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.

Application of near Infrared Reflectance Spectroscopy for the Analysis of Organic C, Total N and pH in Soils of Uruguay

2002 ◽  
Vol 10 (3) ◽  
pp. 215-221 ◽  
Author(s):  
A. Morón ◽  
D. Cozzolino
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Calibration Coefficient ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Total N ◽  
Organic C ◽  
Silver Spring ◽  
Validation Set

Near infrared (NIR) reflectance spectroscopy was used to analyse samples ( n = 332) from different soils from Uruguay (South America) for organic carbon (OC), total nitrogen (N) and pH. One set ( n = 200) of samples randomly selected was used to develop the NIR calibrations while the remaining ( n = 132) samples were used as the validation set. The samples were scanned in a small circular cup in reflectance mode (400–2500 nm), using a Foss NIRSystems 6500 (Silver Spring, MD, USA). Modified partial least squares (MPLS) was used to produce the calibration models and cross-validation was used to avoid collinearity effects among variables. Three mathematical treatments and four scatter corrections were also applied. The calibration coefficient of determination ( R2CAL) and the standard error in cross-validation ( SECV) were 0.94 ( SECV: 1.9) for OC; 0.91 ( SECV: 0.19) for total N in g kg−1 and 0.93 ( SECV: 0.18) for pH, respectively. The simple correlation coefficient of validation ( rVAL) and the standard errors of prediction ( SEP) were 0.74 and 5; 0.73 and 0.4; 0.84 and 0.28 for OC, total N and pH, respectively.

Determination of macro elements in alfalfa and white clover by near-infrared reflectance spectroscopy

2002 ◽  
Vol 139 (4) ◽  
pp. 413-423 ◽  
Author(s):  
A. MORÓN ◽  
D. COZZOLINO
Keyword(s):  
White Clover ◽  
Near Infrared ◽  
Cross Validation ◽  
Dry Weight ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Wide Range

Near-infrared reflectance spectroscopy was used to assess the mineral composition of both alfalfa (Medicago sativa L.) and white clover (Trifolium repens L.). Alfalfa (n=230) and white clover (n=97) plant samples from different locations in Uruguay representing a wide range of soil types were analysed. The samples were scanned for reflectance in a NIRSystems 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Predictive equations were developed using modified partial least squares (MPLS) with cross validation to avoid overfitting. The coefficients of determination in calibration (R_{\rm cal}^{2}) and the standard errors in cross validation (SECV) were 0·93 (SECV: 1·6), 0·95 (SECV: 1·3), 0·93 (SECV: 1·9), 0·88 (SECV: 2·7), 0·82 (SECV: 0·3) and 0·75 (SECV: 4·7) for alfalfa and 0·98 (SECV: 0·8), 0·52 (SECV: 0·8), 0·97 (SECV: 2·7), 0·83 (SECV: 3·1), 0·82 (SECV: 1·9) and 0·45 (SECV: 2·6) for white clover, for N, Ca, K, P, Mg and S in g/kg on a dry weight respectively. Calcium, nitrogen and potassium were well predicted by NIRS in both alfalfa and white clover samples.

The use of visible (VIS) and near infrared (NIR) reflectance spectroscopy to predict fibre diameter in both clean and greasy wool samples

2005 ◽  
Vol 80 (3) ◽  
pp. 333-337 ◽  
Author(s):  
D. Cozzolino ◽  
F. Montossi ◽  
R. San Julian
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Model Development ◽  
Fibre Diameter ◽  
Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Calibration Models ◽  
Nir Reflectance Spectroscopy

AbstractAbstract Visible (VIS) and near infrared (NIR) reflectance spectroscopy combined with multivariate data analysis were explored to predict fibre diameter in both clean and greasy Merino wool samples. Fifty clean and 400 greasy wool samples were analysed. Samples were scanned in a large cuvette using a NIRSystems 6500 monochromator instrument by reflectance in the VIS and NIR regions (400 to 2500 nm). Partial least square (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Different mathematical treatments were used during model development. Cross validation was used to assess the performance and avoid overfitting of the models. The NIR calibration models gave a coefficient of determination in calibration (R2) > 0·90 for clean wool samples and a R2 < 0·50 for greasy wool samples. The values for the residual predictive value, RPD (ratio of standard deviation (s. d.) to the root mean square of the standard error of cross validation (RMSECV)) were 3 for clean and 0·6 for greasy wool samples, respectively. The results indicated that fibre diameter in greasy wool samples was poorly predicted with NIR, while clean wool showed good relationships.More research is required to improve the calibration on greasy wool samples if the technology is to be used for rapid analysis to assist in the selection of animals in breeding programmes.

