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versión On-line ISSN 2521-9766versión impresa ISSN 1405-3195


JARQUIN-SANCHEZ, Aaron et al. Analysis of total nitrogen in tropical soils with near-infrared spectroscopy (NIRS) and chemometrics. Agrociencia [online]. 2011, vol.45, n.6, pp.653-662. ISSN 2521-9766.

Nitrogen (N) is one of the most important elements for plant nutrition; therefore, it is necessary to obtain quick and reliable methods for determining N in soil. The objective of the present study was predicting total nitrogen (Nt) concentration in soils of a Mexican tropical region by means of near-infrared spectroscopy (NIRS) in samples within a plastic bag or without one. The advantage of using NIRS lies in the great selectivity of the technique, which makes it possible to quantify a chemical element in a complex mixture without previous work of separation; besides, it allows evaluating Nt amount in few minutes in different soil samples. In the humid tropic of Tabasco, Mexico, 156 soil samples with contrasting chemical characteristics were selected. The samples were dried, ground, and sieved through a 0.5 mm mesh screen, and the Nt concentration was determined by Kjeldahl method. The samples were packed in a plastic bag and Nt was measured in a NIRS spectrophotometer, model FOSS 5000 (NIR systems). The samples were analyzed with and without bag at a level of 1100 to 2000 nm in order to obtain the models which allow assessing Nt amount in soil. The models generated with and without bag were compared in order to recognize whether the interference of the bag can be corrected with the mathematical models and measurements could be made more easily. The models generated for analysis with and without bag explain 92 and 89 % of the variation. Based on this information, it can be concluded that Nt determination with bag is reliable, more rapid, and easier than that in soil without bag, since we can eliminate cleaning the optical fiber of the reading module between one and the next sample.

Palabras llave : chemical analysis; soil; optical fiber; model; nitrogen prediction.

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