Investigation of the Impact of Instrumental and Software Applications on Cotton and Botanical Trash Identification by Ultraviolet-Visible and Near-Infrared Spectroscopy

被引:0
|
作者
Fortier, Chanel [1 ]
Rodgers, James [1 ]
Foulk, Jonn [2 ]
机构
[1] USDA ARS, Southern Reg Res Ctr, Cotton Struct & Qual, 1100 Robert E Lee Blvd, New Orleans, LA 70124 USA
[2] USDA ARS, Cotton Qual Res Lab, Clemson, SC 29633 USA
来源
JOURNAL OF COTTON SCIENCE | 2011年 / 15卷 / 02期
关键词
D O I
暂无
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
Given the worldwide production, usage, and manufacturing of cotton, protocols that could identify botanical cotton trash components that can become comingled with cotton could be advantageous for quality assessment prior to spinning. Conventional methods such as the High Volume Instrument (HVITM) or Shirley Analyzer do not classify or yield specific trash component information. A program was implemented 1) to determine the efficacy of the ultraviolet-visible (UV-Vis) spectroscopy technique to identify cotton trash types and 2) to compare these identification results to the results of the Fourier-transform near-infrared (FT-NIR) technique to identify cotton and individual cotton trash components. Chemometric routines involve preprocessing methods and evaluation of specific spectral wavelengths to enhance spectral differences among individual pure samples of cotton trash components and cotton fiber. The chemometric software package, Unscrambler, afforded a 67% correct botanical trash identification. The utility of this method to correctly identify cotton fiber and cotton trash components was compared to the FT-NIR spectrometer. Overall, a higher percentage of correct identifications (98%) were observed using the FT-NIR spectrometer coupled with the OPUS IDENT software package. When comparing OPUS IDENT and Unscrambler software packages for sample identification by uploading NIR data into Unscrambler, the FTNIR identification results with Unscrambler were significantly superior to the UV-Vis identification results. Thus, the FT-NIR technique proved to be a better technique than the UV-Vis technique at identifying cotton trash types.
引用
收藏
页码:170 / 178
页数:9
相关论文
共 50 条
  • [21] Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
    Xiao, Qinlin
    Wu, Na
    Tang, Wentan
    Zhang, Chu
    Feng, Lei
    Zhou, Lei
    Shen, Jianxun
    Zhang, Ze
    Gao, Pan
    He, Yong
    FRONTIERS IN PLANT SCIENCE, 2022, 13
  • [22] An Ultraviolet-Visible and Near-Infrared-Responded Broadband NIR Phosphor and Its NIR Spectroscopy Application
    Zhou, Xufeng
    Geng, Wanying
    Li, Junyi
    Wang, Yichao
    Ding, Jianyan
    Wang, Yuhua
    ADVANCED OPTICAL MATERIALS, 2020, 8 (08)
  • [23] THE FURTHER INVESTIGATION OF TANNING MECHANISMS OF TYPICAL TANNAGES BY ULTRAVIOLET-VISIBLE AND NEAR INFRARED DIFFUSED REFLECTANCE SPECTROPHOTOMETRY
    Guo, Junling
    Huang, Xin
    Wu, Chao
    Liao, Xuepin
    Shi, Bi
    JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION, 2011, 106 (07): : 226 - 231
  • [24] Identification of rough rice species and years by visible/near-infrared reflectance spectroscopy
    Shao, Yongni
    He, Yong
    Cao, Fang
    2006 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, PTS 1 AND 2, PROCEEDINGS, 2006, : 988 - 991
  • [25] VARIETY IDENTIFICATION OF CHINESE CABBAGE SEEDS USING VISIBLE AND NEAR-INFRARED SPECTROSCOPY
    Wu, D.
    Feng, L.
    He, Y.
    Bao, Y.
    TRANSACTIONS OF THE ASABE, 2008, 51 (06) : 2193 - 2199
  • [26] Variety identification of Chinese cabbage seeds using visible and near-infrared spectroscopy
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
    不详
    Trans. ASABE, 2008, 6 (2193-2199):
  • [27] The Rapid Detection of Trash Content in Seed Cotton Using Near-Infrared Spectroscopy Combined with Characteristic Wavelength Selection
    Han, Jing
    Guo, Junxian
    Zhang, Zhenzhen
    Yang, Xiao
    Shi, Yong
    Zhou, Jun
    AGRICULTURE-BASEL, 2023, 13 (10):
  • [28] On the use of the fluorescence, ultraviolet-visible and near infrared spectroscopy with chemometrics for the discrimination between plum brandies of different varietal origins
    Jakubikova, M.
    Sadecka, J.
    Kleinova, A.
    FOOD CHEMISTRY, 2018, 239 : 889 - 897
  • [29] Rice Freshness Identification Based on Visible Near-Infrared Spectroscopy and Colorimetric Sensor Array
    Lin, Hao
    Jiang, Hao
    Lin, Jinjin
    Chen, Quansheng
    Ali, Shujat
    Teng, Shyh Wei
    Zuo, Min
    FOOD ANALYTICAL METHODS, 2021, 14 (07) : 1305 - 1314
  • [30] Rice Freshness Identification Based on Visible Near-Infrared Spectroscopy and Colorimetric Sensor Array
    Hao Lin
    Hao Jiang
    Jinjin Lin
    Quansheng Chen
    Shujat Ali
    Shyh Wei Teng
    Min Zuo
    Food Analytical Methods, 2021, 14 : 1305 - 1314