Prediction of ADF and NDF in faeces by NIRS to assess diet composition in grazing animals

2002 ◽  
Vol 2002 ◽  
pp. 172-172
Author(s):  
D. Cozzolino ◽  
A. La Manna ◽  
D. Vaz Martins
Keyword(s):  
Diet Composition ◽  
Near Infrared ◽  
Nutrient Content ◽  
Animal Origin ◽  
Host Animal ◽  
Near Infrared Reflectance ◽  
Herbivore Faeces ◽  
Acid Detergent Fibre ◽  
Ruminant Animals ◽  
Highly Correlated

Chemical analysis have been useful in characterising both nutrient content and digestibility of forages but less useful in predicting voluntary intake by animals (Ward et al., 1982). Faeces is the product of eroding and synthesising digestive processes and consists of residues of feed and plant tissue, component of microbial and animal origin, for this reasons faeces should contain information about the amount and characteristics of the diet. Near infrared reflectance spectroscopy (NIRS) is widely used to predict quality characteristics in forages and several reports (Lyons and Stuth, 1992; Leite and Stuth, 1995, Coates, 1999) indicated that useful prediction of dietary digestibility in grazing ruminants using faecal NIRS analysis. It is assumed for different authors that rangeland herbivore faeces contains chemical bonds resulting from undigested residues and microbial fermentation and host animal digestion end products which can provide NIRS spectral information highly correlated with dietary crude protein and digestibility (Lyons and Stuth, 1992). The objective of this work was to develop NIRS equation calibrations to estimate acid detergent fibre (ADF), neutral detergent fibre (NDF) and nitrogen in faecal samples to be used as a tool to estimate diet composition in ruminant animals under grazing conditions.

scholarly journals Analysis of the Acid Detergent Fibre Content in Turnip Greens and Turnip Tops (Brassica rapa L. Subsp. rapa) by Means of Near-Infrared Reflectance

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 364 ◽  
Author(s):  
Sara Obregón-Cano ◽  
Rafael Moreno-Rojas ◽  
Ana María Jurado-Millán ◽  
María Elena Cartea-González ◽  
Antonio De Haro-Bailón
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Mathematical Treatment ◽  
Second Derivative ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
First Derivative ◽  
Acid Detergent Fibre ◽  
Turnip Greens ◽  
Turnip Tops

Standard wet chemistry analytical techniques currently used to determine plant fibre constituents are costly, time-consuming and destructive. In this paper the potential of near-infrared reflectance spectroscopy (NIRS) to analyse the contents of acid detergent fibre (ADF) in turnip greens and turnip tops has been assessed. Three calibration equations were developed: in the equation without mathematical treatment the coefficient of determination (R2) was 0.91, in the first-derivative treatment equation R2 = 0.95 and in the second-derivative treatment R2 = 0.96. The estimation accuracy was based on RPD (the ratio between the standard deviation and the standard error of validation) and RER (the ratio between the range of ADF of the validation as a whole and the standard error of prediction) of the external validation. RPD and RER values were of 2.75 and 9.00 for the treatment without derivative, 3.41 and 11.79 with first-derivative, and 3.10 and 11.03 with second-derivative. With the acid detergent residue spectrum the wavelengths were identified and associated with the ADF contained in the sample. The results showed a great potential of NIRS for predicting ADF content in turnip greens and turnip tops.

Comparison of methods for prediction of rumen fermentation patterns from diet composition

2001 ◽  
Vol 2001 ◽  
pp. 27-27
Author(s):  
V.E. Brown ◽  
R.E. Agnew ◽  
D.J. Kilpatrick
Keyword(s):  
Diet Composition ◽  
Near Infrared ◽  
Chemical Constituents ◽  
Rumen Fermentation ◽  
Prediction System ◽  
Multiple Linear Regressions ◽  
Near Infrared Reflectance ◽  
Wet Chemistry ◽  
Wide Range ◽  
Linear Regressions

Previous attempts (Offer & Percival, 1998) have been made to develop a prediction system for rumen fermentation patterns from stepwise multiple linear regressions of the chemical constituents of the diet. These authors have also made comparisons between equations derived from diet wet chemistry and those developed from near infrared reflectance spectroscopy (NIRS). However, the potential of NIRS to predict the dynamics of rumen fermentation has not fully been explored using a wide range of forage treatments. Therefore the objective of this experiment was to develop equations from the chemical composition of the diet to predict rumen fermentation patterns and compare these with equations developed from undried and dried NIRS scans of the diets.

scholarly journals NUTRIENT CONTENT OF SOYBEAN MEAL FROM DIFFERENT ORIGINS BASED ON NEAR INFRARED REFLECTANCE SPECTROSCOPY

2020 ◽  
Vol 21 (1) ◽  
pp. 39
Author(s):  
Budi Tangendjaja
Keyword(s):  
Amino Acids ◽  
Soybean Meal ◽  
Near Infrared ◽  
Nutrient Content ◽  
Reflectance Spectroscopy ◽  
Nutrient Composition ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Different Origins

