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Stewart SM, Corlett MT, Gardner GE, Ura A, Nishiyama K, Shibuya T, McGilchrist P, Steel CC, Furuya A. Validation of a handheld near-infrared spectrophotometer for measurement of chemical intramuscular fat in Australian lamb. Meat Sci 2024; 214:109517. [PMID: 38696994 DOI: 10.1016/j.meatsci.2024.109517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/04/2024]
Abstract
The objective of the study was to independently validate a calibrated commercial handheld near infrared (NIR) spectroscopic device and test its repeatability over time using phenotypically diverse populations of Australian lamb. Validation testing in eight separate data sub-groups (n = 1591 carcasses overall) demonstrated that the NIR device had moderate precision (R2 = 0.4-0.64, RMSEP = 0.70-1.22%) but fluctuated in accuracy between experimental site demonstrated by variable slopes (0.50-0.94) and biases (-0.86-0.02). The repeatability experiment (n = 10 carcasses) showed that time to scan post quartering affected NIR measurement from 0 to 24 h (P < 0.001). On average, NIR IMF% was 0.97% lower (P < 0.001) at 24 h (4.01% ± 0.166), compared to 0 h. There was no difference (P > 0.05) between Time 0 and 1 h or Time 0 and 4 h or between replicate scans within each time point. This study demonstrated the SOMA NIR device could predict lamb chemical IMF% with moderate precision and accuracy, however additional work is required to understand how loin preparation, blooming and surface hydration affect NIR measurement.
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Affiliation(s)
- S M Stewart
- Advanced Livestock Measurement Technologies (ALMTech) Project, Murdoch University, School of Agriculture, Western Australia 6150, Australia.
| | - M T Corlett
- Advanced Livestock Measurement Technologies (ALMTech) Project, Murdoch University, School of Agriculture, Western Australia 6150, Australia
| | - G E Gardner
- Advanced Livestock Measurement Technologies (ALMTech) Project, Murdoch University, School of Agriculture, Western Australia 6150, Australia
| | - A Ura
- SOMA Optics, Ltd., Tokyo 190-0182, Japan
| | | | - T Shibuya
- Fujihira Industry Co., Ltd. (FHK), Tokyo 113-0033, Japan
| | - P McGilchrist
- Universiy of New England, School of Environmental and Rural Sciences, Armidale, NSW 2350, Australia
| | - C C Steel
- Universiy of New England, School of Environmental and Rural Sciences, Armidale, NSW 2350, Australia
| | - A Furuya
- Fujihira Industry Co., Ltd. (FHK), Tokyo 113-0033, Japan
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Stewart SM, Polkinghorne R, Pethick DW, Pannier L. Carcass assessment and value in the Australian beef and sheepmeat industry. Anim Front 2024; 14:5-14. [PMID: 38633318 PMCID: PMC11018706 DOI: 10.1093/af/vfae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Affiliation(s)
- Sarah M Stewart
- School of Agriculture, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, Perth 6150, Australia
| | | | - David W Pethick
- School of Agriculture, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, Perth 6150, Australia
| | - Liselotte Pannier
- School of Agriculture, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, Perth 6150, Australia
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Nisbet H, Lambe N, Miller G, Doeschl-Wilson A, Barclay D, Wheaton A, Duthie CA. Using in-abattoir 3-dimensional measurements from images of beef carcasses for the prediction of EUROP classification grade and carcass weight. Meat Sci 2024; 209:109391. [PMID: 38043328 DOI: 10.1016/j.meatsci.2023.109391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 11/01/2023] [Accepted: 11/06/2023] [Indexed: 12/05/2023]
Abstract
Imaging technology can aid the automatic extraction of measurements from beef carcasses, which can be used for objective grading. Many abattoirs, however, rely on manual grading due to the required infrastructure and cost, making technology unfeasible. This study explores 3-dimensional (3D) imaging technology, requiring limited infrastructure, and its ability to predict carcass weight, conformation class and fat class for non-invasive, objective classification. Time-of-flight near-infrared cameras captured 3-dimensional point clouds of beef carcasses, on-line in one commercial abattoir in Scotland, over a 6-month period. Thirty-five 3D images were captured per carcass and processed using machine vison software. Seventy-four measurements were extracted from each point cloud. Removal of extreme outliers resulted in 285,109 datapoints for 17,250 carcasses. Coefficients of variation (CV) for each measurement on a per-animal basis were low and consistent, and measurements were averaged across images. Using a training and validation dataset (70:30), multiple linear regression models predicted EUROP conformation class, fat class, and carcass weight. Stepwise models included fixed effects (sex, breed type, kill date (and cold carcass weight for conformation and fat class)), and 3D image measurements. Including 3D measurements resulted in prediction accuracies of 70%, 50% and 23% for cold carcass weight, conformation, and fat class respectively. Mapping predictions on the traditional EUROP grid used in the UK showed that 99% of conformation classes and 93% of fat classes were classified within the correct or neighbouring grade. The results of this study indicate the potential for non-invasive, in-abattoir technology requiring limited infrastructure to predict carcass traits objectively.
