Identification of Geographical Origin of Olive Oil Using Visible and Near-Infrared Spectroscopy Technique Combined with Chemometrics

摘要

A novel multivariate calibration method was developed to identify the geographical origin of olive oils using visible and near-infrared spectroscopy (Vis/NIRS) on the wavelength between 325 and 1,075 nm. Direct orthogonal signal correction (DOSC) preprocessing method was performed to reduce the influence of light scattering, background noise, and baseline shift during experiment. An optimization method of genetic algorithms (GAs) was used to select informative variables from the full spectrum, and 37 informative variables were selected for partial least squares (PLS) regression analysis. The prediction results indicated that the developed DOSC-GA-PLS model can be successfully employed to predict geographical origin of olive oils. Moreover, the use of GA simplified and improved the predictive ability of the model. The prediction statistical parameters were correlation coefficient ( R_textP2 R_{text{P}}^2 ) of 0.987, relative deviation was 0.093, and the recognition ratio was 97%. It was concluded that Vis/NIRS combined with DOSC-GA-PLS method can be successfully used to determine the geographical origin of olive oils accurately and quickly.