Ash and mineral contents in leaves of woody species: Analysis by near-infrared reflectance spectroscopy
Near-infrared reflectance spectroscopy (NIRS) was evaluated for its effectiveness to determine ash and mineral concentrations [potassium (K), magnesium (Mg), copper (Cu), iron (Fe), and zinc (Zn)], in a total of 182 leaf samples of 17 woody species located in the central-western region of the Iberian Peninsula. Chemical analysis revealed great variability in all leaf mineral elements. This variability was mainly related to differences in leaf habit (deciduous versus evergreen) and to differences in mean leaf longevity and among leaf age classes within evergreen species. A set of samples including all 17 species and leaf age classes was used to develop the calibration equations using multiple linear regression (MLR) and partial-least squares regression (PLSR). The set of samples that did not enter in the calibration was used for external validation. In general, the most satisfactory results were obtained using PLSR and derivative transformations. Despite the strong heterogeneity of the samples included in the study, the results showed that NIRS can be employed as an effective tool, alternative to the more time-consuming standard methods. The best predictive model was obtained for ash content. Models with acceptable accuracy were obtained in the prediction of K and Mg contents. However, their applicability for the determination of trace elements was more limited.
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Published: |
Taylor & Francis
2008
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| Subjects: | Ash and mineral composition, Leaf tissue, Near-infrared reflectance spectroscopy (NIRS), Woody species, |
| Online Access: | http://hdl.handle.net/10261/258797 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003176 |
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| Summary: | Near-infrared reflectance spectroscopy (NIRS) was evaluated for its effectiveness to determine ash and mineral concentrations [potassium (K), magnesium (Mg), copper (Cu), iron (Fe), and zinc (Zn)], in a total of 182 leaf samples of 17 woody species located in the central-western region of the Iberian Peninsula. Chemical analysis revealed great variability in all leaf mineral elements. This variability was mainly related to differences in leaf habit (deciduous versus evergreen) and to differences in mean leaf longevity and among leaf age classes within evergreen species. A set of samples including all 17 species and leaf age classes was used to develop the calibration equations using multiple linear regression (MLR) and partial-least squares regression (PLSR). The set of samples that did not enter in the calibration was used for external validation. In general, the most satisfactory results were obtained using PLSR and derivative transformations. Despite the strong heterogeneity of the samples included in the study, the results showed that NIRS can be employed as an effective tool, alternative to the more time-consuming standard methods. The best predictive model was obtained for ash content. Models with acceptable accuracy were obtained in the prediction of K and Mg contents. However, their applicability for the determination of trace elements was more limited. |
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