Near infrared reflectance spectroscopy (NIRS) has become common techniques to estimate chemical composition of feed ingredient for poultry. Two experiments were performed: first was to compare the capability of NIRS system from three laboratories (E, A and T) to measure nutrient composition of soybean meal (SBM); and the second was to evaluate nutrient composition and quality of 59 samples of SBM from Argentine, Brazil and US using NIRS from T-laboratory. Thirty samples of SBM was used in the first study and the result showed that all NIRS systems were able to estimate proximate, amino acids, metabolizable energy (ME) and carbohydrate components. The second experiment indicated that there were some differences in proximate composition (especially protein), total amino acids and digestible amino acids among SBM from different origins. Brazilian SBM had 2% higher protein and amino acid compared to US or Argentine SBM (P&lt;0.05). However, US SBM had slightly higher ME (20 and 40 kcal kg<sup>-1</sup>) compared to Brazilian and Argentine SBM, respectively. ME is positively correlated with protein (0.50) and fat content (0.58) but negatively correlated with fiber (-0.74) and NSP (-0.61). Stepwise regression analysis demonstrated that ME can be estimated using equation ME (kcal kg<sup>-1</sup>) = 75.7 – 21.0 x Fiber + 87.4 x Fat + 32.9 x Protein + 17.6 x NFE with reasonable accuracy (R<sup>2</sup> = 0.995). In conclusion NIRS can be used to estimate nutrient content of SBM. Brazilian SBM has higher protein and amino acids, but US SBM has slightly higher ME content.

scholarly journals The prediction of forage maize digestibility by near infrared reflection spectroscopy

1994 ◽  
Vol 42 (2) ◽  
pp. 105-113
Author(s):  
J.L. De Boever ◽  
J. Van Waes ◽  
B.G. Cottyn ◽  
C.V. Boucque
Keyword(s):  
Near Infrared ◽  
Reference Method ◽  
Near Infrared Reflectance ◽  
Reference Analysis ◽  
Forage Maize ◽  
Wide Range ◽  
Cellulose Digestibility ◽  
Highly Correlated ◽  
Infrared Reflection Spectroscopy

The potential of near infrared reflectance spectroscopy (NIRS) to predict organic matter digestibility (OMD) of fresh forage maize was examined. Cellulose digestibility, corrected to in vivo level, served as a reference method. Calibration was based on 261 samples, varying in OMD from 68.0 to 80.3% and validation occurred in 58 samples (71.7-75.9% OMD). With a scanning IA-500 monochromator the best equation, based on the second derivative of the reflected energy at wavelengths 1620 and 1664 nm, had a standard error of prediction (SEP) of 0.65%. The repeatability of the prediction amounted to 0.49% and was smaller than that of the reference analysis. The best equation, developed for a simulation of an IA-450 filter-apparatus, had a SEP of 0.74%. Cross-validation on the calibration set showed the validity of the calibrations for a wide range of digestibility. NIRS-predicted OMD was highly correlated with the reference OMD, whereas calculated OMD, based on constant digestion coefficients for the ears and stalk + leaves, did not show any relationship.

Use of near Infrared Reflectance Spectroscopy to Analyse Bovine Faecal Samples

2002 ◽  
Vol 10 (4) ◽  
pp. 309-314 ◽  
Author(s):  
D. Cozzolino ◽  
A. La Manna ◽  
D. Vaz Martins
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Dry Weight ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Faecal Samples ◽  
Acid Detergent Fibre ◽  
Nir Reflectance Spectroscopy ◽  
Calibration Equations

Near infrared (NIR) reflectance spectroscopy was used to predict nitrogen (N), acid detergent fibre (ADF), neutral detergent fibre (NDF) and chromium (Cr) in beef faecal samples. One hundred and twenty faecal samples were scanned in a NIRSystems 6500 monochromator instrument over the wavelength range of 400–2500 nm in reflectance. Calibration equations were developed using modified partial least squares (MPLS) with internal cross validation to avoid overfitting. The coefficient of determination in calibration ( R2cal) and the standard error in cross validation ( SECV) were 0.80 (0.74) for N, 0.92 (12.04) for ADF, 0.86 (13.5) for NDF and 0.56 (0.07) for Cr in g kg−1 dry weight, respectively. Results for validation were 0.78 ( SEP: 0.1) for N, 0.74 ( SEP: 7.5) for ADF, 0.85 ( SEP: 8.5) for NDF and 0.10 (0.09) for Cr in g kg−1 dry weight, respectively.

scholarly journals Fast And Simultaneous Prediction Of Agricultural Soil Nutrients Content Using Infrared Spectroscopy

2019 ◽  
Vol 12 (1) ◽  
pp. 61-66
Author(s):  
Devianti Devianti ◽  
Zulfahrizal Zulfahrizal ◽  
Sufardi Sufardi ◽  
Agus Arip Munawar
Keyword(s):  
Agricultural Soil ◽  
Near Infrared ◽  
Prediction Models ◽  
Agricultural Soils ◽  
Principal Component Regression ◽  
Principal Component ◽  
Nutrient Content ◽  
Laboratory Method ◽  
Near Infrared Reflectance ◽  
Simultaneous Prediction

