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Food and Bioprocess Technology

, Volume 4, Issue 7, pp 1314–1321 | Cite as

Applying Near-Infrared Spectroscopy and Chemometrics to Determine Total Amino Acids in Herbicide-Stressed Oilseed Rape Leaves

  • Fei Liu
  • Zonglai L. Jin
  • Muhammad Shahbaz Naeem
  • Tian Tian
  • Fan Zhang
  • Yong HeEmail author
  • Hui Fang
  • Qingfu F. Ye
  • Weijun J. ZhouEmail author
Communication

Abstract

Near-infrared (NIR) spectroscopy was investigated to determine the total amino acids (TAA) in oilseed rape (Brassica napus L.) leaves under a new herbicide—propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273)—stress. In full-spectrum partial least squares (PLS) models, direct orthogonal signal correction (DOSC) was the best preprocessing method. Successive projections algorithm (SPA) was used to select the relevant variables. Multiple linear regression (MLR), PLS, and least squares-support vector machine (LS-SVM) were used for calibration. The DOSC–SPA–LS-SVM model achieved the best prediction performance with correlation coefficients r = 0.9968 and root mean squares error of prediction (RMSEP) = 0.2950 comparing all SPA–MLR, SPA–PLS, and SPA–LS-SVM models. Some parsimonious direct functions were also developed based on the DOSC–SPA wavelength (1,340 nm) such as linear, index, logarithmic, binominal, and exponential functions. The best performance was achieved by direct exponential function with r = 0.9968 and RMSEP = 0.2943. The overall results indicated that NIR was able to determine the TAA in herbicide-stressed oilseed rape leaves, and the DOSC–SPA was quite helpful for the development of detection sensors and the monitoring of the growing status and herbicide effect on field crop oilseed rape.

Keywords

Near-infrared spectroscopy Direct orthogonal signal correction Successive projections algorithm Oilseed rape Amino acids Least squares-support vector machine 

Notes

Acknowledgments

This work was supported by Zhejiang Provincial Natural Science Foundation of China (Z3090295, Y3080277), Important Zhejiang Provincial Science & Technology Specific Projects (2009C12002), the National Natural Science Foundation of China (31071332, 60802038), Natural Science Foundation of Ningbo (2009A610173), the Fundamental Research Funds for the Central Universities, and Zhejiang Innovation Program for Graduates (YK2008014).

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Copyright information

© Springer Science + Business Media, LLC 2010

Authors and Affiliations

  • Fei Liu
    • 1
  • Zonglai L. Jin
    • 2
  • Muhammad Shahbaz Naeem
    • 2
  • Tian Tian
    • 2
  • Fan Zhang
    • 2
  • Yong He
    • 1
    Email author
  • Hui Fang
    • 1
  • Qingfu F. Ye
    • 2
  • Weijun J. Zhou
    • 2
    Email author
  1. 1.College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouPeople’s Republic of China
  2. 2.College of Agriculture and BiotechnologyZhejiang UniversityHangzhouPeople’s Republic of China

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