Determinación de nitrógeno foliar en palma de aceite con espectroscopía en el infrarrojo medio (mir) y cercano (nir) por el método de regresión de mínimos cuadrados parciales de componentes principales  (pls).

Jhoan Jose Crespo Gonzalez; Orlando Simon Ruiz Villadiego; Karen Stefanie Ospino Villalba

doi:10.22490/21456453.3206

Vol. 11 No. 2 (2020)

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2020-07-07

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Crespo Gonzalez, J. J., Ruiz Villadiego, O. S., & Ospino Villalba, K. S. (2020). Determination of foliar nitrogen in palm of oil with spectroscopy in the middle infrared (MIR) and near (NIR) by the regression method of minimal partial squares of main components (PLS). Revista De Investigación Agraria Y Ambiental, 11(2), 43-57. https://doi.org/10.22490/21456453.3206

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Determination of foliar nitrogen in palm of oil with spectroscopy in the middle infrared (MIR) and near (NIR) by the regression method of minimal partial squares of main components (PLS).

DOI: https://doi.org/10.22490/21456453.3206

Section

Área Agrícola

Jhoan Jose Crespo Gonzalez Universidad Nacional de Colombia

Orlando Simon Ruiz Villadiego Universidad Nacional de Colombia

Karen Stefanie Ospino Villalba Universidad Nacional de Colombia

Contextualization: The determination of foliar nitrogen is one of the indexes that measures the nutritional need of the plant in oil palm crops. It is also of equal importance in this research to focus on a general trend in laboratories called green chemistry, which focuses on minimizing the use of chemical reagents in different laboratory analyzes.

Knowledge gap: Using medium and near infrared spectroscopy (MIR and NIR), the intention was to greatly minimize the generation of contaminants produced by the Kjeldahl method, in addition to reducing analysis times.

Objectives: Determine the amount of foliar nitrogen by constructing predictive models from the mid and near infrared spectra for the determination of foliar nitrogen using Kjeldahl as a reference method.

Methodology: In the development of the experiment, 198 palm leaf samples were analyzed and their respective MIR and NIR infrared spectra were taken. Each of the spectra was pretreated by different mathematical methods to correct for scattering effects of radiation. In total, 8 pretreatments were performed on each of the spectra, including the raw spectra. These were taken to choose the best prediction model for both NIR and MIR spectra.

Results and conclusions: Using the SNV pre-treatment in the model, an RMSE of 0.265 and an R² of 0.51 were obtained for the near-infrared and for the mid-infrared, the model formed with the absorbance of the untreated spectra yielded RMSE values of 0.245 and an R² of 0.46. Although it can be used in a general way as a prediction model, anomalous points can be observed that increase the error and decrease the R2, from these data the need to classify the groups of foliar samples in a better way and if it is it is necessary to make prediction models for each of the groups.

near infrared spectrophotometry (NIR), medium infrared spectrophotometry (MIR), Foliar nitrogen analysis, oil palm, partial least squares regression (PLS), Chemometric

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Determination of foliar nitrogen in palm of oil with spectroscopy in the middle infrared (MIR) and near (NIR) by the regression method of minimal partial squares of main components (PLS).

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