Machine learning applications in forensic DNA profiling: A critical review

Inkjet printer prediction under complicated printing conditions based on microscopic image features

A novel technique is introduced to predict the printer model used to produce a given document. Samples containing only a few letters printed under varying conditions (i.e., different printing modes, letter types, fonts) were collected to establish a dataset of 41 inkjet printer models from common manufacturers, such as HP, Canon, and Epson. Morphological features were analyzed by extraction of image features using several algorithms in a series of microscopic images and a Wilcoxon test was used to measure the significance of variations between printed samples. Significant differences between various printing conditions might post potential challenge to questioned document examination. Discriminant analysis and the k-nearest neighbor (KNN) algorithm were also employed for source printer prediction under varying printing condition on 30% images with the rest images as training dataset. The results of a validation experiment demonstrated that while quadratic discriminant analysis (QDA) achieved an accuracy of 96.3%, a combination of KNN and QDA reached 98.6%. As such, this technique could aid in the forensic examination of printed documents.

Optimizing Analytical Thresholds for Low-Template DNA Analysis: Insights from Multi-Laboratory Negative Controls

When analyzing challenging samples, such as low-template DNA, analysts aim to maximize information while minimizing noise, often by adjusting the analytical threshold (AT) for optimal results. A potential approach involves calculating the AT based on the baseline signal distribution in electrophoresis results. This study investigates the impact of reagent kits, testing quarters, environmental conditions, and amplification cycles on baseline signals using historical records and experimental data on low-template DNA. Variations in these aspects contribute to differences in baseline signal patterns. Analysts should remain vigilant regarding routine instrument maintenance and reagent replacement, as these may affect baseline signals. Prompt analysis of baseline status and tailored adjustments to ATs under specific laboratory conditions are advised. A comparative analysis of published methods for calculating the optimal AT from a negative signal distribution highlighted the efficiency of utilizing baseline signals to enhance forensic genetic analysis, with the exception of extremely low-template samples and high-amplification cycles. Moreover, a user-friendly program for real-time analysis was developed, enabling prompt adjustments to ATs based on negative control profiles. In conclusion, this study provides insights into baseline signals, aiming to enhance genetic analysis accuracy across diverse laboratories. Practical recommendations are offered for optimizing ATs in forensic DNA analysis.