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A series of developmental validation tests for Number of Contributors platforms: Exemplars using NOCIt and a neural network

NCJ Number
Forensic Science International: Genetics Volume: 54 Dated: September 2021 Pages: 102556
Date Published
September 2021
J. Valtl; U. J. Monich; D. S. Lun; J. Kelley; C. M.Grgicak

A vast array of constructs, coupled with a gap in standardized methods by which to validate systems for determining the number of contributors (NOC) to a DNA sample, warrants the current exploration into the measures by which differing NOC systems might be tested for operations.


Complex DNA mixtures are challenging to interpret and require computational tools that aid in that interpretation. Recently, several computational methods that estimate the number of contributors (NOC) to a sample have been developed. Unlike analogous tools that interpret profiles and report LRs, NOC tools vary widely in their operational principle, with some being Bayesian and others being machine learning tools. Conjunctionally, NOC tools may return a single n estimate, or a distribution on n. The current project used two exemplar NOC systems: a probabilistic system named NOCIt, which renders an a posteriori probability (APP) distribution on the number of contributors given an electropherogram and an artificial neural network (ANN). NOCIt is a continuous Bayesian inference system incorporating models of peak height, degradation, differential degradation, forward and reverse stutter, noise and allelic drop-out while considering allele frequencies in a reference population. The ANN is also a continuous method, taking all the same features (barring degradation) into account. Unlike its Bayesian counterpart, it demands substantively more data to parameterize, requiring synthetic data. Each system’s performance was determined by conducting tests on 214 PROVEDIt mixtures where the limit of detection was 1-copy of DNA. This procedure found that after a lengthy training period of approximately 24 h, the ANN’s evaluation process was very fast and perfectly repeatable. In contrast, NOCIt only took a few minutes to train but took tens of minutes to complete each sample and was less repeatable. In addition, it rendered a probability distribution that was more sensitive and specific, affording a reasonable method by which to report all reasonable n that explain the evidence for a given sample. Whatever the method, by acknowledging the inherent differences between NOC systems, this project demonstrated that validation constructs will necessarily be guided by the needs of the forensic domain and be dependent upon whether the laboratory seeks to assign a single n or range of n. (publisher abstract modified)

Date Created: September 21, 2021