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Preliminary assessment of three quantitative approaches for estimating time-since-deposition from autofluorescence and morphological profiles of cell populations from forensic biological samples

NCJ Number
PLoS ONE Dated: 2023
Date Published

This study assesses quantitative approaches to estimate time-since-deposition from autofluorescence and morphological profiles of cell population.


This study investigates whether morphological and autofluorescence measurements of forensically-relevant cell populations generated with Imaging Flow Cytometry (IFC) can be used to predict the TSD of ‘touch’ or trace biological samples. Results showed that GBM and GLMM-LASSO showed the highest accuracy, with mean absolute error estimates in a hold-out test set of 29 and 21 days, respectively. Binary classifiers for these models correctly binned 94–96% and 98–99% of the age estimates as over/under 7 or 180 days, respectively. This suggests that predicted TSD using IFC measurements coupled to one or, possibly, a combination binary classification decision rules, may provide probative information for trace biological samples encountered during forensic casework. Three different prediction frameworks for estimating the number of day(s) for TSD were evaluated: the elastic net, gradient boosting machines (GBM), and generalized linear mixed model (GLMM) LASSO. Additionally, the authors transformed these continuous predictions into a series of binary classifiers to evaluate the potential utility for forensic casework. Determining when DNA recovered from a crime scene transferred from its biological source, i.e., a sample’s ‘time-since-deposition’ (TSD), can provide critical context for biological evidence. Yet, analytical techniques for TSD that are validated for forensic casework are lacking. (Published Abstract Provided)

Date Published: January 1, 2023