Taking near infrared spectroscopy beyond feedstuff analysis to enhance animal production profitability

2013 ◽  
Vol 53 (11) ◽  
pp. 1179 ◽  
Author(s):  
Hadden Graham ◽  
Chris Piotrowski ◽  
Robert Van Barneveld
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Animal Feed ◽  
Nir Spectroscopy ◽  
Near Infrared Reflectance ◽  
Feed Ingredients ◽  
Feed Industry ◽  
Recent Developments ◽  
Feed Costs ◽  
Line Analysis

Near infrared reflectance (NIR) spectroscopy allows a cheap and rapid estimation of composition, and thus is widely used in the animal feed industry for the quality control and quality assurance of feeds and particularly feed ingredients. However, such analyses are often limited to a few variables not closely related to the nutritive value of the particular feed ingredients, and are often retrospective. This paper discusses recent developments in both hardware and software, which now allow real-time and in-line analysis of feed ingredients and feeds, and how these can be used to save substantial costs in the feed industry worldwide by reducing feed costs and giving more predictable animal performance. We also discuss how laboratory, hand-held and in-line NIR equipment could be widely used in the future for the purchase of feed ingredients and the manufacture of animal feeds.

Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

1991 ◽  
Vol 31 (2) ◽  
pp. 205 ◽  
Author(s):  
KF Smith ◽  
PC Flinn
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Cost Effective ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

The use of near infrared reflectance spectroscopy (NIRS) to predict the nutritive value of standard commercial diets for post-weaned pigs

2009 ◽  
Vol 2009 ◽  
pp. 108-108
Author(s):  
M E E McCann ◽  
R Park ◽  
M J Hutchinson ◽  
B Owens ◽  
V E Beattie
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Feed Ingredients ◽  
Infrared Reflectance Spectroscopy ◽  
Whole Wheat ◽  
Complete Diet

In order to assess the nutritive value of pig diets, performance and digestibility trials must be conducted as there is no accurate alternative to predict nutritive value. However, the use of near infrared reflectance spectroscopy (NIRS) to predict performance from feed ingredients has been shown to have potential. Owens et al (2007) investigated the use of NIRS to predict the performance of broilers offered wheat-based diets, through scanning of whole wheat, and observed that NIRS accurately predicted liveweight gain and gain:feed. The aim of this study was to investigate if NIRS could be used to predict the performance of pigs, through scanning of the complete diet.

Prediction of Phenolics and Tannins in Forage Legumes by near Infrared Reflectance

1998 ◽  
Vol 6 (1) ◽  
pp. 175-181 ◽  
Author(s):  
A.V. Goodchild ◽  
F.J. El Haramein ◽  
A. Abd El Moneim ◽  
H.P.S. Makkar ◽  
P.C. Williams
Keyword(s):  
Biological Activity ◽  
Condensed Tannins ◽  
Nutritive Value ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Gas Production ◽  
Near Infrared Reflectance ◽  
Rumen Microbes

Near infrared (NIR) spectroscopy calibrations for measures of tannins and nutritive value were made on a set of 40 hays and straws of Vicia and Lathyrus spp. by the modified partial least squares (MPLS) method and were evaluated by cross-validation. They successfully predicted, in the dry matter, 4.6–34.1 g kg−1 total phenolics with a cross-validation R2 of 0.95 and a SECV of 1.68 g kg−1, 1.3–23.1 g kg−1 total tannins ( R2 = 0.89, SECV = 1.84 g kg−1) and 0.5–30.3 g kg−1 condensed tannins ( R2 = 0.93, SECV = 2.34 g kg−1). In multiple regression and MPLS calibrations, a wavelength close to 2.144 μm was common to all measures of tannins, and was attributed to condensed tannins and its flavanoid precursors. The biological activity of tannins on rumen microbes, measured as a 0–6.9% effect on gas production with rumen liquor in vitro, was less precisely predicted by MPLS ( R2 = 0.49, SECV = 1.49%). The biological activity per gram of chemical tannins could not be predicted by NIR spectroscopy in the material studied. Acid detergent fibre, neutral detergent fibre, crude protein and gas production in vitro were predicted with R2 = 0.95 to 0.96 ( SECV = 18.2, 24.8, 10.1 g kg−1 or 7.2 mL g−1).

Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

1998 ◽  
Vol 6 (1) ◽  
pp. 229-234 ◽  
Author(s):  
William R. Windham ◽  
W.H. Morrison
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Longissimus Dorsi ◽  
Near Infrared Reflectance ◽  
Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

scholarly journals The Relative Performance of a Benchtop Scanning Monochromator and Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in Predicting Forage Nutritive Value

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 658
Author(s):  
Matthew F. Digman ◽  
Jerry H. Cherney ◽  
Debbie J. R. Cherney
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Prediction Models ◽  
Low Cost ◽  
Neutral Detergent Fiber ◽  
In Vitro Digestibility ◽  
Near Infrared Reflectance ◽  
Average Decrease ◽  
Manufacturing Techniques

Advanced manufacturing techniques have enabled low-cost, on-chip spectrometers. Little research exists, however, on their performance relative to the state of technology systems. The present study compares the utility of a benchtop FOSS NIRSystems 6500 (FOSS) to a handheld NeoSpectra-Scanner (NEO) to develop models that predict the composition of dried and ground grass, and alfalfa forages. Mixed-species prediction models were developed for several forage constituents, and performance was assessed using an independent dataset. Prediction models developed with spectra from the FOSS instrument had a standard error of prediction (SEP, % DM) of 1.4, 1.8, 3.3, 1.0, 0.42, and 1.3, for neutral detergent fiber (NDF), true in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude protein (CP), respectively. The R2P for these models ranged from 0.90 to 0.97. Models developed with the NEO resulted in an average increase in SEP of 0.14 and an average decrease in R2P of 0.002.

On-line application of visible and near infrared reflectance spectroscopy to predict physical and sensory characteristics of beef quality

2009 ◽  
Vol 2009 ◽  
pp. 135-135
Author(s):  
N Prieto ◽  
D W Ross ◽  
E A Navajas ◽  
G Nute ◽  
R I Richardson ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Sensory Characteristics ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Beef Quality ◽  
Near Infrared Reflectance ◽  
On Line ◽  
Meat Samples

Visible and near infrared reflectance spectroscopy (Vis-NIR) has been widely used by the industry research-base for large-scale meat quality evaluation to predict the chemical composition of meat quickly and accurately. Meat tenderness is measured by means of slow and destructive methods (e.g. Warner-Bratzler shear force). Similarly, sensory analysis, using trained panellists, requires large meat samples and is a complex, expensive and time-consuming technique. Nevertheless, these characteristics are important criteria that affect consumers’ evaluation of beef quality. Vis-NIR technique provides information about the molecular bonds (chemical constituents) and tissue ultra-structure in a scanned sample and thus can indirectly predict physical or sensory parameters of meat samples. Applications of Vis-NIR spectroscopy in an abattoir for prediction of physical and sensory characteristics have been less developed than in other fields. Therefore, the aim of this study was to test the on-line Vis-NIR spectroscopy for the prediction of beef quality characteristics such as colour, instrumental texture, water holding capacity (WHC) and sensory traits, by direct application of a fibre-optic probe to the M. longissimus thoracis with no prior sample treatment.

Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

1995 ◽  
Vol 78 (3) ◽  
pp. 802-806 ◽  
Author(s):  
José Louis Rodriguez-Otero ◽  
Maria Hermida ◽  
Alberto Cepeda
Keyword(s):  
Near Infrared ◽  
Independent Set ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sample Treatment ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

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