分析化学2025，Vol.53Issue(8)：1331-1341,中插93-中插95,14.DOI:10.19756/j.issn.0253-3820.251124
微管等离子体电离质谱法结合随机森林模型快速鉴别纺织品纤维成分
Rapid Identification of Textile Fiber Composition Using Microtube Plasma Ionization Mass Spectrometry Combined with Random Forest Algorithm
摘要
Abstract
A rapid and accurate method for textile fiber identification was developed for quality control and consumer protection.This method utilized electric soldering iron burning-mesh collision enhanced microtube plasma ionization mass spectrometry(ESIB-MC-μTP-MS)to acquire textile fiber MS data and used a random forest(RF)prediction model to identify fiber composition based on these MS data.The MC-μTP device involved in the method was a homemade low-temperature plasma ionization device constructed using cost-effective and readily available components.The system was applicable for direct analysis of small amount of textile samples without any complex sample pretreatment processes.Characteristic thermal decomposition products of different fibers were generated via soldering iron burning(350℃)in ambient atmosphere,and were subsequently analyzed by a mass spectrometer,with each analysis completed within 5 s.Raw MS data underwent noise reduction,normalization,and global binning steps to form a dataset,and its intrinsic class separability was evaluated using principal component analysis(PCA)combined with k-means clustering.Then,the RF model was trained based on the dimensionality-reduced textile fiber dataset.After grid search optimization,this model demonstrated robust performance with a 0.9762 out-of-bag score,a 0.9683 cross-validation accuracy(5-fold),and a 0.9636 test accuracy,supported by precision,recall,and F1-scores exceeding 0.889 for all fiber classes.The method was applied to analysis of 30 luxury apparel samples from eight brands,among which 20 samples achieved 100%prediction confidence,aligning with labeled compositions.The identification result of two low-confidence samples was further confirmed using attenuated total reflection Fourier transform infrared spectroscopy(ATR-FT-IR).The method has been proven to be simple,portable and with minimal sample requirements for on-site customs inspections,providing a viable tool in the fight against counterfeit products,therefore supporting regulatory enforcement and consumer trust in the textile goods market.关键词
纺织品/微管等离子体电离质谱法/随机森林/纤维成分鉴别Key words
Textiles/Microtube plasma ionization mass spectrometry/Random forest/Fiber composition identification引用本文复制引用
尚宇瀚,吕悦广,孟宪双,吕庆,郭项雨,张庆..微管等离子体电离质谱法结合随机森林模型快速鉴别纺织品纤维成分[J].分析化学,2025,53(8):1331-1341,中插93-中插95,14.基金项目
国家重点研发计划项目(No.2022YFF0607204)和国家市场监督管理总局工业及消费品质量安全保障项目资助. Supported by the National Key Research and Development Program of China(No.2022YFF0607204)and the Industrial and Consumer Goods Quality Safety Assurance Project of SAMR. （No.2022YFF0607204）
