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 >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 >3.0 for each of the 4 parameters, which indicates that the equations can be used in routine analysis.

Prediction of the non-fermentable energy fraction of grass silage using near infrared reflectance spectroscopy (NIRS)

1996 ◽  
Vol 1996 ◽  
pp. 218-218
Author(s):  
E. R. Deaville ◽  
D. I. Givens
Keyword(s):  
Volatile Fatty Acids ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Considerable Proportion ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Energy Fraction ◽  
Infrared Reflectance Spectroscopy ◽  
Grass Silage

The new metabolisable protein system (AFRC, 1992) introduces the requirement to distinguish between the energy in feeds which is fermentable (available for microbial growth) and that which is non-fermentable (available to the host animal; NFE). Fermented forages such as grass silage (GS) may contain substantial amounts of fermentation acids (FA) (volatile fatty acids (VFA) and lactic acid (LA)) and therefore, contain a considerable proportion of NFE. The aim of the present experiment was to investigate the use of near infrared reflectance spectroscopy (NIRS) as a rapid method for predicting the FAs in fresh (undried) GS.

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.

Prediction of composition and ruminal degradability characteristics of barley straw by near infrared reflectance spectroscopy

1999 ◽  
Vol 79 (4) ◽  
pp. 519-523 ◽  
Author(s):  
G. W. Mathison ◽  
H. Hsu ◽  
R. Soofi-Siawash ◽  
G. Recinos-Diaz ◽  
E. K. Okine ◽  
...  
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Neutral Detergent Fiber ◽  
Barley Straw ◽  
Chemical Components ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Ruminal Degradability

The usefulness of near infrared reflectance spectroscopy (NIRS) for the prediction of the nutritive value of straw was examined with 195 samples of barley straw. Excluding lignin, NIRS technique explained 84 to 94% of the variation in chemical components in the validation set. From 69 to 84% of the variation in ruminal degradability characteristics, excluding rate of degradation, was explained by NIRS. With the exception of neutral detergent fiber, accuracies of prediction were not improved when NIRS calibrations were based upon calibration sets containing straw only in contrast with when barley hay and barley silage were included with straw in the calibration set. We conclude that near infrared reflectance spectroscopy is a useful method for predicting chemical composition of straw and estimating its ruminal degradability characteristics. Key words: Barley, straw, near infrared reflectance spectroscopy, in situ, degradability

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.

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