Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: Scott Test Evaluation By Multivariate Image Analysis In Cocaine Samples
Abstract: The Scott test is a preliminary colorimetric method to analyze cocaine. A blue color result in the final step denotes a positive indication for cocaine; however, some pharmacological products may lead to false positives when concentration is higher than 1 mg. In order to eliminate these false positives, digital images derived from the Scott test for pure cocaine, adulterants, diluents, and their binary and ternary mixtures were acquired with a commercial scanner, and the histogram of such images were assessed through Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine Discriminant Analysis (SVM-DA). PCA scores indicated a clear separation between a true positive and a false positive group, with exception for the higher concentrations of the adulterants assessed and their mixtures. In addition, we applied two supervised techniques, PLS-DA and SVM-DA, to the histograms of the digital images derived from the Scott test aimed at categorizing samples into true positive and true negative classes. PLS-DA yielded highly satisfactory results as only two samples were not correctly classified; the SVM-DA correctly classified all samples, yielding sensitivity and specificity equal to 1. © 2016 Elsevier B.V.
Editor: Elsevier Inc.
Citation: Microchemical Journal. Elsevier Inc., v. 127, p. 87 - 93, 2016.
Rights: fechado
Identifier DOI: 10.1016/j.microc.2016.02.012
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File SizeFormat 
2-s2.0-84959563909.pdf1.27 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.