Predicting Elements in White Pine and Red Oak Foliage with Visible-Near Infrared Reflectance Spectroscopy

1997 ◽  
Vol 5 (2) ◽  
pp. 77-82 ◽  
Author(s):  
R.A. Hallett ◽  
J.W. Hornbeck ◽  
M.E. Martin
Keyword(s):  
Forest Canopy ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Red Oak ◽  
Acid Digestion ◽  
White Pine ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy ◽  
Calibration Equations ◽  
Mn Concentrations

Near infrared (NIR) reflectance spectroscopy was evaluated for its effectiveness at predicting Al, Ca, Fe, K, Mg and Mn concentrations in white pine ( Pinus strobus L.) and red oak ( Quercus rubra L.) foliage. A NIR spectrophotometer was used to scan 470 dried, ground foliage samples. These samples were used to develop calibration equations using a modified partial least squares (MPLS) regression technique. For the calibration equations, concentrations of Al, Ca, Fe, K, Mg and Mn as determined by acid digestion and laboratory analysis were regressed against second-difference absorbance values measured from 400 to 2498 nm. The regression models developed by NIR reflectance spectroscopy were unable to predict Fe. Predictions were satisfactory for Al, Ca, K, Mn and Mg. It still is uncertain which mineral/organic associations are being detected by NIR reflectance spectroscopy. Future applications may include prediction of element concentrations in the forest canopy via remote sensing.

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.

Exploring the Use of near Infrared Reflectance Spectroscopy to Study Physical Properties and Microelements in Soils

2003 ◽  
Vol 11 (2) ◽  
pp. 145-154 ◽  
Author(s):  
A. Moron ◽  
D. Cozzolino
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Agricultural Soils ◽  
Low Cost ◽  
Scatter Correction ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Second Derivative ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy

Near infrared (NIR) reflectance spectroscopy was used to predict the content of silt, sand, clay, iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) in soil. A total of 332 samples from agricultural soils (0–15 cm depth) in Uruguay (South America) were used. The samples were scanned in a monochromator instrument (NIRSystems 6500, Silver Spring, MD, USA). Two mathematical treatments (first and second derivative) with SNVD (scatter normal variate and detrend) and without scatter correction were studied. Modified partial least squares (mPLS) was used to develop the calibration models. The coefficient of determination in calibration ( R2cal) and the standard error in calibration ( SEC) using the second derivative were 0.81 ( SEC: 5.1), 0.83 ( SEC: 5.3), 0.92 ( SEC: 2.6) for percent sand, silt and clay, respectively. The R2cal and standard error of cross-validation ( SECV) were for Cu 0.87 ( SEC: 0.7), for Fe 0.92 ( SEC: 21.7), for Mn 0.72 ( SEC: 83.0) and for Zn 0.72 ( SEC: 1.2) on mg kg−1 dry matter. It was concluded that NIR reflectance spectroscopy has a great potential as an analytical method for routine analysis of soil texture, Fe, Zn and Cu due the speed and low cost of analysis.

The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics

2003 ◽  
Vol 140 (1) ◽  
pp. 65-71 ◽  
Author(s):  
D. COZZOLINO ◽  
A. MORÓN
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Silver Spring

Near-infrared reflectance spectroscopy (NIRS) was used for the analysis of soil samples for silt, sand, clay, calcium (Ca), potassium (K), sodium (Na), magnesium (Mg), copper (Cu) and iron (Fe). A total of 332 samples of different soils from Uruguay (South America) were used. The samples were scanned in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance. Cross validation was applied to avoid overfitting of the models. The coefficient of determination in calibration (R^2_{\rm cal}) and the standard errors in cross validation (SECV) were 0·80 (SECV: 6·8), 0·84 (SECV: 6·0), 0·90 (SECV: 3·6) in per cent for sand, silt and clay respectively. For both macro and microelements the R^2_{\rm cal} and SECV were 0·80 (SECV: 0·1), 0·95 (SECV: 2·9), 0·90 (SECV 0·8), for K, Ca, Mg in g/kg respectively, and 0·86 (SECV: 0·82) and 0·92 (SECV: 25·5) for Cu and Fe in mg/kg. It was concluded that NIRS has a great potential as an analytical method for soil routine analysis due to the speed and low cost of analysis.

