Use of dual-energy x-ray absorptiometry in non-ruminant nutrition research

Pomar, C.; Kipper, Marcos; Marcoux, M.

doi:10.1590/s1806-92902017000700010

Cited by 12 publications

(16 citation statements)

References 57 publications

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“…The studies with DXA have as main objective the determination of body or carcass traits that have importance for the meat industry [ 14 ] or animal nutrition [ 15 ]. Comparing the results of DXA studies is not easy because some works focus on predicting the composition based on the amount of chemical or dissected tissues rather than by the proportion, and this way the composition is powerfully described by the carcass weight and not by the DXA value.…”

Section: Imaging Techniques To Assess Carcass and Meat Quality In mentioning

confidence: 99%

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

Silva

Guedes

Rodrigues

et al. 2020

Foods

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In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.

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Section: Imaging Techniques To Assess Carcass and Meat Quality In mentioning

confidence: 99%

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

Silva

Guedes

Rodrigues

et al. 2020

Foods

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“…Finally, we compared the prediction equations we obtained from our data set with those published in the literature (Mitchell et al, 1998a;Mitchell et al, 1998c;Pomar et al, 2001;Mitchell et al, 2003;Pomar et al, 2017;Kipper et al, 2019b). To do this, we applied the published equation to the DXA values of our data set and computed the RMSE of the values predicted using the published equations and the chemically measured values from our study.…”

Section: Statistical Analysis Of Resultsmentioning

confidence: 99%

“…Medical imaging techniques such as DXA, CT and MRI not only enable repeated measurements of the same individual but also yield more reproducible results than dissections. Among medical imaging techniques, DXA has advantages that make it particularly attractive for application in animal science (Pomar et al, 2017), which mainly include financial and work security aspects. Compared to CT and MRI, DXA is easier to use, has lower instrument and operating costs, a more rapid scan speed, little to no operator bias and requires less image processing, consequently allowing for quick data analyses (Scholz et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the information from DXA devices does not fully correspond with the data from invasive techniques, and it is therefore necessary to adjust measurements to reference dissections or, ideally, chemical analyses via prediction equations (Mitchell et al, 1998b). Regression equations have been published for a range of breeds, sexes and weight classes of pigs, using different DXA beam technologies (Mitchell et al, 1998a;Mitchell et al, 1998c;Pomar et al, 2001;Mitchell et al, 2003;Suster et al, 2003;Pomar et al, 2017;Kipper et al, 2019b). The image-processing software on DXA devices used to compute tissue masses was developed for the human body.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

Kasper

Schlegel

Ruiz-Ascacibar

et al. 2020

Preprint

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Studies in animal science assessing nutrient and energy efficiency or determining nutrient requirements necessitate gathering exact measurements of body composition or body nutrient contents. Wet chemical analysis methods or standardized dissection are commonly applied, but both are destructive. Harnessing human medical imaging techniques for animal science can enable repeated measurements of individuals over time and reduce the number of individuals required for research. Dual-energy X-ray absorptiometry (DXA) is particularly promising due to its low acquisition and operating costs, low levels of radiation emission and simple image processing. However, the measurements obtained with DXA do not perfectly match dissections or chemical analyses, requiring the adjustment of the DXA via calibration equations. In principle, DXA results should be independent of animal size and body composition, because bone mineral content and the content of fat and lean tissue are derived from the attenuation of X-rays transmitted through the body. Several calibration regressions have been published, but comparative studies are pending. Thus, it is currently not clear whether existing regression equations can be directly used to convert DXA measurements into chemical values or whether each individual DXA device will require its own calibration for the animal’s breed, age, class or sex. Our study builds prediction equations that relate body composition to the content of single nutrients in growing entire male pigs (BW range 20–100 kg) as determined by both DXA and chemical analyses, and we present the accuracy of those predictions. Moreover, we show that the chemical composition of the empty body can be satisfactorily determined by DXA scans of carcasses. This opens up promising possibilities for the reduction of invasive procedures in the course of nutritional studies. Finally, we compare existing prediction equations for pigs of a similar range of body weights with the equations derived from our DXA measurements and evaluate their fit with our chemical analyses data. We found that existing equations for absolute contents that were built using the same DXA beam technology predicted our data more precisely than equations based on different technologies and percentages of fat and lean mass. This indicates that the creation of generic regression equations that yield reliable estimates of body composition in pigs of different growth stages, sexes and genetic breeds could be achievable in the near future. DXA may be a promising tool for high-throughput phenotyping for genetic studies, because it efficiently measures body composition in a large number and wide array of animals.ImplicationsThe ability to determine body composition non-invasively opens opportunities for improving studies of nutrition, nutrient balance and genetics. The present study contains regression equations to estimate the nutrient composition (energy, water, ash, Ca, P, CP, N and lipid) in the empty body of live pigs and in pig carcasses within a BW range from 20 to 100 kg using DXA. We present regression equations to estimate the EB composition directly from the carcass. This rapid and non-destructive method permits to reduce costs, time and number of animals needed for research, since the same individuals can be scanned repeatedly.

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“…Algorithms developed for humans were successfully adjusted with measurements on live sheep to yield high accuracy and precision in predicting carcass composition (R 2 > 0.90; Pearce et al, 2009). However, some limitations of DEXA include the need for extensive calibration with the animal species of interest, the loss of precision with tissue depth and the need of adjustments to predict chemical composition (Pomar et al, 2017).…”

Section: Body Compositionmentioning

confidence: 99%

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

Gonzalez

Kyriazakis

Tedeschi

2018

Animal

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A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data ‘as is’, further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.

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Use of dual-energy x-ray absorptiometry in non-ruminant nutrition research

Cited by 12 publications

References 57 publications

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

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