Abstract. The functions soil depends on the balances of its structure, nutrients composition as well as other chemical and physical properties. Conventional methods, used to determine nutrients content on agricultural soil were time consuming, complicated sample processing and destructive in nature. Near infrared reflectance spectroscopy (NIRS) has become one of the most promising and used non-destructive methods of analysis in many field areas including in soil science. The main aim of this present study is to apply NIRS in predicting nutrients content of soils in form of total nitrogen (N). Transmittance spectra data were obtained from a total of 18 soil samples from 8 different sites followed by N measurement using standard laboratory method. Principal component regression (PCR) with full cross validation were used to develop and validate N prediction models. The results showed that N content can be predicted very well even with raw spectra data with coefficient correlation (r) and residual predictive deviation index (RPD) were 0.95 and 3.35 respectively. Furthermore, spectra correction clearly enhances and improve prediction accuracy with r = 0.96 and RPD = 3.51. It may conclude that NIRS can be used as fast and simultaneous method in determining nutrient content of agricultural soils.

scholarly journals The influence of the inside diameter of the coring probe on the chemical composition of lucerne hay samples

2015 ◽  
Vol 44 (5) ◽  
pp. 41-43
Author(s):  
M Van Zyl ◽  
GDJ Scholtz ◽  
HJ Van der Merwe ◽  
R Meeske
Keyword(s):  
Chemical Composition ◽  
Quality Index ◽  
Near Infrared ◽  
Grading System ◽  
Limited Data ◽  
Near Infrared Reflectance ◽  
Inside Diameter ◽  
Small Probe ◽  
Similar Chemical ◽  
Acid Detergent Fibre

The obtaining of a representative sample is crucial for the application of an accurate and uniform lucerne hay grading system in South Africa. There is currently limited data available on the effect of the inside diameter of the coring probe on the chemical composition of the lucerne hay samples. A study was therefore undertaken to determine the influence of the inside diameter of a coring probe on the chemical composition of unground lucerne hay samples using the Near Infrared Reflectance Spectroscopy (NIRS) technique. Ten lucerne bales (total 40), randomly chosen from four different grades (Prime, Grade 1, 2 and 3 according to the National Lucerne Trust quality and grading system), were sampled with both a large probe (35 mm inside diameter and 520 mm long) and a small probe (12 mm inside diameter and 450 mm long). The samples with each probe were taken at approximately the same location in the bale. The samples were analysed with the NIRS for crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash and lignin. The model to calculate the new lucerne quality index (NLQI) from the ADF, ash and lignin, according to the National Lucerne Trust quality and grading scheme was used. Regression analysis revealed a significant relationship (r2) between the results of the large and small probe namely CP = 0.77, ADF = 0.95, NDF = 0.94, ash = 0.92, lignin = 0.87 and NLQI = 0.97. Sampling of lucerne hay with a large and small probe was irrelevant as resulted in similar chemical composition results.Keywords: Analysis grading, New Lucerne Quality Index, NIRS, quality, sampling

scholarly journals Prediction of Composition of Broiler Chicken Excreta Using Near-infrared Reflectance Spectroscopy

2005 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
I. T. Kadim ◽  
W. Al-Marzooqi ◽  
O. Mahgoub ◽  
K. Annamalai
Keyword(s):  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Acid Detergent Fibre ◽  
Gross Energy ◽  
Calibration Equations

Near-infrared reflectance spectroscopic (NIRS) calibrations were developed for the prediction of the content of dry matter (DM); nitrogen (N), ether extract (EE), neutral detergent fibre (NDF), acid detergent fibre (ADF), gross energy (GE), calcium (Ca) and phosphate (P) in broiler excreta samples. The chemical composition of broiler excreta was determined by the conventional chemical analysis methods in the laboratory and compared with NIRS. Excreta samples (n = 72) were oven dried (60 oC) and analyzed for DM, N, EE, NDF, ADF, GE, Ca and P. The determined values (mean ± SD) were as follows: DM: 31.46 ± 7.65 (range:19.14 - 44.51), N: 5.85 ± 2.88 (range: 4.85 -7.00), EE: 1.37 ± 0.25 (range: 0.88-1.99), ADF: 16.71 ± 1.99 (range: 12.11-19.97), NDF: 26.26 ± 1.63 (range: 22.03-30.21), GE: 15.27 ± 0.33 (range: 14.52-16.11), Ca: 2.57 ± 0.22 (range: 2.16-3.01), P: 1.79 ± 0.15 (range: 1.41-2.11). The samples were then scanned in a NIRS model 5000 analyzer and the spectra obtained for each sample. Calibration equations and prediction values were developed for broiler excreta samples. The software used modified partial least square regression statistic, as it is most suitable for natural products. For broiler excreta samples, the coefficient of determination (R2) and the standard error of prediction (SEP) was DM = 0.97, 1.27, N = 0.95, 0.72, EE = 0.92, 0.07, ADF = 0.87, 0.78, NDF = 0.88, 0.72, GE = 0.89; 0.24, Ca = 0.96, 0.06, P = 0.93, 0.09, respectively. The results indicate that it is possible to calibrate NIRS to predict major constituents in broiler excreta samples.

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