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.

Application of near infrared reflectance spectroscopy to the determination of carotenoid content in tritordeum for breeding purposes

2005 ◽  
Vol 56 (1) ◽  
pp. 85 ◽  
Author(s):  
S. G. Atienza ◽  
C. M. Avila ◽  
M. C. Ramírez ◽  
A. Martín
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Carotenoid Content ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Non Destructive

For pasta production, the yellow colour, mainly caused by carotenoids, is a worldwide requirement. Hexaploid tritodeums are the amphiploids derived from the cross between Hordeum chilense and Triticum turgidum. They show a higher carotenoid content than their wheat parents. This work aimed to develop a non-destructive method for carotenoid content determination to assist the tritordeum breeding program. We assessed the ability of near infrared reflectance spectroscopy (NIRS) to predict carotenoid content in whole grains of tritordeum. In total, 285 samples were scanned by NIRS. After non-destructive NIRS scanning, the seeds were analysed for carotenoid content and a calibration equation was developed. It is characterised by a coefficient of multiple determination (R2) of 0.85. This equation was initially evaluated by cross validation showing an r2 of 0.81 and a standard error of cross validation (SECV) of 1.49. It was further evaluated using external validation with a different set of samples not included in the calibration. This analysis showed an r2 of 0.81 and a standard error of performance (SEP) of 1.51. This equation allows discrimination between low and high carotenoid content lines in a non-destructive way. These results constitute a substantial advance for tritordeum breeding programs whose final aim is to develop high carotenoid content tritordeums useful for durum wheat breeding.

Opportunities for, and limitations of, near infrared reflectance spectroscopy applications in soil analysis: A review

2009 ◽  
Vol 89 (5) ◽  
pp. 531-541 ◽  
Author(s):  
C Nduwamungu ◽  
N Ziadi ◽  
L -É Parent ◽  
G F Tremblay ◽  
L Thuriès
Keyword(s):  
Sample Preparation ◽  
Standard Error ◽  
Near Infrared ◽  
Soil Analysis ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Reference Methods

Near infrared reflectance spectroscopy (NIRS) is a cost- and time-effective and environmentally friendly technique that could be an alternative to conventional soil analysis methods. In this review, we focussed on factors that hamper the potential application of NIRS in soil analysis. The reported studies differed in many aspects, including sample preparation, reference methods, spectrum acquisition and pre-treatments, and regression methods. The most significant opportunities provided by NIRS in soil analysis include its potential use in situ, the determination of various biological, chemical, and physical properties using a single spectrum per sample, and an estimated reduction of analytical cost of at least 50%. Contradictory results among studies on NIRS utilisation in soil analysis are partly related to variations in sample preparation and reference methods. The following calibration statistics appear to be most appropriate for comparing NIRS performance across soil attributes: (i) coefficient of determination (r2), (ii) ratio of performance deviation (RPD), (iii) coefficient of regression (b), and (iv) ratio of the standard error of prediction (SEP) to the standard error of the reference method (SER), i.e., the ratio of standard errors (RSE). Further investigations on issues such as (i) RSE guidelines, (ii) correlation between NIRS spectrophotometers, (iii) correlation of different reference methods for a given attribute to soil spectra, (iv) identification of key factors affecting the accuracy of NIRS predictions, and (v) efficient use of spectral libraries are required to enhance the acceptability of NIRS as a soil analysis technique and to make it more user-friendly. Standardized guidelines are proposed for the assessment of the accuracy of NIRS predictions of soil attributes.Key words: Near infrared reflectance spectroscopy, soil analysis, calibration

Moisture Analysis of Forage by Near Infrared Reflectance Spectroscopy: Preliminary Collaborative Study and Comparison Between Karl Fischer and Oven Drying Reference Methods

1988 ◽  
Vol 71 (2) ◽  
pp. 256-262
Author(s):  
William R Windham ◽  
Franklin E Barton ◽  
James A Robertson
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Collaborative Study ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Karl Fischer ◽  
Infrared Reflectance Spectroscopy ◽  
Moisture Analysis

Abstract A collaborative study of moisture analysis by neai infrared reflectance spectroscopy (NIRS) has been completed involving 5 laboratories and 20 forage samples. Near infrared reflectance spectroscopy calibrations for moisture were developed in the Associate Referee's laboratory from Karl Fischer (KF) and AOAC air oven (AO) (135°C for 2 h) moisture methods, respectively, and transferred to each collaborating laboratory's NIRS instrument. NIRS moisture data were validated with KF data from the Associate Referee's laboratory and AO data from each collaborating laboratory. The standard error of analysis of KF data by NIRS KF determination and AO data by NIRS AO determination ranged from 0.25 to 0.48% and from 0.74 to 1.88%, respectively. The standard errors between laboratories for NIRS KF and NIRS AO determinations were 0.2" and 0.39%, respectively. The standard error between moisture analyses by NIRS KF and NIRS AO calibrations, averaged across laboratories, was 0.42%. In addition, the standard error between laboratories for the AOAC AO method was 0.63%. The increase in standard error for the AOAC AO method was due to the random and systematic errors associated with the gravimetric techniques. The results indicate that NIRS analysis can accurately and precisely deterrr ine the moisture content of forages and forage crops because of th« very strong absorbance of water in the near infrared region.

