Journal of Spectral Imaging,   Volume 7   Article ID a9   (2018)

Peer reviewed Paper

Quantification of leghaemoglobin content in pea nodules based on near infrared hyperspectral imaging spectroscopy and chemometrics

  • Damien Eylenbosch
  • Benjamin Dumont
  • Vincent Baeten
  • Bernard Bodson
  • Pierre Delaplace
  • Juan Antonio Fernández Pierna
Dpt AgrobioChem, Gembloux Agro-Bio Tech Faculty, University of Liège, Belgium
These authors share senior authorship

 https://orcid.org/0000-0002-7294-2748
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Walloon Agricultural Research Centre, Valorisation of Agricultural Products Department, Food and Feed Quality Unit, Belgium

 https://orcid.org/0000-0003-4342-333X
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Dpt AgrobioChem, Gembloux Agro-Bio Tech Faculty, University of Liège, Belgium

 https://orcid.org/0000-0002-2968-8086
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Terra Teaching and Research Center, Gembloux Agro-Bio Tech Faculty, University of Liège, Belgium

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Walloon Agricultural Research Centre, Valorisation of Agricultural Products Department, Food and Feed Quality Unit, Belgium

 https://orcid.org/0000-0003-4953-8402
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 Corresponding Author
Dpt AgrobioChem and Dpt Terra Teaching and Research Center, Gembloux Agro-Bio Tech Faculty, University of Liège, Belgium
These authors share senior authorship
[email protected]
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Leghaemoglobin content in nodules is closely related to the amount of nitrogen fixed by the legume–rhizobium symbiosis. It is, therefore, commonly measured in order to assess the effect of growth-promoting parameters such as fertilisation on the symbiotic nitrogen fixation efficiency of legumes. The cyanmethaemoglobin method is a reference method in leghaemoglobin content quantification, but this method is time-consuming, requires accurate and careful technical operations and uses cyanide, a toxic reagent. As a quicker, simpler and non-destructive alternative, a method based on near infrared (NIR) hyperspectral imaging was tested to quantify leghaemoglobin in dried nodules. Two approaches were evaluated: (i) the partial least squares (PLS) approach was applied to the full spectrum acquired with the hyperspectral device and (ii) the potential of multispectral imaging was also tested through the preselection of the most relevant wavelengths and the building of a multiple linear regression model. The PLS approach was tested on mean spectra acquired from samples containing several nodules and acquired separately from individual nodules. Peas (Pisum sativum L.) were cultivated in a greenhouse. The nodules were harvested on four different dates in order to obtain variations in leghaemoglobin content. The leghaemoglobin content measured with the cyanmethaemoglobin method in fresh nodules ranged between 1.4 and 4.2 mg leghaemoglobin g–1 fresh nodule. A PLS regression model was calibrated on leghaemoglobin content measured with the reference method and mean NIR spectra of dried nodules acquired with a hyperspectral imaging device. On a validation dataset, the PLS model predicted the leghaemoglobin content in nodule samples well (R2 = 0.90, root mean square error of prediction = 0.26). The multispectral approach showed similar performance. Applied to individual nodules, the PLS model highlighted a wide variability of leghaemoglobin content in nodules harvested from the same plant. These results show that NIR hyperspectral imaging could be used as a rapid and safe method to quantify leghaemoglobin in pea nodules.

Keywords: cyanmethaemoglobin, hyperspectral, imaging, leghaemoglobin, multispectral, near infrared, pea, PLS regression, quantification, spectroscopy


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