Application of Chemometric Methods to Quality Assessment of Commercially Produced Vegetable Oils in Imo State, South East Nigeria

Chemometric analytical techniques offer powerful, data-efficient tools for the quality assessment and authentication of vegetable oils, enabling rapid differentiation between genuine and adulterated products in commercial markets where economic fraud is widespread. This study applies chemometric methods to the quality assessment of commercially produced palm kernel, groundnut, and soybean oils marketed in Imo State, South East Nigeria. Oil samples are collected from seven manufacturers and twelve retail outlets. Quality parameters determined include acid value, peroxide value, iodine value, saponification value, specific gravity, and fatty acid composition by gas chromatography-flame ionisation detection. Near-infrared spectra of all samples are collected using a portable near-infrared spectrometer to build spectral datasets for multivariate analysis. Principal component analysis is applied to identify natural clustering by oil type and to detect outliers indicative of adulteration. Partial least squares regression models are developed to predict acid value and iodine value directly from near-infrared spectra, evaluating the potential for rapid quality screening at point-of-sale. Linear discriminant analysis is used to classify samples as genuine or suspect adulterated based on combined physicochemical and spectral data. Model performance is validated using leave-one-out cross-validation. Findings demonstrate the feasibility of rapid chemometric oil authentication in a resource-limited regulatory environment and provide a practical tool for the National Agency for Food and Drug Administration and Control's market surveillance operations in South East Nigeria. Keywords: chemometrics, vegetable oil quality, near-infrared spectroscopy, Imo State, adulteration detection