Application of multivariate methods to scalp hair metal data to distinguish between drug-free subjects and drug abusers
✍ Scribed by Pilar Bermejo-Barrera; Antonio Moreda-Piñeiro; Adela Bermejo-Barrera; Ana Marı́a Bermejo-Barrera
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 113 KB
- Volume
- 455
- Category
- Article
- ISSN
- 0003-2670
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✦ Synopsis
Human scalp hair samples of drug-free subjects and drug abusers (heroin and cocaine-heroin abusers) were analysed for trace metals by flame atomic absorption spectrometry (FAAS), flame atomic emission spectrometry (FAES) and electrothermal atomic absorption spectrometry (ETAAS). The classification of drug-free subjects and drug abuses groups with four multivariate methods using the metal contents in hair samples as discriminant variables has been discussed. Principal component analysis (PCA), cluster analysis (CA), linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) allow distinguishing the two groups correctly. However, predictions by SIMCA are less satisfactory. Thirteen elements (Ag, Al, Ca, Cd, Cr, Cu, K, Mg, Mn, Na, Ni, Pb, and Zn) were determined by FAAS/FAES/ETAAS in 53 hair samples (16 samples of drug-free people and 37 samples of drug abusers). Human hair samples were prepared as aqueous slurries as sample pre-treatment and they were analysed using the slurry sampling technique. The half-range central value transformation was novelty used as data pre-treatment to homogenise the data. Grouping in the samples (drug-free people and drug abusers) were observed by using PCA and CA (squared Euclidean distance between objects and Ward method as clustering procedure). The application of LDA gave a correct recognition assignation percentage of 91.7 and 100.0% for the drug-free people and drug abusers, respectively, at a significance of 5%, while SIMCA offered recognition percentages of 83.3 and 91.3% for drug-free people and drug abusers, respectively, also at 5%. Finally, some studies were developed to classify heroin abusers and polidrug abusers (cocaine-heroin abusers) by the cited multivariate statistical methods. Recognition percentages of 90.9 and 100.0% were reached for heroin abusers and polidrug abusers groups, respectively, after LDA, while these percentages decreased to percentages lower than 90.0% when SIMCA was applied.