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.
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).
Department of Analytical Chemistry, University of Valencia, Edificio Jerónimo Muñoz, 50th Dr. Moliner, 46100 Burjassot, Spain.
Julia
J
Kuligowski
David
D
Carrión
Guillermo
G
Quintás
Salvador
S
Garrigues
Miguel
M
de la Guardia
D003296:Cooking; D003314:Corn Oil / Q000032:analysis*; D016018:Least-Squares Analysis; D000069463:Olive Oil; D010938:Plant Oils / Q000032:analysis*; D011786:Quality Control; D017550:Spectroscopy, Fourier Transform Infrared; D000074242:Sunflower Oil; D013696:Temperature
D000069463:Olive Oil; D010938:Plant Oils; D000074242:Sunflower Oil; D003314:Corn Oil