摘要
针对常规方法鉴别纯纺织物及预测混纺织物组分含量工序多、耗时长且污染环境的问题,采用傅里叶变换红外光谱仪,结合衰减全反射(ATR)附件测试各纯纺及二组分混纺织物的傅里叶变换衰减全反射红外光谱(ATR-IR)图,并从已测的753个样品中筛选出正反经纬组分一致的纯纺及混纺织物样品205个,建立了涤纶/棉、涤纶/羊毛、涤纶/锦纶、蚕丝/棉和涤纶/粘胶等纯纺及混纺织物的ATR-IR谱库。并利用自建谱库的检索功能,对20个未知纤维织物样品进行快速无损鉴别及含量预测。研究表明:对于纯纺织物,识别准确率为100%;对于混纺织物,当误差≤3%时,通过T检验,其定量预测结果与国标法测定值无显著差异,方便了样品的快速检验与含量预测。
Aiming at many processes, long time-consuming and polluting the environment when identifying pure textile fabrics by conventional methods and predicting the component contented of blended fabrics, the attenuated total reflection Fourier transform infrared spectroscopy(ATR-IR) spectra of various pure spun and two-component blended fabrics were tested by using Fourier transform infrared spectrometer in combination with the attenuated total reflection (ATR) attachment, and the ATR-IR spectral library was established, which was composed of 205 pure and blended fabric samples of back-to-face as well as warp and weft with identical components. The samples comprising polyester/cotton, polyester/wool, polyester/polyamide, silk/cotton and polyester/viscose blended fabric were selected from 753 samples. 20 samples of unknown fabrics were identified and predicted by using the searching function of the self-built spectrum library, and the accuracy of recognition can reach 100% for pure spun. For blended fabric, the quantitative predicted results were not significantly different from those of national standard method by the T -test analysis in less than 3% error. The prediction greatly facilitates the rapid detection and quantification of samples.
作者
魏子涵
李文霞
杜宇君
马静雯
郑佳辉
WEI Zihan;LI Wenxia;DU Yujun;MA Jingwen;ZHENG Jiahui(School of Materials Science and Engineering,Beijing Institute of Fashion Technology,Beijing 100029,China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2019年第8期64-68,共5页
Journal of Textile Research
基金
国家重点研发计划项目(2016YFB0302900)
2016年度中国化学纤维工业协会-绿宇基金项目(HXKY05160384)
关键词
衰减全反射
傅里叶变换红外光谱
无损检测
织物组分鉴别
attenuated total reflection
Fourier transform infrared spectroscopy
nondestructive testing
identification of fabric component
