Abstract: A rapid and efficient method of soldering iron cauterization coupled with rapid evaporative ionization mass spectrometry (SIC-REIMS) was developed for fast acquisition of mass spectrometric data from textile samples. This approach utilized a heated electric soldering iron to directly cauterize the sample surface, generating smoke plumes that were simultaneously analyzed by a REIMS system. The method required no sample pretreatment, making it straightforward and time-efficient, with each data acquisition cycle completed within just 4-5 s. The performance of SIC-REIMS was optimized by adjusting key operational parameters, the cone voltage was set to 50 V, the heating bias voltage to 60 V, the auxiliary solvent flow rate to 200 μL/min, and the soldering iron temperature to 450 ℃. These optimized conditions ensured stable and reproducible mass spectrometric signals, which met reproducibility standards for MS analysis. Mass spectrometric data were collected from 39 authenticated textile samples spanning seven fiber categories by SIC-REIMS, including cotton, silk, wool, polyester, polyamide, spandex, and acrylic. The resulting dataset, comprising 359 mass spectra and 4 500 variables (m/z values), was processed with the LiveID software to develop a principal component analysis-linear discriminant analysis (PCA-LDA) model for classifying textile fiber compositions. The PCA-LDA model undergoing five-fold cross-validation achieves a misclassification rate of 2.23%. It exhibits exceptional classification performance for various fiber types, accuracy, precision, recall, and F1 scores exceed 99% for cotton, silk, polyester, and polyamide. For wool, these metrics are not less than 90%, and for spandex and acrylic, they are over 75%. This accuracy makes the method suited for rapid and reliable identification of textile fiber, addressing the needs of rapid quality assessments. Feature importance analysis of the PCA-LDA model combined with Progenesis QI screening identifies 29 characteristic fragment ions specific to the seven fiber categories, including seven ions from cotton, four from silk, three from wool, eight from polyester, five from polyamide, and one each from spandex and acrylic. These characteristic ions provide critical chemical markers for further understanding and classification of textile fibers. The trained PCA-LDA model was subsequently applied to analyze 20 textile samples obtained from the market or online. Using LiveID's offline recognition mode, the predicted results aligned with both the claimed fiber compositions and manual identification results. Overall, the SIC-REIMS method offers a rapid, accurate, and technically advanced solution for textile authentication and quality evaluation, serving as a valuable reference for the authentication and quality control of clothing and textile products.