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

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

Determination of Acid-Detergent Fiber and Crude Protein in Forages by Near-Infrared Reflectance Spectroscopy: Collaborative Study

1988 ◽  
Vol 71 (6) ◽  
pp. 1162-1167 ◽  
Author(s):  
Franklin E Barton II ◽  
William R Windham
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Collaborative Study ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Acid Detergent Fiber ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Abstract A Collaborative Study Was Conducted To Determine The Standard Error Of Difference Among Laboratories For Near-Infrared Reflectance Spectroscopic (Nirs) Determination Of Acid-Detergent Fiber (Adf) And Crude Protein In Forages. The 6 Participating Laboratories Were Members Of The Usda/Ars National Near-Infrared Reflectance Spectroscopy Forage Research Project. The Nirs Calibration Equations Were Developed In The Associate Referee's Laboratory For Crude Protein And Adf And Were Transferred To The Instrument In Each Of The Other Collaborating Laboratories. The Calibration Set Included Over 650 Diverse Forage Samples With Crude Protein And Adf Calibration Data; The Validation Set Included 94 Samples Of Bermudagrass. Amonglaboratory Reproducibility For The Nirs Method, Calculated As The Relative Standard Deviation For Reproducibility (Rsdr), Was 1.14% For Adf And 0.42% For Crude Protein. The Variance Component For Among-Laboratory Variation (Coefficient Of Variation) Was 2.54% For Adf And 2.89% For Crude Protein. These Results Confirm That It Is Possible To Calibrate, Validate, And Transfer (Nirs) Equations And Data Among Laboratories For The Accurate Determination Of Adf And Crude Protein, And Thereby Demonstrate That Nirs Can Be Used As A Standard Method For The Analysis Of Forages. The Method Has Been Adopted Official First Action

Diet quality and liveweight gain of steers grazing Leucaena - grass pasture estimated with faecal near infrared reflectance spectroscopy (F.NIRS)

2008 ◽  
Vol 48 (7) ◽  
pp. 835 ◽  
Author(s):  
R. M. Dixon ◽  
D. B. Coates
Keyword(s):  
Diet Quality ◽  
Crude Protein ◽  
Protein Concentration ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Additional Reference

Three drafts of Bos indicus cross steers (initially 178–216 kg) grazed Leucaena–grass pasture [Leucaena leucocephala subspecies glabrata cv. Cunningham with green panic (Panicum maximum cv. trichoglume)] from late winter through to autumn during three consecutive years in the Burnett region of south-east Queensland. Measured daily weight gain (DWGActual) of the steers was generally 0.7–1.1 kg/day during the summer months. Estimated intakes of metabolisable energy and dry matter (DM) were calculated from feeding standards as the intakes required by the steers to grow at the DWGActual. Diet attributes were predicted from near infrared reflectance spectroscopy spectra of faeces (F.NIRS) using established calibration equations appropriate for northern Australian forages. Inclusion of some additional reference samples from cattle consuming Leucaena diets into F.NIRS calibrations based on grass and herbaceous legume–grass pastures improved prediction of the proportion of Leucaena in the diet. Mahalanobis distance values supported the hypothesis that the F.NIRS predictions of diet crude protein concentration and DM digestibility (DMD) were acceptable. F.NIRS indicated that the percentage of Leucaena in the diet varied widely (10–99%). Diet crude protein concentration and DMD were usually high, averaging 12.4 and 62%, respectively, and were related asymptotically to the percentage of Leucaena in the diet (R2 = 0.48 and 0.33, respectively). F.NIRS calibrations for DWG were not satisfactory to predict this variable from an individual faecal sample since the s.e. of prediction were 0.33–0.40 kg/day. Cumulative steer liveweight (LW) predicted from F.NIRS DWG calibrations, which had been previously developed with tropical grass and grass–herbaceous legume pastures, greatly overestimated the measured steer LW; therefore, these calibrations were not useful. Cumulative steer LW predicted from a modified F.NIRS DWG calibration, which included data from the present study, was strongly correlated (R2 = 0.95) with steer LW but overestimated LW by 19–31 kg after 8 months. Additional reference data are needed to develop robust F.NIRS calibrations to encompass the diversity of Leucaena pastures of northern Australia. In conclusion, the experiment demonstrated that F.NIRS could improve understanding of diet quality and nutrient intake of cattle grazing Leucaena–grass pasture, and the relationships between nutrient supply and cattle growth.

Application of Near Infrared Reflectance Spectroscopy to Compositional Analysis of Commercial Pig Feed Mixes

1987 ◽  
Vol 70 (3) ◽  
pp. 420-423
Author(s):  
Shih-Ling Y Chen ◽  
Ali Hsu ◽  
Mane-Lane Lee
Keyword(s):  
Near Infrared ◽  
Screening Method ◽  
Compositional Analysis ◽  
Correlation Coefficients ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Pig Feed

Abstract Official samples of commercial pig feed mixes taken for routine inspection were analyzed by near infrared reflectance spectroscopy (NIRS). Separate calibrations were established for pre-starting pig, starting pig, growing pig, finishing pig, and lactating sow feeds. Mean correlation coefficients and standard errors of calibration, respectively, obtained by comparing NIRS values with conventional chemical analysis values, were as follows: calibration set (about 35 samples per category)-moisture 0.87,0.44%; protein 0.90,0.75%; fiber 0.8b, 0.49%; analytical set (about 15 samples per category)—moisture 0.73, 0.42%; protein 0.90, 0.72%; fiber 0.83, 0.40%. Mean coefficients of variation for NIRS were moisture 3.9%, protein 4.2%, and fiber 14.8%; those for conventional analyses were moisture 2.7%, protein 1.1%, and fiber 11.7%. The results indicated that for moisture, protein, and fiber determinations in commercial pig feed mix products with various and unknown formulations, successful rapid NIRS analysis could be achieved by using a filter-type spectrometer and advanced mathematical data treatments. Among 246 samples inspected, protein content in 70% of them exceeded by 1-8% the legal minimum protein level. Application of NIRS provides an accurate and prompt on-line technique for feed mix analysis during the formulation process and can be beneficial to the operation of feed mills. For official inspection, the technique might serve as a preliminary screening method.

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