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Open Access CC BY V4.0
Journal article
Accreditation of new technologies for predicting intramuscular fat percentage: Combining Bayesian models and industry rules for transparent decisions
PloS one, Vol.20(3), e0314714
2025
PMID: 40127377
Abstract
he experiment evaluated a method for statistically assessing the accuracy of technologies that measure intramuscular fat percentage (IMF%), enabling referencing against accreditation accuracy thresholds. To compare this method to the existing rules-based industry standard we simulated data for 4 separate devices that predicted IMF% across a range between 0.5-9.5% for sheep meat. These devices were simulated to reflect increasingly inaccurate predictions, and the two methods for statistically assessing accuracy were then applied. We found that for the technology which only just meets the accreditation accuracy standards, as few as 25 samples were required within each quarter of the IMF% range to achieve 80% likelihood of passing accreditation. In contrast, using the rules based approach at least 200 samples were required within each quarter of the IMF% range, and this increased the likelihood of passing to only 50%. This method has been developed into an on-line analysis App, which commercial users can freely access to test the accuracy of their technologies.
Details
- Title
- Accreditation of new technologies for predicting intramuscular fat percentage: Combining Bayesian models and industry rules for transparent decisions
- Authors/Creators
- Graham E. Gardner - Murdoch UniversityClair L. Alston-Knox - Predictive Analytics Group, Melbourne, VIC, Australia
- Publication Details
- PloS one, Vol.20(3), e0314714
- Publisher
- PubMed
- Identifiers
- 991005884952107891
- Copyright
- © 2025 Gardner, Alston-Knox.
- Murdoch Affiliation
- School of Agricultural Sciences
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.51 Dairy & Animal Sciences
- 3.51.206 Meat Quality
- Web Of Science research areas
- Food Science & Technology
- ESI research areas
- Agricultural Sciences