Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy

Stuth, Jerry W.; Jama, Abdi Adan; Tolleson, Douglas R.

doi:10.1016/s0378-4290(03)00140-0

Cited by 131 publications

(116 citation statements)

References 39 publications

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“…But at industrial level, these methods are not fitted in the scheme and could not serve the purpose of screening or monitoring of quality parameters of each product. Near infra-red spectroscopy (NIRS) provides an alternative, non-destructive technology for measuring constituents of biological materials with little sample preparation and is able to provide reliable and accurate results of larger range of samples of multiple properties at one time (Stuth et al, 2003).…”

Section: Issn: 2319-7706 Volume 7 Number 01 (2018)mentioning

confidence: 99%

Comparative Efficiency of Conventional and NIR Based Technique for Proximate Composition of Pigeon Pea, Soybean and Rice Cultivars

Singh¹,

Patel²,

Litoria³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

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Section: Issn: 2319-7706 Volume 7 Number 01 (2018)mentioning

confidence: 99%

Comparative Efficiency of Conventional and NIR Based Technique for Proximate Composition of Pigeon Pea, Soybean and Rice Cultivars

Singh¹,

Patel²,

Litoria³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

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“…According to Stuth et al (2003), there is increasing interest in NIR calibration of in vivo digestibility values as it allows the elimination of the intermediate laboratory techniques for predicting forage energy value. However, little has been published in the literature on the prediction of nutritional forage value by NIRS prediction of feed value of temperate forages NIRS, possibly because large databases with in vivo digestibility values conducted under standardized conditions are required.…”

Section: Nir Spectral Regionsmentioning

confidence: 99%

NIRS prediction of the feed value of temperate forages: efficacy of four calibration strategies

Andueza

Picard

Jestin

et al. 2011

Animal

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Near infrared reflectance spectroscopy (NIRS) of 924 fresh temperate forages were used to develop calibration models for chemical composition -crude ash (CA) and crude protein (CP) -organic matter digestibility (OMD) and voluntary intake (VI). We used 110 samples to assess the models. Four calibration strategies for determining forage quality were compared: (i) speciesspecific calibration, (ii) family-specific calibration, (iii) a global procedure and (iv) a local approach. Forage calibration data sets displayed CA values ranging from 52 to 205 g/kg of dry matter (DM), CP values from 50 to 280 g/kg DM, OMD values from 0.48 to 0.85 g/g and VI values from 22.5 to 115.2 g DM/kg metabolic body weight (BW 0.75 ). The calibration models performed well for all the variables except for VI. For CA, local procedure showed lower standard error of prediction (SEP) than species-specific, family-specific or global models. For CP, the calibration models all showed similar SEP values (11.13, 11.08, 11.38 and 11.34 g/kg DM for species-specific, family-specific, global and local approaches). For OMD, the local procedure gave a similar SEP (0.024 g/g) to specific species and global procedures (0.027 g/g) and a lower SEP than the family-specific approach (0.028 g/g). For VI, the local approach and species-specific calibration showed lower SEP (7.08 and 7.16 g/kg BW 0.75 ) than the broad-based calibrations (8.09 and 8.34 g/kg BW 0.75 for family-specific model and global procedure, respectively). Local calibration may thus offer a practical way to develop robust universal equations for animal response determinations.Keywords: chemical composition, in vivo organic matter digestibility, voluntary intake, near infrared reflectance spectroscopy ImplicationsThis study evaluates the utility of different near infrared reflectance spectroscopy (NIRS) calibration methods for predicting the feed value of fresh forages. We compare an alternative local approach with the more classical global approach. The local approach conserves all the advantages of NIRS technology (rapidity, non-contaminant technology and precision) and takes into account the number of samples and their diversity when large database are available. It overcomes the main limitation of NIRS, which is that the samples for prediction have to be close to those of the calibration population. The local approach could improve prediction of the nutritional value of feeds, and thus have important economic and environmental implications for animal nutrition. IntroductionRuminant performance is closely related to the digestibility and intake of feedstuffs. In most countries, knowledge of the feed value of forages is based on in vivo measurements made in castrated male sheep (Demarquilly et al., 1995 for a review). However, the large-scale application of in vivo digestibility trials for evaluating forage feed value is an expensive and time-consuming process that requires large amounts of feeds. As knowing these values is essential for controlled animal feeding, several methods have been d...

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“…a high degree of inho-mogeneity with respect to chemical composition of the subsets is advantageous (Stuth et al, 2003). Lovett et al (2004), however, studying the ability to predict GP kinetics of maize silage, iden tifi ed the inhomogeneity of maize samples, which contain both fi b rous and starchy fractions, as a main error source in prediction perfor mance of NIRS.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, the time need for in cuba tion and the necessity of rumen fl uid donor animals is a dis ad vantage. This obstacle might be overcome by the near infrared refl ectance spectroscopy (NIRS) technology, which has shown great potential to predict for age quality para meters in a rapid and non-destructive way (Stuth et al, 2003). For fo rage grasses, NIRS gave pro mising results in predicting static gas volumes, while for ki netic para meters re sults were less accurate (Herrero et al, 1997;An drés et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Prediction of gas production kinetics of maize stover and ear by near infrared reflectance spectroscopy

Kruse¹,

Herrmann²,

Loges³

et al. 2008

J. Anim. Feed Sci.

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This study was conducted to evaluate the potential of NIRS to predict gas production (GP) kinetics for maize ear and stover. Two approaches were applied to characterize GP kinetics: 1. describing GP profi les by fi tting an exponential model and 2. by calculating GP for various incubation intervals. In the fi rst approach, NIRS pre dic tion equations could explain a high proportion of variability related to the fermen ta tion rate (c), the asymptotic GP (A), and the lag phase (L) of ear samples in the calibration sub set (R²: 0.81-0.87), while for stover predictive ability was inferior. Validation showed satisfactory results only for the fermentation rate (R²: 0.81-0.88). In the se cond approach NIRS calibration was acceptable for GP in several incubation inter vals, but validation showed satisfactory results for intervals 3-7, 7-12, and 7-16 h of sto ver samples only (R²>0.74). Further research is required to elucidate potential sources of error before obtaining robust NIRS equations.

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Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy

Cited by 131 publications

References 39 publications

Comparative Efficiency of Conventional and NIR Based Technique for Proximate Composition of Pigeon Pea, Soybean and Rice Cultivars

Comparative Efficiency of Conventional and NIR Based Technique for Proximate Composition of Pigeon Pea, Soybean and Rice Cultivars

NIRS prediction of the feed value of temperate forages: efficacy of four calibration strategies

Prediction of gas production kinetics of maize stover and ear by near infrared reflectance spectroscopy

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