Rapid Detection of Adulteration in Cold-Pressed Olive Oils via Handheld NIR Spectroscopy and Machine Learning

Advances in Food Science and Technology | Vol. 14, No. 7, July 2026 | pp. 001–006

ISSN: 6732-4215

Article Type: Original Research Paper

Title: Rapid Detection of Adulteration in Cold-Pressed Olive Oils via Handheld NIR Spectroscopy and Machine Learning

Names of Authors: Sanjay Nair¹, Lucia Rossi²

Authors’ Affiliations: ¹Department of Analytical Chemistry, Indian Institute of Science, Bangalore, India; ²Food Safety Infrastructure Lab, University of Rome, Rome, Italy

Abstract: Premium cold-pressed extra virgin olive oils (EVOO) are frequent targets for corporate food fraud, often being diluted with cheap canola, sunflower, or soybean substitute oils. Traditional chromatographic identification protocols require lengthy preparation steps, specialized laboratories, and destructive sample treatments. This article introduces a non-destructive screening pipeline using a portable Near-Infrared (NIR) spectrometer linked with machine learning classification algorithms. Spectral footprints were collected across 350 oil mixtures containing low adulteration concentrations (ranging from 2% to 20% oil volume fractions). The raw absorbance data records were cleaned using standard multiplicative scatter corrections (MSC). A Partial Least Squares Regression (PLSR) model was trained to predict dilution ratios. The validation metrics prove that the portable NIR pipeline achieved a true classification validation score of 98.6% when identifying adulterated batches. The system identified minor adulterant concentrations down to a minimal quantification boundary of 2.5% oil dilution. The testing cycle was executed within 15 seconds per sample, providing a rapid solution for customs checkpoints.

Keywords: Olive Oil Fraud, Food Authentication, NIR Spectroscopy, Partial Least Squares, Chemometrics, Non-Destructive Testing

Manuscript Timeline: Received: January 05, 2026; Revised: February 18, 2026; Accepted: March 24, 2026; Published: July 15, 2026

Citation: Nair, S., & Rossi, L. (2026). Rapid Detection of Adulteration in Cold-Pressed Olive Oils via Handheld NIR Spectroscopy and Machine Learning. Advances in Food Science and Technology, 14(10), 001–006.

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