Short Communication: Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs

2011 ◽  
Vol 91 (2) ◽  
pp. 301-304 ◽  
Author(s):  
R. T. Zijlstra ◽  
M. L. Swift ◽  
L. F. Wang ◽  
T. A. Scott ◽  
M. J. Edney
Keyword(s):  
Near Infrared ◽  
Energy Content ◽  
External Validation ◽  
Reflectance Spectroscopy ◽  
Commercial Application ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Digestible Energy ◽  
Infrared Reflectance Spectroscopy

Zijlstra, R. T., Swift, M. L., Wang, L. F., Scott, T. A. and Edney, M. J. 2011. Short Communication:Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs. Can. J. Anim. Sci. 91: 301–304. Density, chicken apparent metabolizable energy (AME), and near infrared reflectance spectroscopy (NIRS) were tested to predict the widely varying swine digestible energy (DE) content of barley. The DE content of 39 barley samples ranged from 2686 to 3163 kcal kg−1 (90% DM) in grower pigs. The R2 between DE content and density (0.14) and broiler chicken AME content (0.18 and 0.56, without and with enzyme, respectively) was low. In contrast, the coefficient of determination to predict swine DE content for ground barley samples using NIRS was respectable for external validation (R2=0.74) and internal cross validation (1-VR=0.79), but more robust calibrations should be developed for commercial application.

scholarly journals The use of near-infrared reflectance spectroscopy (NIRS) to predict dairy fibre feeds in vitro digestibility

2022 ◽  
Vol 951 (1) ◽  
pp. 012100
Author(s):  
R. Zahera ◽  
L.A. Sari ◽  
I.G. Permana ◽  
Despal
Keyword(s):  
Near Infrared ◽  
External Validation ◽  
Cost Effective ◽  
Reflectance Spectroscopy ◽  
In Vitro Digestibility ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Abstract Information on dairy fibre feed digestibility is important in ration formulation to better predict dairy cattle performance. However, its measurement takes time. Near-infrared reflectance spectroscopy (NIRS) is a rapid, precise, and cost-effective method to predict nutrient value, such as chemical content and digestibility of feedstuffs. This study aims to develop a database for an in vitro digestibility prediction model using NIRS, including dry matter digestibility (DMD), neutral and acid detergent fibre digestibility (NDFD and ADFD), and hemicellulose digestibility (HSD). Eighty dietary fibre feeds consisting of Napier grass, natural grass, rice straw, corn stover, and corn-husk were collected from four dairy farming areas in West Java (Cibungbulang District of Bogor Regency, Parung Kuda District of Sukabumi Regency, Pangalengan District of Bandung Regency, and Lembang District of West Bandung Regency). The spectrum for each sample was collected thrice using NIRSflex 500, which was automatically separated by 2/3 for calibration and 1/3 for validation. External validation was conducted by measuring 20 independent samples. Calibration and validation models were carried out by NIRCal V5.6 using the partial least squares (PLS) regression. The results showed that all parameters produce r2 > 0.5 except for ADFD. Relative prediction deviation (RPD) > 1.5 was only found in hemicellulose digestibility prediction. RPL (SEP/SEL) <1.0 were found in DMD and hemicellulose digestibility. It is concluded that hemicellulose digestibility can be predicted using NIRS accurately while other parameters need improvement.

The use of near infrared reflectance spectroscopy (NIRS) for the prediction of the digestible energy content of barley for growing pigs

2003 ◽  
Vol 2003 ◽  
pp. 153-153
Author(s):  
M. E. E. McCann ◽  
K. J. McCracken ◽  
R. E. Agnew
Keyword(s):  
Chemical Composition ◽  
Nutritive Value ◽  
Near Infrared ◽  
Energy Content ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Feed Ingredient ◽  
Near Infrared Reflectance ◽  
Digestible Energy

It is not possible to carry out in vivo pig digestibility studies on each feed or feed ingredient therefore there is a need for a rapid means of predicting the digestible energy content of a feed or feed ingredient. Near infrared reflectance spectroscopy (NIRS) is an extremely rapid technique and has been used to predict chemical composition and nutritive value for a wide range of feeds and feed ingredients (Leeson et al 2000). In the literature, some workers have reported that NIRS has a high degree of accuracy for determining chemical composition and nutritive value while others have reported a lower degree of accuracy. The aim of the current study was to examine the value of NIRS in predicting the digestible energy (DE) content of barley from which pig diets were formulated.

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.

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