Author/Authors :
Marco F. Ferr?o، نويسنده , , Simone C. Godoy، نويسنده , , Annelise E. Gerbase، نويسنده , , Cesar Mello، نويسنده , , Jo?o Carlos Furtado، نويسنده , , Cesar L. Petzhold، نويسنده , , Ronei Jesus Poppi، نويسنده ,
Abstract :
This paper presents the use of least-squares support vector machine (LS-SVM) for quantitative determination of hydroxyl value (OHV) of hydroxylated soybean oils by horizontal attenuated total reflection Fourier transform infrared (HATR/FT-IR) spectroscopy. A least-squares support vector machine (LS-SVM) calibration model for the prediction of hydroxyl value (OHV) was developed using the range 1805.1–649.9 cm−1. Validation of the method was carried out by comparing the OHV of a series of hydroxylated soybean oil predicted by the LS-SVM model to the values obtained by the AOCS standard method. A correlation coefficient equal to 0.989 and RMSEP = 4.96 mg of KOH/g was obtained. This study demonstrates a better prediction ability of the LS-SVM technique to determine OHV in hydroxylated soybean oil samples by HATR/FT-IR spectra.
Keywords :
Least-squares support vector machine , Hydroxyl value , Chemometrics , Hydroxylated soybean oil , Horizontal attenuated total reflectance