Portable Spectrophotometric Olive Oil Quality Analysis System Integrated with Oxidative Stress-State Modeling

Abstract Olive oil quality control requires analytical methods that distinguishes authentic extra virgin olive oil from lower-grade or adulterated oils while simultaneously monitoring oxidation, phenolic depletion, and storage-related deterioration. Conventional reference techniques such as gas chromatography, liquid chromatography, nuclear magnetic resonance spectroscopy, and standardized wet-chemistry assays remain essential for confirmatory analysis, yet their cost, infrastructure needs, and sample-processing demands limit continuous use at farms, mills, warehouses, and retail inspection points. Here we present a full research framework for a portable spectrophotometric olive oil quality analysis system that integrates ultraviolet-visible spectroscopy, near-infrared spectroscopy, chemometric calibration, edge-machine-learning classification, and oxidative stress-state modeling. The system is designed to quantify quality indicators such as free acidity, peroxide value, conjugated diene/triene absorbance, phenolic depletion, pigment degradation, and adulteration-associated spectral deviation without relying on destructive laboratory workflows. The research develops the platform as an analytical chemistry and embedded diagnostic system rather than as a simple consumer sensor. The designed instrument combines controlled optical illumination, wavelength-resolved detection, temperature-compensated sample handling, onboard preprocessing, encrypted data storage, and cloud-supported model governance. The biochemical model links spectral behavior to triglyceride structure, oleic-linoleic acid balance, phenolic antioxidant buffering, radical propagation, hydroperoxide accumulation, aldehyde formation, and the degradation of chlorophyll and carotenoid pigments during light or heat exposure. The computational model couples standard normal variate correction, derivative spectroscopy, principal component analysis, partial least squares regression, support vector machines, random forests, gradient boosting, and uncertainty-aware edge inference. A validation strategy is applied using authenticated extra virgin, virgin, refined, and pomace oils; controlled adulteration series with sunflower, soybean, canola, palm, and refined olive oils; and accelerated storage experiments under oxygen, heat, and light stress. Expected outputs include rapid screening of authenticity, estimation of regulatory quality parameters, early detection of oxidative instability, and generation of traceable reports for producers, inspectors, and supply-chain actors. The framework is aligned with international olive oil quality concepts while emphasizing that portable spectroscopy is best positioned as a high-throughput screening and decision-support layer rather than a replacement for legal confirmatory testing.