Rapid Textile Fibers Classification Technology Based on Near-Infrared Spectroscopy and Machine Learning

Authors

  • Xingyan Ruan
  • Li Luo
  • Xiangyang Yu
  • Yefan Cai
  • Weibin Hong

DOI:

https://doi.org/10.54097/qhhqr929

Keywords:

Near-infrared Spectroscopy, Machine Learning, Fiber Classification, Accuracy, AUC_macro

Abstract

The accurate identification of textile materials plays a crucial role in production control, quality inspection, and market regulation. In recent years, near-infrared (NIR) spectroscopy has been widely applied in the identification and classification of textile fibers due to its rapid, non-destructive, and efficient characteristics. Therefore, this study utilizes a handheld near-infrared spectrometer and chemometrics to provide a convenient, efficient, non-destructive, and green analytical method for the qualitative analysis of textiles. The article collects spectral data of fiber samples using a handheld NIR spectrometer and analyzes them with chemometric methods. Data preprocessing techniques, feature extraction algorithms, comprehensive sampling methods, and classification algorithms are employed to establish classification models. After ten-fold cross-validation, the optimal SG_RF model is obtained, with a classification accuracy of 93.7% on the test set and an AUC_macro of 0.928, demonstrating excellent performance. The article validates the feasibility of using near-infrared spectroscopy combined with chemometric methods for textile fiber classification, offering a rapid, non-destructive, and efficient approach for textile fiber classification.

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Published

21-01-2025

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Articles

How to Cite

Ruan, X., Luo, L., Yu, X., Cai, Y., & Hong, W. (2025). Rapid Textile Fibers Classification Technology Based on Near-Infrared Spectroscopy and Machine Learning. Frontiers in Computing and Intelligent Systems, 11(1), 89-96. https://doi.org/10.54097/qhhqr929