Abstract
The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).
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References
Iñón FA, Garrigues S, de la Guardia M (2004) Anal Chim Acta 513:401–412
Moros J, Galipienso N, Vilches R, Garrigues S, de la Guardia M (2008) Anal Chem 80:7257–7265
Moros J, Kuligowski J, Quintás G, Garrigues S, de la Guardia M (2008) Anal Chim Acta 630:150–160
Moros J, Cassella RJ, Barciela-Alonso MC, Moreda-Piñeiro A, Herbello-Hermelo P, Bermejo-Barrera P, Garrigues S, de la Guardia M (2010) Vib Spectrosc 53:204–213
Eigenvector Research Inc., Wenatchee, USA. Available at: http://www.eigenvector.com
Brereton RG (2009) Chemometrics for pattern recognition. Wiley, London
Gertz C (2000) Eur J Lipid Sci Technol 102:566–572
Kuligowski J, Quintás G, Garrigues S, de la Guardia M (2010) Chromatographia 71:201–209
Marmesat S, Rodrigues E, Velasco J, Dobarganes MC (2007) Int J Food Sci Technol 42:601–608
Guillén MD, Cabo N (1999) J Agric Food Chem 47:709–719
Vlachos N, Skopelitis Y, Psaroudaki M, Konstantinidou V, Chatzilazarou A, Tegou E (2006) Anal Chim Acta 573–574:459–465
Valdés AF, Garcia AB (2006) Food Chem 98:214–219
Muik B, Lendl B, Molina-Diaz A, Valcarcel M, Ayora-Cañada MJ (2007) Anal Chim Acta 593:54–67
Tena N, Aparicio R, García-González DL (2009) J Agric Food Chem 57:9997–10003
Climaco Pinto R, Locquet N, Eveleigh L, Rutledge DN (2010) Food Chem 120:1170–1177
Le Dréau Y, Dupuy N, Gaydou V, Joachim J, Kister J (2009) Anal Chim Acta 642:163–170
Le Dréau Y, Dupuy N, Artaud J, Ollivier D, Kister J (2009) Talanta 77:1748–1756
Moros J, Roth M, Garrigues S, de la Guardia M (2009) Food Chem 114:1529–1536
Szabó A, Bázár G, Locsmándi L, Romvári R (2010) J Food Qual 33:42–58
Kuligowski J, Quintás G, Garrigues S, de la Guardia M (2010) Anal Bioanal Chem 397:861–869
Dupuy N, Duponchel L, Huvenne JP, Sombret B, Legrand P (1996) Food Chem 57:245–251
Ozen BF, Weiss I, Mauer LJ (2003) J Agric Food Chem 51:5871–5876
Yang H, Irudayaraj J, Paradkar MM (2005) Food Chem 93:25–32
Beltrán Sanahuja A, Prats Moya MS, Maestre Pérez SE, Grané Teruel N, Martín Carratalá ML (2009) J Am Oil Chem Soc 86:51–58
Lerma-García MJ, Ramis-Ramos G, Herrero-Martínez JM, Simó-Alfonso EF (2010) Food Chem 118:78–83
de B. Harrington P, Kister J, Artaud J, Dupuy N (2009) Anal Chem 81:7160–7169
Marbach R (2005) J Near Infrared Spectrosc 13:241–254
Kuligowski J, Quintás G, Garrigues S, de la Guardia M (2009) J Sep Sci 32:4089–4095
Westerhuis JA, Hoefsloot HCJ, Smit S, Vis DJ, Smilde A, van Velzen EJJ, van Duijnhoven JPM, van Dorsten FA (2008) Metabolomics 4:81–89
Barker M, Rayens W (2003) J chemom 17:166–173
Wold S, Sjöström M, Eriksson L (2001) Chem Int Lab Syst 58:109–130
Esbensen KH, Geladi P (2010) J chemom 24:168–187
Acknowledgments
JK acknowledges the “V Segles” grant provided by the University of Valencia to carry out this study. Authors acknowledge the financial support of Ministerio de Educación y Ciencia (Projects AGL2007-64567 and CTQ2008-05719/BQU) and Conselleria d´Educació de la Generalitat Valenciana (Project PROMETEO 2010-055).
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Kuligowski, J., Carrión, D., Quintás, G. et al. Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy. Anal Bioanal Chem 399, 1305–1314 (2011). https://doi.org/10.1007/s00216-010-4457-2
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DOI: https://doi.org/10.1007/s00216-010-4457-2