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The luminol test has been used for over 60 years by forensic investigators for presumptive identification of blood and visualization of blood splatter patterns. Multiple studies have estimated the limit of detection (LD) for bloodstains when luminol is employed, with results ranging from 100× to 5,000,000× dilute. However, these studies typically have not identified and controlled important experimental variables which may affect the luminol LD for bloodstains. Without control of experimental parameters in the laboratory, variables which affect the potential of presumptive bloodstain test methods remain largely unknown, and comparisons required to establish new, more powerful detection methods are simply impossible. We have developed a quantitative method to determine the relationship between the amount of blood present and its reaction with luminol by measuring, under controlled conditions, the resulting chemiluminescent intensity with a video camera, combined with processing of the digital intensity data. The method resulted in an estimated LD for bloodstains on cotton fabric at ∼200,000× diluted blood with a specific luminol formulation. Although luminol is the focus of this study, the experimental protocol used could be modified to study effects of variables using other blood detection reagents., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Our laboratories have recently developed a flow-through imaging photometer to characterize and classify fluorescent particles between 3 and 47 μm in size. The wide aperture of the objective lens (0.7 NA) required for measuring spectral fluorescence of single particles restricts the depth of field, such that a large sample volume results in many particles that are out of focus. Here, we describe numerical methods for determining the size of these objects, regardless of their distance from the focal plane, using image processing and multivariate calibration. An intensity profile is extracted from the images and is used as the input for a variety of calibration methods, including partial least squares, neural networks, and support vector machines. The capabilities of these methods are examined to establish the best method for particle sizing that is independent of focus. We found that support vector machines provided the best results, with size estimation error of ±3.1 μm.