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Determination of plant silicon content with near infrared reflectance spectroscopy.
Smis, Adriaan; Ancin Murguzur, Francisco Javier; Struyf, Eric; Soininen, Eeva M; Herranz Jusdado, Juan G; Meire, Patrick; Bråthen, Kari Anne.
Afiliação
  • Smis A; Ecosystem Management Research Group, University of Antwerp Antwerp, Belgium ; Department of Arctic and Marine Biology, UiT - The Arctic University of Norway Tromsø, Norway.
  • Ancin Murguzur FJ; Department of Arctic and Marine Biology, UiT - The Arctic University of Norway Tromsø, Norway.
  • Struyf E; Ecosystem Management Research Group, University of Antwerp Antwerp, Belgium.
  • Soininen EM; Department of Arctic and Marine Biology, UiT - The Arctic University of Norway Tromsø, Norway.
  • Herranz Jusdado JG; Department of Arctic and Marine Biology, UiT - The Arctic University of Norway Tromsø, Norway.
  • Meire P; Ecosystem Management Research Group, University of Antwerp Antwerp, Belgium.
  • Bråthen KA; Department of Arctic and Marine Biology, UiT - The Arctic University of Norway Tromsø, Norway.
Front Plant Sci ; 5: 496, 2014.
Article em En | MEDLINE | ID: mdl-25309567
ABSTRACT
Silicon (Si) is one of the most common elements in the earth bedrock, and its continental cycle is strongly biologically controlled. Yet, research on the biogeochemical cycle of Si in ecosystems is hampered by the time and cost associated with the currently used chemical analysis methods. Here, we assessed the suitability of Near Infrared Reflectance Spectroscopy (NIRS) for measuring Si content in plant tissues. NIR spectra depend on the characteristics of the present bonds between H and N, C and O, which can be calibrated against concentrations of various compounds. Because Si in plants always occurs as hydrated condensates of orthosilicic acid (Si(OH)4), linked to organic biomolecules, we hypothesized that NIRS is suitable for measuring Si content in plants across a range of plant species. We based our testing on 442 samples of 29 plant species belonging to a range of growth forms. We calibrated the NIRS method against a well-established plant Si analysis method by using partial least-squares regression. Si concentrations ranged from detection limit (0.24 ppmSi) to 7.8% Si on dry weight and were well predicted by NIRS. The model fit with validation data was good across all plant species (n = 141, R (2) = 0.90, RMSEP = 0.24), but improved when only graminoids were modeled (n = 66, R (2) = 0.95, RMSEP = 0.10). A species specific model for the grass Deschampsia cespitosa showed even slightly better results than the model for all graminoids (n = 16, R (2) = 0.93, RMSEP = 0.015). We show for the first time that NIRS is applicable for determining plant Si concentration across a range of plant species and growth forms, and represents a time- and cost-effective alternative to the chemical Si analysis methods. As NIRS can be applied concurrently to a range of plant organic constituents, it opens up unprecedented research possibilities for studying interrelations between Si and other plant compounds in vegetation, and for addressing the role of Si in ecosystems across a range of Si research domains.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2014 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2014 Tipo de documento: Article