scholarly journals Nutritive evaluation of herbage from permanent meadows by near-infrared reflectance spectroscopy: 2. Prediction of crude protein and dry matter degradability

2005 ◽  
Vol 85 (9) ◽  
pp. 1572-1579 ◽  
Author(s):  
Sonia Andrés ◽  
Alfredo Calleja ◽  
Secundino López ◽  
Ángel R Mantecón ◽  
F Javier Giráldez
Keyword(s):  
Crude Protein ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

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

Near-infrared reflectance spectroscopy for estimating food intake in housed dairy cows

1999 ◽  
Vol 1999 ◽  
pp. 93-93
Author(s):  
Y. Unal ◽  
P. C. Garnsworthy
Keyword(s):  
Dairy Cows ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Dry Matter Intake ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Faecal Samples

Dry matter intake (DMI) is a major limitation to milk production in dairy cows, but is difficult to measure under commercial conditions where cows are housed and fed in groups. Several methods have been developed to estimate DMI by individual cows, such as using inert markers, where dual markers can be used to predict digestibility and faecal output simultaneously. However, their scope is limited by the laboratory analyses required and there are problems with marker dosing and recovery. Predictions of DMI by near-infrared reflectance spectroscopy (NIRS) have been reported, but they have been based on scanning forage samples to predict intake potential. Since DMI is a function of the animal as well as the diet, it is more logical to scan samples of faeces when predicting individual intakes. The objective of this study was to see whether NIRS could accurately predict DMI from faecal samples of individual cows.

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

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