Ecuaciones de calibración en espectroscopía de reflectancia en el infrarrojo cercano (NIRS) para predicción de parámetros nutritivos en forrajes tropicales

Abstract

Ecuaciones de calibración en espectroscopía de reflectancia en el infrarrojo cercano (NIRS) para predicción de parámetros nutritivos en forrajes tropicalesNear infrared spectroscopy (NIRS) ResumenEl estudio tuvo como objetivo desarrollar y evaluar ecuaciones de calibración para predecir los parámetros de calidad nutritiva de especies forrajeras tropicales utilizando espectroscopía de reflectancia en el infrarrojo cercano (NIRS, por su sigla en inglés). En total fueron analizadas 1,991 muestras de tejido de gramíneas y leguminosas heterogéneas por su edad de rebrote, estado fenológico y partes de las plantas recolectadas, época de muestreo y sitio o lugar de procedencia. En grupos aleatorios de muestras se realizaron análisis químicos de referencia para proteína cruda (n=315), fibra detergente neutro (n=243), fibra detergente ácido (FDA) (n=156), digestibilidad in vitro de la materia seca (n=449) y digestibilidad de fibra detergente neutro (DFDN) (n=238). Las curvas de calibración se calcularon mediante el uso de errores estándar de calibración, validación cruzada (SEVC) y predicción. La precisión de cada ecuación se calculó por el coeficiente de determinación (R 2 ) y el índice RPD (relación entre la desviación estándar y el SEVC). La validación se realizó con muestras externas resultando coeficientes de correlación (r) >0.90 para los parámetros, con excepción de FDA (r=0.72). Los resultados indican que la predicción de los parámetros de calidad nutritiva con el uso de NIRS en forrajes tropicales es confiable y rápida; no obstante la determinación de la fracción de FDA por este método analítico requiere un mayor ajuste para incrementar su confiabilidad.Palabras clave: Calidad nutritiva, gramíneas, leguminosas, métodos analíticos, nutrición animal. AbstractThe objective of this study was to develop NIRS calibration curves to predict nutritional quality parameters of tropical forage species. For this a total of 1,991 samples of tropical forages (grasses and legumes) were employed. These samples showed a high heterogeneity in regrowth age, vegetative state and parts of the plants collected, sampling period and sample origin. Chemical analysis was performed to determine crude protein, neutral detergent fiber, acid detergent fiber (ADF), in vitro digestibility of dry matter and digestibility of neutral detergent fiber. A group of samples was used for each chemical parameter to develop the calibration curves. The curves were chosen taking into account the standard errors of: calibration; cross validation (SECV); and prediction. To evaluate the accuracy of each equation the coefficient of determination (R 2 ) and the RPD (ratio performance deviation) index (relation between standard deviation and SECV), which assesses the predictive power of the equations, were calculated. Validation was performed with external samples and results showed correlation coefficients (r) of >0.90 for all parameters except for ADF (r=0.72), demonstrating the precision of the predictive equations. However, the determination of ADF by this ...

Use of near infrared spectroscopy for the evaluation of forage for ruminants

“…Near infrared spectroscopy is a very rapid technique with a low maintenance cost. It provides results at the time of analysis, which makes it a very interesting tool for animal feed control (Pujol et al, 2007) and a reliable tool in the determination of forage quality parameters (Castro, 2002;Ibáñez & Alomar, 2008;Molano et al, 2016).…”

“…With good validation equation parameters and the inclusion of new spectra, it is then possible to make a prediction of the selected parameters of new samples. To select the NIRS calibration and validation equations, they must have the highest R 2 and the lowest errors associated with the measurements in each phase (Molano et al, 2016).…”

“…Forages are the main and most important source of nutrients for ruminant livestock (Molano et al, 2016). Thus, it is important to understand and know forage nutritional values for improved business gains, because they directly influence the productive and reproductive performance of animals (Molano et al, 2016).…”

“…Forages are the main and most important source of nutrients for ruminant livestock (Molano et al, 2016). Thus, it is important to understand and know forage nutritional values for improved business gains, because they directly influence the productive and reproductive performance of animals (Molano et al, 2016). This knowledge also allows animal requirements to be met to avoid unnecessary losses in the environment (De Boever et al, 1997), as is a prediction of animal performance and the subsequent development of the livestock industry (Herrero et al, 1996).…”

“…Nutritional value estimation is generally done using classical wet chemical methods, such as those of Tilley & Terry (1963) and Goering & Van Soest (1970), to obtain nutritional information about forage (Herrero et al, 1996); however, some of these techniques are time-consuming, require skilled labor, are often expensive, use chemical reagents that in some cases may be hazardous contaminants and can be inaccurate (Herrero et al, 1996;Molano et al, 2016).…”