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

Paz, Camila Carvalho da; Silva, André Guimarães Maciel e; Rêgo, Aníbal Coutinho do

doi:10.22491/rca.2019.2923

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Also referred to as a rapid decision tool, non‐destructive, and valid alternative technique, which represents a radical shift from conventional chemical methods (Li et al., 2016; Lobos, Gou, Hube, Saldaña, & Alfaro, 2013). The NIRS has enabled livestock producers and forage researchers to quickly and cheaply estimate forage nutrient composition (Johnson et al., 2017; Paz, Silva, & Rêgo, 2019).…”

Section: Introductionmentioning

confidence: 99%

Using near infrared reflectance spectroscopy for estimating nutritional quality of Brachiaria humidicola in breeding selections

Mazabel

Worthington

Peters

et al. 2020

Agrosystems Geosci & Env

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Brachiaria humidicola (BH) (syn. Urochloa humidicola) is an important forage grass in the tropics due to its capacity to grow in nutrient-deficient soils, tolerate waterlogging, and inhibit soil nitrification. A major objective of BH breeding is to improve its nutritional quality. Therefore, a rapid and low-cost method is needed to assess main quality parameters such as neutral detergent fiber (NDF), acid detergent fiber (ADF), in vitro dry matter digestibility (IVDMD), and crude protein (CP). This study developed models using near infrared reflectance spectroscopy (NIRS) to predict concentrations of these parameters toward breeding. Samples were collected from BH trials located in different regions of Colombia, scanned for NIRS (400-2,500 nm), analyzed with wet chemistry as reference values, and used to build the chemometric models. Results from wet chemistry showed wide variability in terms of dry matter percentage for NDF (51.6-76.2%), ADF (26.1-46.1%), IVDMD (41.5-78.3%), and CP (2.8-12.8%). The NIRS models were validated using an independent set of samples and have coefficients of determination (R 2) and one minus the variance ratio (1-VR) values in the range of .9 and .95, suggesting a good correlation between reference-lab and NIRSpredicted values. The standard errors of cross validation (SECV) for IVDM, NDF, ADF, and CP were 1.59, 1.18, 0.74, and 0.53%, respectively. Prediction efficiency (ratio of performance to standard deviation, RPD) for all parameters was above 3.0, except for CP (2.6). Calibrations obtained present an adequate adjustment and predictive tendency, making them suitable for selection and BH breeding.

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Section: Introductionmentioning

confidence: 99%

Using near infrared reflectance spectroscopy for estimating nutritional quality of Brachiaria humidicola in breeding selections

Mazabel

Worthington

Peters

et al. 2020

Agrosystems Geosci & Env

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Prediction performance of portable near infrared reflectance instruments using preprocessed dried, ground forage samples

Berzaghi¹,

Cherney

Casler

2021

Computers and Electronics in Agriculture

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Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuff

Rukundo¹,

Danao²,

MacDonald³

et al. 2021

AIMSAGRI

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<abstract> <p>Two handheld near infrared (NIR) spectrometers were used to quantify crude protein ($CP$) content of mixed forage and feedstuff composed of Sweet Bran, distiller's grains, corn silage, and corn stalk. First was a transportable spectrometer, which measured in the visible and NIR ranges (320–2500 nm) with a spectral interval of 1 nm (H1). Second was a smartphone spectrometer, which measured from 900–1700 nm with a spectral interval of 4 nm (H2). Spectral data of 147 forage and feed samples were collected by both handheld instruments and split into calibration ($n$ = 120) and validation ($n$ = 27) sets. For H1, only absorbances in the NIR region (780–2500 nm) were used in the multivariate analyses, while for H2, absorbances in the second and third overtone regions (940–1660 nm) were used. Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using mean-centered data that had been preprocessed using standard normal variate (SNV) or Savitzky-Golay first derivative (SG1) or second derivative (SG2) algorithm. PCA models showed two major groups—one with Sweet Bran and distillers grains, and the other with corn silage and corn stalk. Using H1 spectra, the PLS regression model that best predicted $CP$ followed SG1 preprocessing. This model had low root mean square error of prediction ($RMSEP$ = 2.22%) and high ratio of prediction to deviation ($RPD$ = 5.24). With H2 spectra, the model best predicting $CP$ was based on SG2 preprocessing, returning $RMSEP$ = 2.05% and $RPD$ = 5.74. These values were not practically different than those of H1, indicating similar performance of the two devices despite having absorbance measurements only in the second and third overtone regions with H2. The result of this study showed that both handheld NIR instruments can accurately measure forage and feed $CP$ during screening, quality, and process control applications.</p> </abstract>

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

Cited by 3 publications

References 32 publications

Using near infrared reflectance spectroscopy for estimating nutritional quality of Brachiaria humidicola in breeding selections

Using near infrared reflectance spectroscopy for estimating nutritional quality of Brachiaria humidicola in breeding selections

Prediction performance of portable near infrared reflectance instruments using preprocessed dried, ground forage samples

Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuff

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