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.

Broad-based calibrations of in vitro gas production of forages by near-infrared reflectance spectroscopy

1998 ◽  
Vol 22 ◽  
pp. 234-237
Author(s):  
M. Herrero ◽  
N. S. Jessop
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Gas Production ◽  
Grass Species ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Wide Range

There is increasing demand to obtain fast and accurate dynamic nutritional information from forages. Near-infrared reflectance spectroscopy (NIRS) offers the possibility for obtaining such information for a range of nutritional constituents of foods. Herrero et al. (1996 and 1997) calibrated in vitro gas production measurements of a single grass species by NIRS. There would be greater practical benefit if the gas production predictions could be obtained using calibrations derived from a wide range of plant species, since a single equation could be used for all forages. The objective of this study was to investigate if in vitro gas production measurements of a broad based sample population could be calibrated by NIRS.

Near infrared reflectance spectroscopy as a potential surrogate method for the analysis of D13C in mature kernels of durum wheat

2001 ◽  
Vol 52 (8) ◽  
pp. 809 ◽  
Author(s):  
J. P. Ferrio ◽  
E. Bertran ◽  
M. Nachit ◽  
C. Royo ◽  
J. L. Araus
Keyword(s):  
Durum Wheat ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Local Models ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Δ13c Values ◽  
Infrared Reflectance Spectroscopy ◽  
Wide Range

Carbon isotope discrimination (Δ13C) in grain is a potentially useful trait in breeding programs that aim to increase the yield of wheat and other cereals. Near infrared reflectance spectroscopy (NIRS) is used in routine assays to determine grain and flour quality. This study assesses the ability of NIRS to predict Δ13C in mature kernels of durum wheat. Plants were grown in north-west Syria as this location provided 3 distinct Mediterranean trials that covered a wide range for Δ13C values in grains (from about 12.9‰ to 17.6‰). We measured the spectral reflectance signature between 1100 and 2500 nm in samples from the same flour used in the conventional (i.e. mass spectrometry) determinations of Δ13C. By using principal components regression and partial least squares regression (PLSR), a model of the association between conventional laboratory analysis and these spectra was produced. Global regressions, which included samples from all 3 trials, and local models, which used samples from only one trial, were built and then validated with sample sets not included in calibration procedures. In global models, strong significant correlations (P < 0.001) were found between NIRS-predicted Δ13C and measured Δ13C values. PLSR gave r 2 values of 0.86 and 0.82 for calibration and validation sets, respectively. Although less strongly correlated, all local models selected for a subset of samples with significantly higher Δ13C values. Local models also performed well when selecting samples from the other 2 trials. The advantages and possible limitations of NIRS are further discussed.

Study of dissected lamb muscles by visible and near infrared reflectance spectroscopy for composition assessment

2000 ◽  
Vol 70 (3) ◽  
pp. 417-423 ◽  
Author(s):  
D. Cozzolino ◽  
I. Murray ◽  
J. R. Scaife ◽  
R. Paterson
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Cross Validation ◽  
Rectus Femoris ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Fresh Weight Basis ◽  
Infrared Reflectance Spectroscopy

AbstractNear infrared reflectance spectroscopy (NIRS) was used to study the reflectance properties of intact and minced lamb muscles in two presentations to the instrument to predict their chemical composition. A total of 306 muscles were examined from 51 lambs, consisting of the following muscles: longissimus dorsi, supraspinatus, infraspinatus, semimembranosus, semitendinosus and rectus femoris. Modified partial least squares (MPLS) regression models of chemical variables yielded R2 and standard error of cross-validation (SECV) of 0·76 (SECV: 10·4), 0·83 (SECV: 5·5) and 0·73 (SECV: 4·7) for moisture, crude protein and intramuscular fat in the minced samples expressed as g/kg on a fresh-weight basis, respectively. Calibrations for intact samples had lower R2 and higher standard error of cross validation (SECV) compared with the minced samples.

GENETIC STUDIES ON KERNEL HARDNESS IN WHEAT USING GRINDING TIME AND NEAR INFRARED REFLECTANCE SPECTROSCOPY

1983 ◽  
Vol 63 (4) ◽  
pp. 825-832 ◽  
Author(s):  
D. R. SAMPSON ◽  
D. W. FLYNN ◽  
P. JUI
Keyword(s):  
Near Infrared ◽  
Single Gene ◽  
Reflectance Spectroscopy ◽  
Kernel Hardness ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Wide Range ◽  
Grinding Time

Kernel hardness was measured in 600 random lines from five crosses and in seven control cultivars of spring wheat grown in 2 yr, using grinding time and near infrared reflectance spectroscopy. Both methods clearly differentiated soft from hard cultivars. Grinding time was the more accurate method because it gave lower coefficients of variation and higher correlations between years but it required five times more grain. The parents of the five crosses represented a range in hardness and were themselves from the cross Opal (hard) × Pitic 62 (soft). A hard × hard cross gave only hard lines; a medium × soft cross gave mostly soft; three soft or medium × hard crosses gave a wide range of hardness types that in two crosses suggested a single gene difference between hard and soft. Heritability of grinding time in standard units ranged from 55 to 92% per progeny and on average was 10% greater for soft lines than for hard.Key words: Wheat, kernel hardness, grinding time, Technicon InfraAlyzer, genetics

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.

scholarly journals Optimizing Near Infrared Reflectance Spectroscopy to Predict Nutritional Quality of Chickpea Straw for Livestock Feeding

Animals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3409
Author(s):  
Tena Alemu ◽  
Jane Wamatu ◽  
Adugna Tolera ◽  
Mohammed Beyan ◽  
Million Eshete ◽  
...  
Keyword(s):  
Nutritional Quality ◽  
Nutritive Value ◽  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Improvement Programs

Multidimensional improvement programs of chickpea require screening of a large number of genotypes for straw nutritive value. The ability of near infrared reflectance spectroscopy (NIRS) to determine the nutritive value of chickpea straw was identified in the current study. A total of 480 samples of chickpea straw representing a nation-wide range of environments and genotypic diversity (40 genotypes) were scanned at a spectral range of 1108 to 2492 nm. The samples were reduced to 190 representative samples based on the spectral data then divided into a calibration set (160 samples) and a cross-validation set (30 samples). All 190 samples were analysed for dry matter, ash, crude protein, neutral detergent fibre, acid detergent fibre, acid detergent lignin, Zn, Mn, Ca, Mg, Fe, P, and in vitro gas production metabolizable energy using conventional methods. Multiple regression analysis was used to build the prediction equations. The prediction equation generated by the study accurately predicted the nutritive value of chickpea straw (R2 of cross validation >0.68; standard error of prediction <1%). Breeding programs targeting improving food-feed traits of chickpea could use NIRS as a fast, cheap, and reliable tool to screen genotypes for straw nutritional quality.

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