in-plant measurement of intramuscular fat (imf)
TRANSCRIPT
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Cameron Craigie,Science Impact leader – Meat products & Supply, AgResearch
In-plant measurement of Intramuscular fat (IMF)
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Project Team
AgResearchTricia Johnson, Kathryn McRae, Marlon Reis, Kevin Taukiri, Michelle Challies, Paul Shorten & Adam StuartAlliance Group LimitedSonja Lindsay, Johanna Phillips, Gemma Milne, Gary Maclennan, Mike Tate Other partnersNeville Jopson (Abacusbio), Paul Muir (On-Farm Research) Chris Logan (Lincoln University)
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Format
• Overview of IMF• Overview of Beef and Lamb NZ Genetics project and
results to date• Demo of ASD Trek instrument• Q&A
(A Workshop is interactive so please ask questions)
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Types of fat
• Removed from the carcass during processing:Internal
e.g. kidney fat
• Remains with the carcass during processing:Subcutaneous Inter-muscular Intra-muscularSeen on outside Between muscles Within the muscle
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Fat is influenced by
• Sex of lamb• Will be lower in entire males – less overall fat
• Age at slaughter – maturity• Late developing depot
• Slaughter weight• Relates to maturity
• Genetics• Has been shown to be highly heritable• Identified as research priority by Beef + Lamb New
Zealand Genetics
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Why are we interested in IMF?
• Key driver of meat palatability, directly linked to:• Flavor (fat soluble compounds and fat composition)• Tenderness• Juiciness
• IMF also key trait for resilience (as an energy reserve)But…Selection for lean meat yield using BVs for post-weaning growth, eye muscle depth and back fat is reducing IMF across the lamb carcass (Anderson et al. 2015 Animal 9:6 1081-1090)
RISK: Going too lean and reducing palatability
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So how can we measure it?
Beef and Lamb NZ Genetics project with Alliance Group Limited has been running for 18 months:Aim: To investigate the feasibility for real-time non-destructive prediction of meat tenderness, pH, colour, intramuscular fat content as of lamb loins at 24 hours post mortem in two commercial meat processing plants
• Proof-of-concept. HSI (a new technology) and NIRS (an existing technology).
• Test the performance and suitability of HSI and NIRS for the purposes of developing a meat-quality based decision support system.
• Develop prediction equations to convert spectral data into meat quality information (tenderness, IMF%, colour stability and pH).
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Sampling
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Imaging systems
• Visible-Near Infrared spectroscopy (NIRS) spectral range 350-2500 nm
• AgR instrument + Halo
• Hyperspectral imaging (HSI) spectral range 550-1650 nm. – similar principle to NIRS, but added advantage of a spatial dimension.
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Imaging systems
• Visible-Near Infrared spectroscopy (NIRS) spectral range 350-2500 nm
• AgR instrument + Halo
• Hyperspectral imaging (HSI) spectral range 550-1650 nm. – similar principle to NIRS, but added advantage of a spatial dimension.
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Hyperspectral imaging
37 cm
Hyperspectral camera
Sample
Light source
Detector
54 cm
37 cm
50°Speed (mm/s) = 11.1(~6 seconds to collect the following image)
Direction Movement
50 100 150 200 250 300
20
40
60
80
100
120
140
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Image processing
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Meat spectra of tissue types identified
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Process Spectra using an Multivariate calibration algorithm
Convert spectral data to predictions of IMF
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Summary of the MQ data in BLG spectral projectSpectral data + Meat Quality reference readings
Trait 2014-‐born 2015-‐born Total
pH 2517 2183 4700
IMF 1678 218 1896
Tenderness 1716 -‐
Colour 1830 -‐
MBS 906 -‐
Largest dataset of its kind ever collected
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Results to date
Distribution of IMF in 2014-born lambs
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Distribution of IMF% from 2014-born lambs (n = 1678)
0
5
10
15
20
25
30
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10
Percentage
IMF%
Mean IMF% = 2.64%
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Predicting IMF% using Hyperspectral imaging
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0 1 2 3 4 5 6 7 8 9 10
Predicted IMF %
Measured IMF %
Calibration
Validation
The validation data gives an R2 value of 0.75 and an RMSE of 0.407%
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Progress to date
• Initial indications are that NIR and Hyperspectral Imaging work for predicting IMF (and pH)
• We have proven the concept – We can measure IMF objectively and accurately
• Still to do: Technical challenges of implementing these technologies and integrating predicted meat quality results into value chain (e.g. SIL and other databases).
• So we are not there yet with in-plant measures, but it will happen.
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Demo of new hand-held instrument ASD TrekCurrently being evaluated by AgResearch Potential applications as an in-plant (or in-market) tool for non-invasive meat quality measurement
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Q&A
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Thank you.Cameron Craigie, PhDScience impact leader -‐ AgResearch 021 289 906307 838 [email protected]