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Affiliation(s)
- Holly Nisbet
- Scotland's Rural College, West Mains Road, Edinburgh, UK; The Roslin Institute, University of Edinburgh, Easter Bush, UK.
| | - Nicola Lambe
- Scotland's Rural College, West Mains Road, Edinburgh, UK
| | - Gemma Miller
- Scotland's Rural College, West Mains Road, Edinburgh, UK
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Lobo AAG, Cônsolo NRB, Dias J, Menezes ACB, Martins T, Silva J, Machado FS, Marcondes MI, Pflanzer SB, Nassu RT, Scheffler TL, Chizzotti ML. Short Communication: 'The use of dual energy x-ray absorptiometry (DXA)' to predict the veal carcass composition. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.105104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Zhang Z, Li X, Tian J, Chen J, Gao G. A review: Application and research progress of bioimpedance in meat quality inspection. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ziyi Zhang
- Beijing Laboratory of Food Quality and Safety, College of Information and Electrical Engineering China Agricultural University Beijing People's Republic of China
| | - Xinxing Li
- Beijing Laboratory of Food Quality and Safety, College of Information and Electrical Engineering China Agricultural University Beijing People's Republic of China
| | - Jianjun Tian
- College of Food Science and Engineering Inner Mongolia Agricultural University Hohhot People's Republic of China
| | - Jing Chen
- School of Logistics Beijing Wuzi University Beijing People's Republic of China
| | - Ge Gao
- School of Logistics Beijing Wuzi University Beijing People's Republic of China
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Payne CE, Anderson F, Pannier L, Pethick DW, Gardner GE. Bone mineral concentration predicted by dual energy X-ray absorptiometry and its relationship with lamb eating quality. Meat Sci 2021; 186:108725. [PMID: 35078013 DOI: 10.1016/j.meatsci.2021.108725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 12/09/2021] [Accepted: 12/15/2021] [Indexed: 11/29/2022]
Abstract
Lumbar bone mineral concentration, as predicted by dual energy x-ray absorptiometry (DEXA), may reflect changes in lamb maturity and eating quality. New season (n = 60) and old season (n = 60) lambs were slaughtered and DEXA scanned at a commercial abattoir across 2 kill groups. The second lumbar vertebra was isolated from the spine for determination of calcium, phosphorus, and magnesium concentration (mg/g). The loin and rack cuts were collected for consumer sensory grilling and roasting analyses. Mineral concentration was significantly higher in old season lambs within kill group 1 (P < 0.05). DEXA was a positive predictor of phosphorus and calcium concentration, but only when DEXA lean % (P < 0.05) was included in the model. Calcium and phosphorus were significant positive predictors of overall liking scores (P < 0.05), but only for the rack roast. These effects became insignificant when DEXA lean % was included. These results suggest that DEXA values likely reflect changes in both DEXA lean % and bone minerals, and that DEXA lean % was the driver of eating quality, rather than maturity.
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Affiliation(s)
- C E Payne
- Australian Cooperative Research Centre for Sheep Industry Innovation, Australia; Murdoch University, College of Science, Health, Engineering and Education, Western Australia 6150, Australia; Department of Primary Industries and Regional Development, Western Australia 6151, Australia.
| | - F Anderson
- Murdoch University, College of Science, Health, Engineering and Education, Western Australia 6150, Australia
| | - L Pannier
- Murdoch University, College of Science, Health, Engineering and Education, Western Australia 6150, Australia
| | - D W Pethick
- Australian Cooperative Research Centre for Sheep Industry Innovation, Australia; Murdoch University, College of Science, Health, Engineering and Education, Western Australia 6150, Australia
| | - G E Gardner
- Australian Cooperative Research Centre for Sheep Industry Innovation, Australia; Murdoch University, College of Science, Health, Engineering and Education, Western Australia 6150, Australia
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Leighton PL, Segura JD, Lam SD, Marcoux M, Wei X, Lopez-Campos OD, Soladoye P, Dugan ME, Juarez M, PRIETO NURIA. Prediction of carcass composition and meat and fat quality using sensing technologies: A review. MEAT AND MUSCLE BIOLOGY 2021. [DOI: 10.22175/mmb.12951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Consumer demand for high-quality healthy food is increasing, thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.
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Gardner GE, Apps R, McColl R, Craigie CR. Objective measurement technologies for transforming the Australian & New Zealand livestock industries. Meat Sci 2021; 179:108556. [PMID: 34023677 DOI: 10.1016/j.meatsci.2021.108556] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 01/04/2023]
Abstract
This paper introduces the special edition of Meat Science focused upon the development, calibration and validation of technologies that measure traits influencing meat eating quality, or carcass fat and lean composition. These papers reflect the combined research efforts of groups in Australia, through the Advanced Livestock Measurement Technologies project, and New Zealand through AgResearch. We describe the various technologies being developed, how these devices are being trained upon common gold-standard measurements, and how their outputs are being simultaneously integrated into existing industry systems. We outline how this enhances the industry uptake and adoption of these technologies, and how this is further accelerated by education programs and strategic industry investment into their commercialisation.
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Affiliation(s)
- G E Gardner
- Murdoch University, School of Veterinary & Life Sciences, Western Australia 6150, Australia.
| | - R Apps
- Meat and Livestock Australia, North Sydney, NSW 2060, Australia
| | - R McColl
- Meat Industry Association of New Zealand, 154 Featherston Street, Wellington 6011, New Zealand
| | - C R Craigie
- AgResearch Limited, 1365 Springs Road, Lincoln 7674, New Zealand
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