Electrochemical investigation of edible oils: Experimentation, electrical signatures, and a supervised learning–case study of adulterated peanut oils

Abstract

Traditional approaches to characterize edible oils such as chemical, chromatographic and light absorption techniques are laborious, expensive, and bulky to implement. This paper presents the electrochemical impedance spectroscopy of 13 types of edible oils, a rapid robust approach to characterizing the electrical behavior of oils without sample preparation. This is achieved through probing the oils via oscillating electric fields to capture oil-specific electrical behaviors. The principal component analysis discriminates the oil types well and establishes repetitive behavioral trends, perceived as electrical signatures. This data is applied in a case study of adulterated peanut oils to quantify adulteration via supervised machine learning with batch-wise leave-one-out implementation. The mean absolute errors and R2 values measure 2.18–3.27 and 0.975–0.991 respectively across 4 test batches. This work provides an exemplar for the electrochemical study of edible oils, with potential for portable proof-of-value device configurations for rapid in situ analysis of edible oils and adulterated oils.