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A multivariate-based wavenumber selection method for classifying medicines into authentic or counterfeit classes.

Michel J Anzanello¹, Rafael S Ortiz, Renata P Limbergerb

¹Department of Industrial Engineering, Federal University of Rio Grande do Sul, 90035-190 Rio Grande do Sul, Brazil. michel.anzanello@gmail.com

Journal of Pharmaceutical and Biomedical Analysis

|June 18, 2013

Summary

This study introduces a new method using principal components analysis (PCA) and k-nearest neighbor (KNN) to select important wavenumbers from Fourier transform infrared (FTIR) spectroscopy data, improving fraudulent medicine detection.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Area of Science:

Analytical Chemistry
Spectroscopy
Chemometrics

Background:

Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) is used for detecting counterfeit drugs.
FTIR-ATR spectra contain numerous wavenumbers, potentially hindering classification accuracy.
Efficient selection of relevant wavenumbers is crucial for accurate sample classification.

Purpose of the Study:

To develop a novel method for selecting informative wavenumber subsets from FTIR-ATR spectra.
To enhance the classification accuracy of authentic versus fraudulent medicines.
To reduce the number of variables required for effective drug analysis.

Main Methods:

Integration of principal components analysis (PCA) with k-nearest neighbor (KNN) classification.
Development of a variable importance index based on PCA outputs.
Iterative backward variable elimination guided by the importance index to identify optimal wavenumber subsets.

Main Results:

The proposed method significantly reduced the number of retained wavenumbers (e.g., 1.84% for Cialis, 7.72% for Viagra).
Classification accuracy for Cialis increased by 2.1% (0.9897 from 0.9689).
Classification accuracy for Viagra increased by 1.56% (0.9278 from 0.9135).

Conclusions:

The developed PCA-integrated KNN method effectively selects parsimonious wavenumber subsets for FTIR-ATR data.
This approach improves the accuracy and efficiency of detecting fraudulent medicines.
The method offers a robust chemometric strategy for spectroscopic analysis and classification tasks.