Determination of olive oil quality parameters and adulteration with NIR spectroscopy

Importance of the Topic

Olive oil authenticity and quality assessment is critical due to its nutritional value economic significance and vulnerability to adulteration Reliable and rapid evaluation methods support producers and regulators in ensuring product integrity while reducing environmental impact and operational costs.

Objectives and Study Overview

This study aimed to develop robust near infrared spectroscopy based models for quantifying key olive oil quality indicators and detecting adulteration A diverse set of 137 virgin and refined olive oil samples was measured to establish predictive relationships for parameters including iodine value free fatty acids refractive index peroxide value K232 induction time and fatty acid profile.

Methodology and Instrumentation Used

• Samples were scanned in transmission mode over the 1000–2250 nm range using 8 mm disposable vials at controlled temperature of 40 °C
• Data acquisition and chemometric model building were performed with OMNIS software dividing data into 75% calibration and 25% validation sets
• Model performance was evaluated using cross validation metrics such as SEC SECV SEP and R2CV

Instrumentation Used

• OMNIS NIR Analyzer Liquid operating in transmission mode
• 8 mm borosilicate vials with temperature monitoring
• OMNIS software for spectrum collection and multivariate analysis

Results and Discussion

The calibration models exhibited strong prediction capability with R2CV values above 0.86 for all quality parameters Fatty acids such as oleic linoleic and palmitic acids showed R2CV exceeding 0.96 while physicochemical markers like refractive index achieved R2CV of 0.998 Induction time and UV absorption index K232 were also reliably predicted demonstrating the applicability of NIR spectroscopy for comprehensive olive oil profiling.

Benefits and Practical Applications

Near infrared spectroscopy eliminates the need for reagents and sample preparation reducing chemical waste and operational expenses Measurement times of less than ten seconds per sample enable high throughput screening The approach supports routine quality control and rapid screening for potential adulteration across production and distribution chains.

Future Trends and Potential Applications

Extension of NIR models to additional markers such as sterol and moisture content Integration with automated sampling systems and coupling with complementary techniques like titration Development of portable and inline NIR devices for real time monitoring Advanced machine learning algorithms to enhance model robustness and adulterant detection capabilities.

Conclusion

The presented application demonstrates that near infrared spectroscopy provides a rapid accurate and sustainable alternative to conventional analytical methods for olive oil quality assessment The established models achieve high correlation with reference techniques while offering significant cost and time savings.