Detection of Adulteration in Camellia Oil Using Near-Infrared Spectroscopy

Abstract

Near-infrared spectroscopy (NIRS) combined with chemometrics analysis was used in this study to qualitatively and quantitatively determine the adulterated Camellia oil. A binary model was constructed for determining both the authenticity and the number of adulterated contents. NIRS combined with support vector machine classification was used to establish a full spectral model and a selected spectral model via competitive adaptive heavy-weighted sampling and backward interval partial least squares. Notably, both of them were proved to be suitable for determining the authenticity of Camellia oil. NIRS combined with support vector machine regression may be used to predict the amount of adulterated content in Camellia oil because of the high model correlation coefficient (R was higher than 99%, and the maximum mean square error was 0.0605).