4.7 Article

Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy

Journal

ANALYTICA CHIMICA ACTA
Volume 629, Issue 1-2, Pages 56-65

Publisher

ELSEVIER
DOI: 10.1016/j.aca.2008.09.027

Keywords

Visible/near-infrared spectroscopy; Oilseed rape; Acetolactate synthase and protein content; Partial least squares analysis; Back-propagation neural network; Least squares-support vector machine

Funding

  1. National Science and Technology Support Program of China [2006BAD10A09]
  2. National Natural Science Foundation of China [30671213, 20632070]
  3. National High Technology Research and Development Program of China [2007AA10Z210, 2006AA10Z234, 2006AA10A214]
  4. Chinese Academy of Sciences [KGCX3-SYW-203-03]
  5. Natural Science Foundation of Zhejiang Province [R307095, Y506152]
  6. Teaching and Research Award Program
  7. Science and Technology Department of Zhejiang Province [2005C12029, 2008C22078]

Ask authors/readers for more resources

A new acetolactate synthase (ALS)-inhibiting herbicide, propyl 4-(2-(4,6-dimethoxy-pyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273), was applied to oilseed rape (Brassica napus L.) leaves in different leaf positions. Visible/near-infrared (Vis/NIR) spectroscopy was investigated for fast and non-destructive determination of ALS activity and protein content in rapeseed leaves. Partial least squares (PLS) analysis was the calibration method with comparison of different spectral preprocessing by Savitzky-Golay (SG) smoothing, standard normal variate (SNV), first and second derivative. The best PLS models were obtained by first-derivative spectra for ALS, whereas original spectra for soluble, non-soluble and total protein contents. Simultaneously, certain latent variables (LVs) were used as the inputs of back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) models. All LS-SVM models outperformed PLS models and BPNN models. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias in validation set by LS-SVM were 0.998, 0.715 and 0.079 for ALS, 0.999, 33.084 and 1.178 for soluble protein, 0.997, 42.773 and 6.244 for non-soluble protein, 0.999, 59.562 and 7.437 for total protein, respectively. The results indicated that Vis/NIR spectroscopy combined with LS-SVM could be successfully applied for the determination of ALS activity and protein content of rapeseed leaves. The results would be helpful for further on field analysis of using Vis/NIR spectroscopy to monitor the growing status and physiological properties of oilseed rape. (C) 2008 Elsevier B.V. All rights reserved.

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