Rapid Diagnosis of Nutrient Elements in Fingered Citron Leaf Using Near Infrared Reflectance Spectroscopy

Abstract

2 In order to provide references for leaf nutrition diagnosis of fingered citron, the technique of near infrared reflectance spectroscopy (NIRS) was introduced to analyze nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) in the dry-leaf samples of fingered citron. The best calibration model for N was developed with high RSQ CAL (0.90), SD/SECV (2.73) and low SEC (1.06 mg g −1 ), good calibration models were obtained for P, K, Fe and Mn, and no significant correlations were found between the spectra and the individual amounts of Zn and Cu. When tested using a validation set (n = 38), N was well predicted with low values of SEP (1.21 mg g −1 ) and high RPD (2.5). The values of SEP and RPD were also acceptable for the external validation of P, Fe and Mn. Near-infrared spectroscopy analysis technique shows potential of diagnosing minerals in fingered citron, particularly for N, P, Fe and Mn.

Recent developments in fast spectroscopy for plant mineral analysis

“…Several authors find that calibrations for micronutrients generally perform poorer than calibrations for macronutrients ( Petisco et al, 2005 , 2008 ; Huang et al, 2009 ; Liao et al, 2012 ). This is supported by Table 3 , where RPD values above 2 are almost exclusively found for macronutrient calibrations.…”

Análisis del contenido en minerales en nabizas y grelos (Brassica rapaL. var.rapa) mediante reflectancia en el infrarrojo cercano

“…Los valores de RPD vc de la validación cruzada para cada mineral varían entre 3,47 para el Ca y 1,73 para el Cu (Tabla 3). En algunos casos (Ca, Mg), estos valores superan a los descritos en la bibliografía para los mismos minerales contenidos en otras matrices vegetales: en alfalfa Ca = 3,10 (González et al, 2007); en hojas de naranja Ca = 1,5 y Mg = 2 (Menesatti et al, 2010); en Eruca vesicaria Ca = 0,78 y Mg = 1,1 (Villatoro, Moreno, et al, 2012) y en tomate Ca = 2,73 y Mg = 2,51 (García et al, 2012); y en otros minerales los valores obtenidos en este trabajo también superan a otros descritos en cidra: K = 1,2, P = 1,4 y Fe = 1,7 (Liao et al, 2012). Merece la pena destacar el RPD vc obtenido para la ecuación de calibración del Ca (3,47) ya que está considerado un micronutriente esencial que, en el norte de España y Portugal, es aportado a la dieta fundamentalmente a través del consumo de plantas de Brassica como las nabizas y Tabla 1.…”

“…Aunque los minerales no poseen bandas de absorción en la región del infrarrojo pueden estar asociados a estructuras orgánicas en forma de óxidos, quelatos u otras moléculas que permitan poder ser estimados mediante ecuaciones de calibración desarrolladas para el NIRS, como se ha comprobado en trabajos publicados previamente en otras matrices vegetales (Cozzolino & Moron, 2004;García et al, 2012;González, Hernández, & González, 2007;Liao, Wu, Chen, Guo, & Shi, 2012;Martínez et al, 2014;Menesatti et al, 2010;Plans, Simó, Casañas, & Sabaté, 2012;Villatoro, Moreno, et al, 2012).…”

PHYLIS: A Low-Cost Portable Visible Range Spectrometer for Soil and Plants

“…Ref. [ 16 ] used NIR to estimate the nutrient content of plant leaves and found varying degrees of accuracy: N could be estimated well; P, Fe and Mn were estimated moderately well; and K, Zn and Cu were poorly estimated.…”

“…[ 22 ] showed that different wavelength ranges can contribute information on soil total carbon estimates, and Ref. [ 23 ] demonstrated that visible wavelength spectroscopy (350–700 nm) could be used to estimate N, P and K in paddy soils, although they also showed that NIR was more effective for estimating N and K. This differs from the work of [ 16 ] mentioned above, which found that K was poorly estimated in plant leaves using NIR. This difference in calibration performance between plant and soil for certain characteristics is difficult to explain without a detailed comparison of the two approaches used, and highlights the fact that variation in materials and methodologies can make large differences in estimation accuracy.…”