scholarly journals Use of near infrared reflectance spectroscopy to predict phytate phosphorus, total phosphorus, and crude protein of common poultry feed ingredients

2017 ◽  
Vol 96 (1) ◽  
pp. 160-168 ◽  
Author(s):  
R. Aureli ◽  
Q. Ueberschlag ◽  
F. Klein ◽  
C. Noël ◽  
P. Guggenbuhl
Keyword(s):  
Total Phosphorus ◽  
Crude Protein ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Poultry Feed ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Feed Ingredients ◽  
Phytate Phosphorus

Prediction of crude protein content in field peas using near infrared reflectance spectroscopy

2006 ◽  
Vol 86 (1) ◽  
pp. 157-159 ◽  
Author(s):  
G. C. Arganosa ◽  
T. D. Warkentin ◽  
V. J. Racz ◽  
S. Blade ◽  
C. Phillips ◽  
...  
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Correlation Coefficients ◽  
Reflectance Spectroscopy ◽  
Field Pea ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

A rapid, near-infrared spectroscopic method to predict the crude protein contents of 72 field pea lines grown in Saskatchewan, both whole seeds and ground samples, was established. Correlation coefficients between the laboratory and predicted values were 0.938 and 0.952 for whole seed and ground seed, respectively. Both methods developed are adequate to support our field pea breeding programme. Key words: Field pea, near-infrared reflectance spectroscopy, crude protein

Sampling requirements for predicting cattle diet quality using faecal near-infrared reflectance spectroscopy (F.NIRS) in heterogeneous tropical rangeland pastures

2010 ◽  
Vol 32 (4) ◽  
pp. 435 ◽  
Author(s):  
I. A. White ◽  
L. P. Hunt ◽  
D. P. Poppi ◽  
S. R. Petty
Keyword(s):  
Sample Size ◽  
Protein Content ◽  
Crude Protein ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Crude Protein Content ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Collection Method

Faecal near-infrared reflectance spectroscopy (F.NIRS) provides predictive information on cattle diets and nutritional levels, useful for livestock management or for research purposes. Potential errors exist throughout the entire F.NIRS process, including the collection method. The accepted collection method involves aggregating equal amounts of faecal material from 5 to 15 animals, mixing and removing a single sample for analysis. The adequacy of this method was tested by collecting and analysing up to 70 samples from individual cattle in different paddocks. Two methods were used to determine sample size based on observed variability in dietary attributes. Variability of dietary non-grass material and crude protein content increased with paddock size, so required sample size also increased. For dietary F.NIRS predictions to be used for research, our results suggest from 20 to 51 samples are needed in small to large paddocks to accurately predict the proportion of dietary non-grass material, from 12 to 50 samples for crude protein content and from 6 to 34 samples for dry matter digestibility. Composite samples from 15 cattle provided representative means in less than 50% of the situations investigated using biologically significant precision levels, but would be adequate for management of animal nutrition. Analysis of individual samples provided additional measures of range and variability which were also informative.

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.

ANALYSIS OF HAY, HAYLAGE AND CORN SILAGE SAMPLES BY NEAR INFRARED REFLECTANCE SPECTROSCOPY

1985 ◽  
Vol 65 (3) ◽  
pp. 753-760 ◽  
Author(s):  
E. V. VALDES ◽  
L. G. YOUNG ◽  
I. McMILLAN ◽  
J. E. WINCH
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Corn Silage ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Mineral Contents ◽  
Infrared Reflectance Spectroscopy ◽  
Filter Type

Separate calibrations for hay, haylage and corn silage were developed to predict chemical composition by near infrared reflectance spectroscopy (NIR). A scanning type of NIR instrument was used to select the best set of wavelengths (λ) while a filter type was used to evaluate the calibrations. Reflectance (R) was recorded as log (1/R). Bias (nonrandom error) was corrected for each set of samples before the NIR analysis. Percent crude protein (CP), acid detergent fiber (ADF), calcium (Ca) and phosphorus (P) were studied in the hay samples. In addition, potassium (K) and magnesium (Mg) were included for the haylage and corn silage samples. Six hundred samples, including calibration (C) and prediction sets (PRE1 and PRE2) were used. PRE1 samples came from the same population as the C samples, whereas PRE2 samples were obtained in a different year. Accuracy of the predictions was assessed by the coefficients of determination (r2), standard error of the estimate (SEE), and coefficients of variation (CV). Crude protein was the parameter best predicted by NIR with r2, SEE and CV ranging from 0.72 to 0.96, 0.43 to 1.17 and 5.6 to 10.4, respectively. The highest SEE for crude protein were associated with the PRE2 samples for haylage and hay samples (1.09 and 1.17), respectively. NIR predictions of ADF had r2, SEE and CV values ranging from 0.21 to 0.92, 1.44 to 2.53 and 5.3% to 7.9%, respectively. Corn silage had the lowest SEE for ADF in both C and PRE1 sets. Predicting mineral contents by NIR gave high CV (10.5%–34.5%) and low r2 values (0.02–0.75). Calcium predictions had the highest variability, and P and Mg predictions the lowest.These results indicate that CP was successfully predicted by NIR. The higher SEE values for ADF may have been due to variation in the wet chemistry values of some samples. Minerals were not adequately predicted by NIR as assessed by r2, SEE and CV values. Key words: Near infrared reflectance spectroscopy, forage, chemical analysis

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