PLS-DA wavenumber selection for the categorization of medicine samples based on multiple criteria

Forensic Science International

Volumn 242, Issue , 2014, Pages 111-116

  Anzanello, Michel J ^a   Ortiz, Rafael S ^b   Limberger, Renata ^a   Mariotti, Kristiane ^a  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO SUL   (Brazil)

^b Brazilian Federal Police   (Brazil)

Author keywords

ATR FTIR; Counterfeit medicines; PLS DA; Wavenumber identification

Indexed keywords

COUNTERFEIT DRUG; SILDENAFIL; TADALAFIL;

ACCURACY; ALGORITHM; ARTICLE; DATA ANALYSIS; DISCRIMINANT ANALYSIS; DRUG CLASSIFICATION; FORENSIC IDENTIFICATION; INFRARED SPECTROSCOPY; LINEAR SYSTEM; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; VARIANCE; WAVENUMBER;

EID: 84904480404     PISSN: 03790738     EISSN: 18726283     Source Type: Journal    
DOI: 10.1016/j.forsciint.2014.06.034     Document Type: Article

References (23)

1. Been F., Roggo Y., Degardin K., Esseiva P., Margot P. Profiling of counterfeit medicines by vibrational spectroscopy. Forensic Sci. Int. 2011, 211:83-100.
2. Holzgrabe U., Malet-Martino M. Analytical challenges in drug counterfeiting and falsification-the NMR approach. J. Pharm. Biomed. Anal. 2011, 55:679-687.
3. Sacré P.-Y., Deconinck E., de Beer T., Courselle P., Vancauwenberghe R., Chiap P., Crommen J., de Beer J.O. Impurity fingerprints for the identification of counterfeit medicines-a feasibility study. J. Pharm. Biomed. Anal. 2010, 53:445-453.
4. Rodomonte A.L., Gaudiano M.C., Antoniella E., Lucente D., Crusco V., Bartolomei M., Bertocchi P., Manna L., Valvo L., Alhaique F., Muleri N. Counterfeit drugs detection by measurement of tablets and secondary packaging colour. J. Pharm. Biomed. Anal. 2010, 53:215-220.
5. Fernandez F.M., Hostetler D., Powell K., Kaur H., Green M., Mildenhall D.C., Newton P.N. Poor quality drugs: grand challenges in high throughput detection, countrywide sampling, and forensics in developing countries. Analyst 2010, 136:3073-3082.
6. Lopes M., Wolff J., Bioucas-Dias J., Figueiredo M. Determination of the composition of counterfeit Heptodinimage tablets by near infrared chemical imaging and classical least squares estimation. Anal. Chim. Acta 2009, 641:46-51.
7. Ortiz R.S., Mariotti K.C., Bruna Fank R.P., Limberger M., Anzanello P., Mayorga Counterfeits Cialis and Viagra fingerprinting by ATR-FTIR spectroscopy with chemometry: can the same pharmaceutical powder mixture be used to falsify two medicines?. Forensic Sci. Int. 2013, 226:282-289.
8. Anzanello M., Ortiz R., Limberger R., Mayorga P. A multivariate-based wavenumber selection method for classifying medicines into authentic or counterfeit classes. J. Pharm. Biomed. 2013, 83:209-214.
9. Brito A., Brito L., Honorato F., Pontes M., Pontes L. Classification of cereal bars using near infrared spectroscopy and linear discriminant analysis. Food Res. Int. 2013, 51:924-928.
10. Li P., Du G., Cai W., Shao X. Rapid and nondestructive analysis of pharmaceutical products using near-infrared diffuse reflectance spectroscopy. J. Pharm. Biomed. 2012, 70:288-294.
11. Xiaobo Z., Jiewen Z., Povey M.J.W., Holmes M., Hanpin M. Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta 2010, 667:14-32.
12. Balabin R.M., Smirnov S.V. Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data. Anal. Chim. Acta 2011, 692:63-72.
13. Anzanello M., Albin S., Chaovalitwongse W. Multicriteria variable selection for classification of production batches. Eur. J. Oper. Res. 2012, 218:97-105.
14. Ferrer A., Aguado D., Vidal-Puig S., Prats J., Zarzo M. PLS: a versatile tool for industrial process improvement and optimization. Appl. Stochastic Models Bus 2008, 24:551-567.
15. Anzanello M., Albin S., Chaovalitwongse W. Selecting the best variables for classifying production batches into two quality levels. Chemom. Intell. Lab. 2009, 97:111-117.
16. Lu J., Chen S., Wang W., Zuylen H. A hybrid model of partial least squares and neural network for traffic incident detection. Expert Syst. Appl. 2012, 39:775-4784.
17. Sinkov N., Sandercock P., Harynuk J. Chemometric classification of casework arson samples based on gasoline content. Forensic Sci. Int. 2014, 235:24-31.
18. Yang A., You W., Ji G. Using partial least squares and support vector machines for bankruptcy prediction. Expert Syst. Appl. 2011, 38:8336-8342.
19. Wold S., Sjostrom M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. 2001, 58:109-130.
20. Esposito-Vinzi V., Chin W., Henseler J., Wang W. Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields 2007, 850. Springer, Berlin. first ed.
21. Hoskuldsson A. Variable and subset selection in PLS regression. Chemom. Intell. Lab. 2001, 55:23-38.
22. Almeida M., Fidelis C., Barata L., Poppi R. Classification of Amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation. Talanta 2013, 117:305-311.
23. Rencher A. Methods of Multivariate Analysis 2002, Wiley, New York, NY. second ed.