Uso De La Espectroscopía De Reflectancia en El Infrarrojo Cercano Para El Análisis De Calidad De Ensilaje De Maíz

Cozzolino, Daniel; Fassio, A.; Fernández, Enrique

doi:10.4067/s0365-28072003000400007

Cited by 15 publications

(24 citation statements)

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“…Según Cozzolino et al (2003) y Valenciaga y Saliba (2006) no existe un número mínimo definido de muestras para una calibración satisfactoria; no obstante es necesario evaluar productos heterogéneos con un mínimo de 100 muestras. Los colectivos de calibración desarrollados en este trabajo contenían un número superior a 150 muestras para cada parámetro con sus respectivos datos de laboratorio, lo cual sugiere una alta representatividad de las muestras usadas.…”

Section: Discussionunclassified

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

Molano

CORTÉS

Avila

et al. 2016

Trop. Grassl.-Forrajes Trop.

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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 ...

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Section: Discussionunclassified

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

Molano

CORTÉS

Avila

et al. 2016

Trop. Grassl.-Forrajes Trop.

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“…Among the models obtained, is worth mentioning the high predictive value of the TA equation, which was unexpected given that it corresponds to inorganic and mineral substances that do not have absorption in the near infrared region; although these compounds can be associated with organic structures through oxides, chelates or other compounds, which can allow for their indirect estimation using NIRS (Shenk and Westerhaus, 1994;Cozzolino et al, 2003). The TA and CP parameters had a high determination coefficient (R 2 > 0.93) reflecting the strong relationship between NIR absorption bands and the reference data (Figure 2).…”

Section: Wavelength (Nn)mentioning

confidence: 99%

“…It could be expected that in complex fractions, such as CF, NDF or ADF, NIRS would have a low predictive power given that these are empirical entities that represent complex structures of the cellular wall, while the spectral data obtained by NIRS responds with exactitude to the real chemical groups of the sample (Alomar and Fuchslocher, 1998;Cozzolino et al, 2003). The strong relationship in this study between the data predicted by NIRS and the reference data is notable, which considerably raises the predictive quality of the equations for fiber, in the case of fiber obtained by detergent techniques (NDF and ADF), with RPD > 4.46 and 1-VR > 0.96, the reliability is made more robust by the large number of samples used in calibration and validation.…”

Section: Wavelength (Nn)mentioning

confidence: 99%

“…In the determination of pH by NIRS, there is an association with the OH groups of the sample, mainly in correspondence to the moisture content (Cozzolino et al, 2003 LITERATURE CITED…”

Section: Nirs Values (%)mentioning

confidence: 99%

“…When a sample is scanned, radiant energy is absorbed selectively according to the frequency of the specific vibrations of the molecules present in the sample, resulting in a spectrum which is unique for that sample. All of the organic bonds (i.e., C-H, N-H and O-H) have absorption bands in the NIR region, while minerals can only be detected in organic compounds (Osborne, 2000;Cozzolino et al, 2003;Park et al, 2005). Consequently, NIRS can detect the bonds of fractions of proteins, fats and carbohydrates in forage.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of the Chemical Composition and Fermentation Parameters of Pasture Silage by Near Infrered Reflectance Spectroscopy (NIRS)

Ibáñez

Alomar

2008

Chilean J. Agric. Res.

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0.89 and SECV (%) of 5.14, 6.69, 9.96, 16.01 and 9.15 for A, CP, CF, NDF and ADF, respectively. NIRS showed moderate accuracy for ME, with 1-VR > 0.87, SECV: 0.07 Mcal kg-1 and low accuracy, although with feasibility as a ranking method, for pH and N-NH3, with 1-VR > 0.72 and SECV of 0.14 and 1.49, respectively. It is concluded that the equations obtained can be used to predict the nutritional composition of pasture silages.]]>

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A Review of the Application of Near-Infrared Spectroscopy for the Analysis of Potatoes

López

Arazuri

García

et al. 2013

J. Agric. Food Chem.

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Potato (Solanum tuberosum L.) is one of the most important crops in the world being considered as a staple food in many developing countries. The potato industry like other vegetable and fruit industries is subject to the current demand of quality products. In order to meet this challenge, the food industry is relying on the adoption of nondestructive and environmentally friendly techniques to determine quality of products. Near-infrared spectroscopy (NIRS) is currently one of the most advanced nondestructive technologies regarding instrumentation and application, and it also complies with the environment requirements as it does not generate emissions or waste. This paper reviews research progress on the analysis of potatoes by NIRS both in terms of determination of constituents and classification according to the different constituents of the tubers. A brief description of the fundamentals of NIRS technology and its advantages over other quality assessment techniques is included. Finally, future prospects of the development of NIRS technology at the industrial level are explored.

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Uso De La Espectroscopía De Reflectancia en El Infrarrojo Cercano Para El Análisis De Calidad De Ensilaje De Maíz

Cited by 15 publications

References 0 publications

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

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

Prediction of the Chemical Composition and Fermentation Parameters of Pasture Silage by Near Infrered Reflectance Spectroscopy (NIRS)

A Review of the Application of Near-Infrared Spectroscopy for the Analysis of Potatoes

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