Evaluation of Homogeneity in Drug Seizures Using Near-Infrared (NIR) Hyperspectral Imaging and Principal Component Analysis (PCA)

• Main Author: Strindlund, Olle
• Format: Others
• Language: English
• Published: Linköpings universitet, Kemi 2020
• Subjects: Homogeneity; drug seizures; pharmaceuticals; NIR hyperspectral imaging; chemometrics; PCA; Analytical Chemistry; Analytisk kemi
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166747

The selection of a representative sample is a delicate problem when drug seizures comprised of large number of units arrive at the Swedish National Forensic Centre (NFC). If deviating objects in the selected sample size are found, additional analyzes are required to investigate how representative the results are for the entire population. This generates further pressure on operational analysis flow. With the goal to provide a tool which forensic scientists at NFC can base their assessment of the representative nature of the selected sampling of large drug seizures on, this project investigated the possibilities of evaluating the level of homogeneity in drug seizures using near-infrared (NIR) hyperspectral imaging along with principal component analysis (PCA). A total of 27 sample groups (homogeneous, heterogeneous and seized sample groups) were analyzed and different predictive models were developed. The models were either based on quantifying the variation in NIR spectra or in PCA scores plots. It was shown that in the spectral range of 1300-2000 nm, using a pre-processing combination of area normalization, quadratic (second polynomial) detrending and mean centering, promising predictive abilities of the models in their evaluation of the level of homogeneity in drug seizures were achieved. A model where the approximated signal-dependent variation was related to the quotient of significant and noise explained variance given by PCA indicated most promising predictive abilities when quantifying the variation in NIR spectra. Similarly, a model where a rectangular area, defined by the maximum distances along PC1 and PC2, was related to the cumulative explained variance of the two PCs showed most promising predictive abilities when quantifying the variation in PCA scores plots. Different zones for which within sample groups are expected to appear based upon their degree of homogeneity could be established for both models. The two models differed in sensitivity. However, more comprehensive studies are required to evaluate the models applicability from an operational point-of-view.