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.

Predicting the stage of decay of decomposing leaves by near infrared reflectance spectroscopy

1993 ◽  
Vol 23 (12) ◽  
pp. 2552-2559 ◽  
Author(s):  
Dominique Gillon ◽  
Richard Joffre ◽  
Pierre Dardenne
Keyword(s):  
Leaf Litter ◽  
Standard Error ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Litter Bags ◽  
Litter Mass ◽  
Infrared Reflectance Spectroscopy

To study mineral cycling in forest ecosystems, it is essential to know the decomposition rate of the litter. This study attempted to predict directly, by near infrared reflectance spectroscopy, the stage of decomposition of leaf litter expressed as the percentage of ash-free litter mass remaining (LMR). Leaf litter of 10 different species, with varied initial compositions and at different stages of decomposition produced by incubation in the laboratory under controlled conditions, were used in this study. The LMR calibrations were carried out on half of the samples of the various populations (all species, woody species, broad-leaved species, trees, broad-leaved trees, oaks, deciduous trees, and evergreen trees). The standard error of cross validation varied between 1.69 and 3.01. Predictions were carried out on the other half of the samples of each population; the standard error of prediction varied between 2.35 and 3.77, with a r2 (coefficient of determination) of 0.97 to 0.99. The calibration equations obtained from the laboratory samples were applied to samples that had decomposed in the field in litter bags. The standard error of prediction varied between 4.46 and 5.97, with a r2 of 0.90 to 0.93. Near infrared reflectance spectroscopy thus provides a direct prediction of the LMR in leaf litter of different species, during the decomposition stage studied (i.e., between 100 and 20% of litter mass remaining). The possibilities of using near infrared reflectance spectroscopy in decomposition studies are discussed.

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.

scholarly journals Analysis of Grass Silage from Northwestern Spain by Near-Infrared Reflectance Spectroscopy

2002 ◽  
Vol 85 (3) ◽  
pp. 541-545 ◽  
Author(s):  
Begoña Villamarín ◽  
Esperanza Fernández ◽  
Jesus Mendéz
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Neutral Detergent Fiber ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Grass Silage ◽  
Northwestern Spain

Abstract Near-infrared reflectance spectroscopy (NIRS) was evaluated for the determination of protein, crude fiber (CF), acid detergent fiber (ADF), and neutral detergent fiber (NDF) in grass silage. Calibration equations were based on analyses of 366 samples of grass silage produced in Northwestern Spain over 4 consecutive years (1992–1995) and validated by analyses of a set of 72 silage samples harvested during 1996. Dried and ground samples were analyzed by chemical and NIRS procedures. The spectral data were analyzed by regression against a range of chemical parameters, using modified partial least-squares (MPLS) multivariate analysis in conjunction with different mathematical treatments of the spectra. For each parameter, the optimum calibration was evaluated on the basis of the coefficient of multiple determination (R2), the coefficient of simple correlation (r2), the standard error of calibration (SEC), the standard error of cross-validation (SECV), and the standard error of validation (SEV). R2 and r2 were &gt;0.90; SEC values were 0.58, 1.04, 1.40, and 1.75; SECV values were 0.64, 1.15, 1.50, and 2.04; and SEV values were 0.56, 1.02, 1.42, and 1.80 for protein, CF, ADF, and NDF, respectively. The ratio of the standard deviation of the reference data to the SEV was &gt;3.0 for each of the 4 parameters, which indicates that the equations can be used in routine analysis.

scholarly journals Discrimination between seedlings of Eucalyptus globulus, E. nitens and their F1 hybrid using near-infrared reflectance spectroscopy and foliar oil content

2008 ◽  
Vol 57 (1-6) ◽  
pp. 262-269 ◽  
Author(s):  
J. R. Humphreys ◽  
J. M. O’Reilly-Wapstra ◽  
J. L. Harbard ◽  
N. W. Davies ◽  
A. R. Griffin ◽  
...  
Keyword(s):  
Eucalyptus Globulus ◽  
Near Infrared ◽  
Cross Validation ◽  
Morphological Characteristics ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
F1 Hybrid ◽  
Infrared Reflectance Spectroscopy

Summary Identification of plant hybrids produced from closely related species can be difficult using morphological characteristics alone, particularly when identifying young seedlings. In this study, we compared the performance of three calibration models developed to discriminate between seedlings of Eucalyptus globulus, E. nitens and their first-generation hybrid using either foliar oil chemistry or near-infrared reflectance spectral data from fresh, whole leaves. Both oil and near-infrared reflectance spectroscopy (NIRS) models were developed using partial least-squares discriminant analysis and showed high classification accuracy, all correctly classifying more than 91% of samples in cross-validation. Additionally, we developed a larger, “global” and independently validated NIRS model specifically to discriminate between E. globulus and F1 hybrid seedlings of different ages. This model correctly classified 98.1% of samples in cross-validation and 95.1% of samples from an independent test set. These results show that NIRS analysis of fresh, whole leaves can be used as a rapid and accurate alternative to chemical analysis for the purpose of hybrid identification.

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