented into 2D matrices. Three transfer learning methods were tried. All the models got a high classification accuracy of

> 92 %

, with the highest at 96.2% for VGG16. These results suggested that the knowledge learned from machine vision by the CNN models can accelerate the training process and reduce the risk of overfitting. The results showed that deep CNN and transfer learning have great potential for the classification of copper concentrates by portable LIBS.

Keywords

classification convolutional neural networks flotation concentrate laser-induced breakdown spectroscopy transfer learning