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In Preparation · 2026
SpeciesID: a bias-aware EM framework for quantitative species authentication in meat products
Abdul Malek, A.Z. et al.
·
In preparation
Abstract
SpeciesID implements a bias-aware expectation-maximization algorithm for quantitative species authentication from amplicon sequencing, achieving 2.59 percentage points mean absolute error and perfect detection accuracy across 54 simulated binary mixtures with trace detection at 0.5% at 500 reads per marker. Validated on 174 real amplicon samples from two independent studies.
Figures
Fig 1
SpeciesID analytical pipeline overview
Fig 2
Binary mixture quantification accuracy
Fig 3
Validation on real amplicon datasets
SpeciesID analytical pipeline overview
Binary mixture quantification accuracy
Validation on real amplicon datasets