Reproducibility of high-performance thin-layer chromatography (HPTLC) in textile dye analysis

In the forensic comparison of dyed fibers, thin-layer chromatography (TLC) is one of the few analytical techniques that have proven sensitive enough to detect and separate the minute quantities of dyestuffs present in single fibers. The method has become well-established by trace evidence examiners as a means of further distinguishing fibers whose colors appear to be the same by both comparison microscopy and microspectrophotometry. As practiced at present, the forensic analysis of dyes is limited to a comparison of the separated dye bands from known and questioned fibers performed on the same TLC plate. It is recognized, however, that retardation factor (R-f) alone is not sufficient proof of identify. The limited use of investigative TLC analysis in forensic fiber examinations is due in part to the range of uncontrolled or poorly defined variables that affect the reproducibility of the developed plate. Through the study of a six component test dye mixture developed on over 50 high-performance thin-layer chromatography plates, the effects of several critical variables that affect reproducibility and resolution, including: plate selection, pre-elution, tank saturation, developing distance, and eluent stability have been evaluated. When considered collectively, the results of this research provide a means for acquiring and archiving repeatable data, both from casework and known reference samples collected on different plates and at different times. These provide a pathway for the development and utilization of reference databases for the identification of dyes. The empirical uncertainty established for the generalized separation procedure used in this research provides objective guidance for evaluating the significance of associations (or eliminations) made on the basis Rf. Ultimately, this research also opens a pathway to the use of forensic dye analysis as an investigative tool, rather than one exclusively restricted to comparative analyses. (C) 2018 Elsevier B.V. All rights reserved.