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    Optimal methods for forensic hair analysis are often debated, especially regarding extraction parameters that include incubation time, temperature, and size of extracted hair particles. To assess hair pretreatment parameters for analysis of drugs of abuse, the statistical technique known as Design of Experiments (DoE) is useful. DoE evaluates both the individual roles and the combinatorial associations between multiple variables and overall drug extraction efficiency. Previous reports have focused on incorporated hair reference material (HRM), which is prepared in the lab at a specified drug concentration. In contrast, authentic HRM, which is prepared by diluting hair from drug users with blank hair to achieve specific drug concentrations, is an effective matrix for standardization of forensic hair testing, since the drug is incorporated into the hair matrix via uptake from systemic distribution. In the present study, extraction parameters for authentic HRM samples containing multiple drugs (diazepam, alprazolam, cocaine, methamphetamine, oxycodone, hydrocodone, and morphine) and metabolites (nordiazepam, cocaethylene, norcocaine, hydroxycocaine, and 6-monoacetylmorphine) were optimized based on recovery using a 23 full factorial DoE matrix. The factors evaluated included extraction solvent volume/sample weight ratio (12.5 or 25 μL/mg), particle size (pulverized or cut into 1 mm snippets), and extraction time (2 or 24 h) using solvent swelling. DoE analysis revealed significant differences in the optimal combinations of extraction parameters for maximum recovery. However, for the majority of drugs and metabolites, the most effective extraction method consisted of pulverizing hair prior to a 2-h extraction with a 12.5 μL/mg extraction solvent volume/sample weight ratio. © 2022 American Academy of Forensic Sciences.


    Brianna Spear, Anthony P DeCaprio. Evaluation of extraction parameters in authentic hair reference material using statistical design of experiments. Journal of forensic sciences. 2022 Jul;67(4):1607-1616

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    PMID: 35506703

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