Related Experiment Video
Updated: Feb 18, 2026

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
Published on: May 20, 2013
Multi-spectral imaging for the estimation of shooting distances
Félix Zapata1, María López-López1, José Manuel Amigo2
1Department of Analytical Chemistry, Physical Chemistry and Chemical Engineering and University Institute of Research in Police Sciences (IUICP), University of Alcalá, Ctra. Madrid-Barcelona km 33.6, 28871 Alcalá de Henares (Madrid), Spain.
Abstract:
Multispectral images of clothing targets shot at seven different distances (from 10 to 220cm) were recorded at 18 specific wavelengths in the 400-1000nm range to visualize the gunshot residue (GSR) pattern. Principal component analysis (PCA) showed that the use of violet-blue wavelengths (430, 450 and 470nm) provided the largest contrast between the GSR particles and the white cotton fabric. Then, the correlation between the amount of GSR particles on clothing targets and the shooting distance was studied. By selecting the blue frame of multispectral images (i.e. the blue frame in the red-green-blue (RGB) system which falls at 470nm), the amount of pixels containing GSR particles was accounted based on the intensity of pixels in that frame. Results demonstrated that the number of pixels containing GSR exponentially decreases with the shooting distance from 30 to 220cm following a particular exponential equation. However, the targets shot at the shortest distance (10cm) did not satisfy the above equation, probably due to the noticeable differences of the GSR-pattern of these targets (e.g. high presence of soot). Then, the equation was applied to validation samples to estimate the shooting distances, obtaining results with an error below 10%.
More Related Videos
08:22Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis
Published on: October 27, 2020
07:34Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
Published on: August 22, 2019