Estimating error rates for firearm evidence identification is a fundamental challenge in forensic science. This paper describes the recently developed congruent matching cells (CMC) method for image comparisons, its application to firearm evidence identification, and its usage and initial tests for error rate estimation. The CMC method divides compared topography images into correlation cells. Four identification parameters are defined for quantifying both the topography similarity of the correlated cell pairs and the pattern congruency of the registered cell locations. A declared match requires a significant number of CMCs, i.e., cell pairs that meet all similarity and congruency requirements. Initial testing on breech face impressions of a set of 40 cartridge cases fired with consecutively manufactured pistol slides showed wide separation between the distributions of CMC numbers observed for known matching and known non-matching image pairs. Another test on 95 cartridge cases from a different set of slides manufactured by the same process also yielded widely separated distributions. The test results were used to develop two statistical models for the probability mass function of CMC correlation scores. The models were applied to develop a framework for estimating cumulative false positive and false negative error rates and individual error rates of declared matches and non-matches for this population of breech face impressions. The prospect for applying the models to large populations and realistic case work is also discussed. The CMC method can provide a statistical foundation for estimating error rates in firearm evidence identifications, thus emulating methods used for forensic identification of DNA evidence.

The application of surface-enhanced Raman spectroscopy (SERS) for everyday quantitative analysis is hindered by the point-to-point variability of SERS substrates that arises due to the heterogeneous distribution of localized electromagnetic fields across a suite of plasmonic nanostructures. Herein, we adopt surface-enhanced elastic scattering as a SERS internal standard. Both elastic and inelastic (i.e., Raman) scattering are simultaneously enhanced by a given “hot spot”, and thus, the surface-enhanced elastic scattering signal provides a localized intrinsic internal standard that scales across all of the plasmon-enhanced electromagnetic fields within a substrate. Elastically scattered light originates from the amplified spontaneous emission (ASE) of the commercial laser, leading to the formation of a low-wavenumber pseudo band that arises from the interaction of the ASE and the edge filter. A theoretical model was developed to illustrate the underlying mechanism supporting this normalization approach. The normalized Raman signals are independent of the incident laser intensity and the density of “hot spots” for numerous SERS substrates. Following “hot-spot” (HS) normalization, the coefficient of variation for the tested SERS substrates decreases from 10 to 60% to 2%–7%. This approach significantly improves SERS quantitation of four chloroanilines and enables collection of highly reproducible analyte adsorption results under both static and dynamic imaging conditions. Overall, this approach provides a simple means to improve SERS reproducibility without the need to use additional chemicals as internal standards.

Areal Cross Correlation Function, a statistical function of three dimensional surface topography ANOVA Analysis of Variance ATF Bureau of Alcohol, Tobacco, Firearms, and Explosives BF Breech face CCF Cross Correlation Function, a statistical function of two dimensional surface topography DAS Data Acquisition Station, a component of IBIS (below) EEEL Electronics and Electrical Engineering Laboratory, an organizational unit of NIST FP Firing pin

Ultrasensitive surface-enhanced Raman spectroscopy (SERS) still faces difficulties in quantitative analysis because of its susceptibility to local optical field variations at plasmonic hotspots in metallo-dielectric nanostructures. Current SERS calibration approaches using Raman tags have inherent limitations due to spatial occupation competition with analyte molecules, spectral interference with analyte Raman peaks, and photodegradation. Herein, we report that plasmon-enhanced electronic Raman scattering (ERS) signals from metal can serve as an internal standard for spatial and temporal calibration of molecular Raman scattering (MRS) signals from analyte molecules at the same hotspots, enabling rigorous quantitative SERS analysis. We observe a linear dependence between ERS and MRS signal intensities upon spatial and temporal variations of excitation optical fields, manifesting the |E|4 enhancements for both ERS and MRS processes at the same hotspots in agreement with our theoretical prediction. Furthermore, we find that the ERS calibration’s performance limit can result from orientation variations of analyte molecules at hotspots.