Digital Detection of Olive Oil Rancidity Levels and Aroma Profiles Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Machine Learning Modelling

The success of the olive oil industry depends on provenance and quality-trait consistency affecting consumers' acceptability/preference purchase intention. Companies rely laboratories to analyze samples assess within production chain, which may be time-consuming, cost-restrictive, untimely obtaining results, making process more reactive than predictive. This study proposed implementing digital technologies using near-infrared spectroscopy (NIR) a novel low-cost e-nose level rancidity aromas in commercial extra-virgin oil. Four different oils were spiked with three levels (N = 17). These evaluated gas-chromatography-mass-spectroscopy, NIR, an e-nose. machine learning models developed classify types (Model 1: NIR inputs; Model 2: inputs) predict peak area 16 3: NIR; 4: inputs). results showed high accuracies (Models 1–2: 97% 87%; Models 3–4: R 0.96 0.93). change companies from predictive food/beverages secure product quality acceptability.