期刊文献+

棉-涤混纺面料中棉含量的近红外光谱分析 被引量:31

Quantitative analysis of cotton content in cotton-terylene textile by near infrared technique
下载PDF
导出
摘要 建立了分析精度较高的用于检测棉-涤混纺面料里含棉量的近红外光谱分析模型。选择了50个棉-涤混纺面料作为对象,自行设计采样装置,采集其近红外光谱;然后,经一阶导数、二阶导数、Savitzky-Golay滤波等方法预处理,结合偏最小二乘法,建立了4个棉成分的定量分析模型。结果表明:Savitzky-Golay滤波对定标结果几乎没有影响;经一阶导数预处理后的光谱数据结合偏最小二乘法建立的模型具有较高的分析精度,定标均方差和预测均方差分别达到了0.022和0.018,分析误差控制在±0.05以内,基本满足了纺织领域快速定量检测的精度需求。同时,还分析了近红外光谱技术在该应用领域的研究重点。 A calibration model for determining the cotton content in cotton-terylene textile precisely was established. The near infrared spectra of 50 cotton-terylene textile samples were collected using self-designed accessory, and Savitsky-Goaly filter smoothing, first order derivative and second order derivative were used to pretreat the spectra. Calibration models for cotton content were established with Partial Least Square(PLS). The experimental results indicate that Savitsky-Goaly filter smoot- hing spectral treatments can not affect the performance of the model in this case, but NIR technique can analyze the cotton content in the textile precisely. The Root Mean Square Error of Calibration (RMSEC) and Root Mean Square Error of Prediction(RMSEP) are 0. 022 and 0. 018 respectively,and the error of analysis is restricted in ±0.05. In addition, several key aspects of this application were expressed.
出处 《光学精密工程》 EI CAS CSCD 北大核心 2008年第11期2051-2054,共4页 Optics and Precision Engineering
基金 浙江省科技计划重点资助项目(No.2004C21043)
关键词 近红外光谱 纺织面料 棉含量 偏最小二乘法 预处理 near infrared spectroscopy textile cotton content Partial Least Square(PLS) method pretreatment
  • 相关文献

参考文献9

  • 1陆婉珍.现代近红外光谱分析技术[M].北京:中国石化出版社,2001.
  • 2PASQUINI C. Near infrared spectroscopy: fundamentals, practical aspects and analytical applications[J]. J. Braz. Chem. Soc., 2003,14(2):198-219.
  • 3CHALMERS M, GRIFFITHS R. Handbook of Vibrational Spectroscopy [M]. Hohn Wiley&Sons, Ltd, 2003.
  • 4O'NEIL A J, JEER D, MOFFAT A C. Measurement of the percentage volume particle size distribution of powdered microcrystalline cellulose using reflectance near-infrared speetroscopy[J]. Analyst, 2003,128:1326-1330.
  • 5SOHN M, BARTON F E, MCCLUNG A M,et al.. Near-infrared spectroscopy for determination of protein and amylose in rice flour through use of derivatives[J]. Cereal Chemistry, 2004,81(3): 341-344.
  • 6HAALAND D M, THOMAS E V. Partial least squares methods for spectral analyses [J]. Anal. Chem. , 1988, 60 (11): 1193-1217.
  • 7MCSHANE M J, COTE G L, SPIEGELMAN C H. Assessment of partial least-squares calibration and wavelength selection for complex near-infrared spectra [J]. Appl. Spectrosc. , 1998, 52 (6): 878-884.
  • 8张玲.PLS定标法在近红外光谱分析仪中的应用研究[J].光学精密工程,2000,8(3):238-241. 被引量:18
  • 9赵强,张工力,陈星旦.多元散射校正对近红外光谱分析定标模型的影响[J].光学精密工程,2005,13(1):53-58. 被引量:43

二级参考文献6

  • 1[1]Norris K H, Williams P C. Optimization of mathematical treatments of raw near-infrared signal in the measurement of protein in hard red spring wheat. Ⅰ. influence of particle size[J]. Cereal Chemistry, 1984, 61(2): 158-165.
  • 2[2]Geladi Paul, Kowalski Bruce R. Partial least-squares regression: a tutorial[J]. Analytica Chimica Acta,1986, 185: 1-17.
  • 3[3]Geladi Paul, Kowalski Bruce R. An example of 2-block predictive partial least-squares regression with simulated data[J]. Analytica Chimica Acta, 1986, 185: 19-32.
  • 4[4]Haaland David M. Thomas Edward V. Partial least-squares methods for spectral analyses. 1. relation to other quantitative calibration methods and the extraction of qualitative information[J]. Anal. Chem., 1988, 60: 1193-1202.
  • 5[5]Mcshane Michael J, Cote Gerard L, Spiegelman Clifford H. Assessment of partial least-squares calibration and wavelength selection for complex near-infrared spectra[J]. Applied Spectroscopy, 1998, 52(6): 878-884.
  • 6WILLIAMS P,NORRIS K.Near-infrared technology in the agricultural and food industries[M].

共引文献113

同被引文献229

引证文献31

二级引证文献185

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部