RESUMEN

A novel method for the rapid identification of hemp fibers is proposed in this paper, utilizing terahertz time-domain spectroscopy (THz-TDS) combined with the LargeVis (LV) dimensionality reduction technique. This approach takes advantage of the strengths of THz-TDS while enhancing classification accuracy through LV. To verify the efficacy of this method, terahertz absorption spectral data from three types of hemp fibers were processed. The THz absorption spectra were initially preprocessed using Hanning filtering. Following this, the filtered data underwent dimensionality reduction through three distinct methods: linear Principal Component Analysis (PCA), nonlinear t-Distributed Stochastic Neighbor Embedding (t-SNE), and the LV method. This sequence of steps resulted in a two-dimensional feature data matrix derived from the THz source spectral data. The resultant feature data matrices were then input into both K-Nearest Neighbors (KNN) and Decision Tree (DT) classifiers for analysis. The classification accuracies of six models were evaluated, revealing that the LV-KNN model achieved a 86.67% accuracy rate for the three hemp fiber types. Impressively, the LV-DT model achieved a perfect 100.00% accuracy rate for the same fibers. The LV-DT model, when integrated with THz spectroscopy technology, offers a quick and precise method for identifying various types of hemp fibers. This development introduces an innovative optical measurement scheme for the characterization of textile materials.