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.

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.

Near Infrared Reflectance Spectroscopy Coupled to Chemometrics as a Cost-Effective, Rapid and Non-Destructive Tool for Fish Fraud Control: Monitoring Source, Condition and Nutritional Value of Five Common Whitefish Species

2020 ◽  
Author(s):  
Diogo B Gonçalves ◽  
Carla S P Santos ◽  
Teresa Pinho ◽  
Rafael Queirós ◽  
Pedro D Vaz ◽  
...  
Keyword(s):  
Near Infrared ◽  
Atlantic Cod ◽  
Cost Effective ◽  
Reflectance Spectroscopy ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Abstract Fish fraud is a problematic issue for the industry that to be properly addressed requires the use of accurate, rapid and cost-effective tools. In this work, near infrared reflectance spectroscopy (NIRS) was used to predict nutritional values (protein, lipids and moisture) as well as to discriminate between source (farmed vs. wild fish) and condition (fresh, defrosted or frozen fish). Five whitefish species consisting of Alaskan pollock (Gadus chalcogrammu), Atlantic cod (Gadus morhua), European plaice (Pleuronectes platessa), Common sole (Solea solea) and Turbot (Psetta maxima), including farmed, wild, fresh and frozen ones, were scanned by a low-cost handheld near infrared reflectance spectrometer with a spectral range between 900 nm and 1700 nm. Several machine learning algorithms were explored for both regression and classification tasks, achieving precisions and coefficient of determination higher than 88% and 0.78, respectively. Principal component analysis (PCA) was used to cluster samples according to classes where good linear discriminations were denoted. Loadings from PCA reveal bands at 1150, 1200 and 1400 nm as the most discriminative spectral regions regarding classification of both source and condition, suggesting the absorbance of OH, CH, CH2 and CH3 groups as the most important ones. This study shows the use of NIRS and both linear and non-linear learners as a suitable strategy to address the fish fraud problematic and fish quality control.

Broad-based calibrations of in vitro gas production of forages by near-infrared reflectance spectroscopy

1998 ◽  
Vol 22 ◽  
pp. 234-237
Author(s):  
M. Herrero ◽  
N. S. Jessop
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Gas Production ◽  
Grass Species ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Wide Range

There is increasing demand to obtain fast and accurate dynamic nutritional information from forages. Near-infrared reflectance spectroscopy (NIRS) offers the possibility for obtaining such information for a range of nutritional constituents of foods. Herrero et al. (1996 and 1997) calibrated in vitro gas production measurements of a single grass species by NIRS. There would be greater practical benefit if the gas production predictions could be obtained using calibrations derived from a wide range of plant species, since a single equation could be used for all forages. The objective of this study was to investigate if in vitro gas production measurements of a broad based sample population could be calibrated by NIRS.

Predicting maize silage starch degradability by near infrared reflectance spectroscopy

2003 ◽  
Vol 2003 ◽  
pp. 50-50 ◽  
Author(s):  
D.K. Lovett ◽  
E.R. Deaville ◽  
D.I. Givens ◽  
E. Owen
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Maize Silage ◽  
Near Infrared Reflectance Spectroscopy ◽  
Limited Sample ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Maize silage consists of a starch and a fibrous fraction, both of which should be considered when assessing nutritive value. The in vitro evaluation of starch disappearance is laborious and costly. The near infrared reflectance spectroscopy (NIRS) technique requires limited sample preparation and is quick to operate once a calibration is established. This study investigated the potential of NIRS to predict maize starch disappearance in vitro.

QUALITY OF ALFALFA HERBAGE ESTIMATED BY A PREPARED CELLULASE SOLUTION AND NEAR INFRARED REFLECTANCE SPECTROSCOPY

1989 ◽  
Vol 69 (3) ◽  
pp. 833-839 ◽  
Author(s):  
S. S. BUGHRARA ◽  
D. A. SLEPER ◽  
R. L. BELYEA ◽  
G. C. MARTEN
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
In Vitro Digestibility ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Wide Range ◽  
Medicago Sativa L

Little information is available on estimating in vitro dry matter digestibility (IVDMD) of alfalfa (Medicago sativa L.) herbage by a prepared cellulase solution (PCS) and then using these IVDMD estimates to calibrate near infrared reflectance spectroscopy (NIRS) equations. Objectives were to compare PCS digestion to that by two rumen fermentation procedures, including true in vitro digestibility (TIVD), and develop NIRS equations to estimate TIVD, neutral detergent fiber, and acid detergent fiber of alfalfa hay. Seventy-eight alfalfa samples, having a wide range in herbage quality, were analyzed for IVDMD using five different PCS procedures and two rumen fermentation procedures (true and apparent in vitro digestibility). The best NIRS calibration equation for TIVD had R2 of 0.92 and a standard error of selection of 20.7 g kg−1. Correlations between IVDMD and TIVD obtained by the various PCS assays ranged from 0.91 to 0.96 (P < 0.01), with regression coefficients ranging from 0.94 to 0.98. We concluded that PCS gave rapid and accurate estimates of TIVD and that NIRS could accurately estimate TIVD of a wide range of alfalfa herbage quality.Key words: Acid detergent solubles, fungal cellulase solubles, in vitro digestible dry matter, Medicago sativa L., neutral detergent solubles, alfalfa

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