Population affinity estimation on a Spanish sample: testing the validity and accuracy of cranium and mandible online software methods

Katie S Sieber, Julieta Gómez García-Donas (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

Abstract

Population affinity estimation is an important step in the identification of unknown individuals. To ensure accurate results, validation studies of newly developed methods must be performed using different target populations and skeletal elements. This research aims to determine the accuracy and reliability of population affinity estimation on a modern Spanish sample using two online software applications.

The sample consists of 114 adult individuals (51 males, 63 females) using 38 measurements and one angle from the skull and mandible. AncesTrees was used for craniometric measurements and (hu)MANid for mandibular variables with different classification models and probability thresholds being evaluated. The required parameters were inputted for each individual and statistics were generated to assess the accuracy of the estimation.

AncesTrees performed with the greatest accuracy as the program correctly classified the sample as Southwestern European or European, with highest accuracies being 54.56% (trial 1), 86.05% (trial 2), 82.61% (trial 3), 34.55% (trial 4) and 100.00% (trial 5). (hu)MANid correctly classified the sample as being from white origin with accuracies ranging from 70.59 to 80.00% without considering correct sex estimation, while accuracy ranged between 62.75 and 80.00% accounting for estimated sex.

Population affinity estimation may determine subsequent methods used in the construction of the biological profile. Our results demonstrate varying accuracy rates depending on the element and method, offering a critical view in relation to software applicability and validity. Reference populations and intrinsic and extrinsic factors can potentially influence the method accuracy and reliability. Future research should focus on the inclusion of underrepresented groups.

Original languageEnglish
Article number102180
JournalLegal Medicine
Early online date23 Nov 2022
DOIs
Publication statusE-pub ahead of print - 23 Nov 2022

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