Prediction of the Neuro-Toxin Beta-N-Oxalyl-Amino-L-Alanine in Lathyrus Species, Using near Infrared Reflectance Spectroscopy

1998 ◽  
Vol 6 (A) ◽  
pp. A93-A96 ◽  
Author(s):  
F. Jaby El-Haramein ◽  
A. Abd-El Moneim ◽  
H. Nakkoul ◽  
P.C. Williams
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Lower Limbs ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Northern India ◽  
Drought Tolerant ◽  
Large Numbers ◽  
Analysis Application ◽  
Nir Reflectance Spectroscopy

Grasspea or chickling vetch ( Lathyrus spp.) is a common food legume, widely grown and eaten in northern India, Bangladesh, Pakistan, Nepal and Ethiopia. It contains the neurotoxin beta-N-oxalyl-amino-L-alanine (BOAA), which can cause the disease known as “neuro-lathyrism”, an irreversible paralysis of the lower limbs, if BOAA-rich seeds form a large proportion of the diet. Lathyrus is a drought-tolerant crop, and ICARDA seeks to breed high-yielding lines that are low in BOAA. Conventional methods for determination of BOAA are time-consuming, expensive, and not practicable for screening large numbers of genotypes. Near infrared (NIR) reflectance spectroscopy offers a rapid, inexpensive method of analysis. Application of NIR reflectance spectroscopy to the prediction of BOAA in Lathyrus has been achieved by developing NIR reflectance spectroscopy equations involving 88 samples, which represented three species: L. sativus, L. cicera and L. ochrus. Both intact and ground seeds were studied. Content of BOAA ranged from 0.09 to 0.83%. Seeds of L. cicera were significantly lower than those of the other two species. The best results were obtained from whole seeds, using multiple linear regression. The standard error of prediction of 0.05% and coefficient of determination ( r2) of 0.94 are considered quite adequate for use in the Lathyrus breeding programme.

Measurement of the magnesium concentration in perennial ryegrass (Lolium perenne) using near infrared reflectance spectroscopy

1991 ◽  
Vol 42 (8) ◽  
pp. 1399 ◽  
Author(s):  
KF Smith ◽  
SE Willis ◽  
PC Flinn
Keyword(s):  
Lolium Perenne ◽  
Perennial Ryegrass ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Magnesium Concentration ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Calibration Equations

Near infrared reflectance spectroscopy (NIR) was used to develop calibration equations to measure the magnesium concentration in perennial ryegrass herbage (Lolium perenne). A subset of 72 samples was selected on the basis of spectral variation from 400 samples grown in 1988-1989. Three alternative equations were chosen using stepwise multiple linear regression, with standard errors ranging from 0.4 to 0.3 g/kg DM with corresponding squared multiple correlation coefficients ( R2) of 0.68 to 0.82. The equations had 2, 4 and 4 wavelength terms respectively. When these equations were tested on an independent population of perennial ryegrass samples, a significant bias existed when using the 4 term equations but there was no bias when the 2 term equation was used. We conclude that NIR can be used to screen large numbers of perennial ryegrass plants for magnesium concentration. However, for the calibration equations to be used for the analysis of other populations equation performance must be monitored by comparing reference and NIR analyses on a small number of samples.

Near-infrared reflectance spectroscopy for estimating food intake in housed dairy cows

1999 ◽  
Vol 1999 ◽  
pp. 93-93
Author(s):  
Y. Unal ◽  
P. C. Garnsworthy
Keyword(s):  
Dairy Cows ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Dry Matter Intake ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Faecal Samples

Dry matter intake (DMI) is a major limitation to milk production in dairy cows, but is difficult to measure under commercial conditions where cows are housed and fed in groups. Several methods have been developed to estimate DMI by individual cows, such as using inert markers, where dual markers can be used to predict digestibility and faecal output simultaneously. However, their scope is limited by the laboratory analyses required and there are problems with marker dosing and recovery. Predictions of DMI by near-infrared reflectance spectroscopy (NIRS) have been reported, but they have been based on scanning forage samples to predict intake potential. Since DMI is a function of the animal as well as the diet, it is more logical to scan samples of faeces when predicting individual intakes. The objective of this study was to see whether NIRS could accurately predict DMI from faecal samples of individual cows.

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