# Potential Rapid Quantification of Antioxidant Capacity of Olea europaea L. Leaves by Near-Infrared Spectroscopy Using Different Assays

## TL;DR

This study explores using near-infrared spectroscopy to quickly measure the antioxidant capacity of olive leaves, avoiding traditional methods that require reagents and time.

## Contribution

The paper introduces a rapid, non-destructive method for quantifying antioxidant capacity in olive leaves using near-infrared spectroscopy.

## Key findings

- Predictive models achieved high R² values (0.9–1) for DPPH, ABTS, and ORAC assays.
- High RPD values confirmed strong reliability and predictive performance of the models.
- Near-infrared spectroscopy proved effective for rapid antioxidant capacity quantification.

## Abstract

The olive tree has exceptional agricultural and economic importance in Mediterranean regions due to its fruit, which is used to produce olive oil. However, the olive oil industry generates a significant amount of waste, including leaves from Olea europaea L. These leaves contain a high concentration of bioactive compounds, predominantly phenolic ones, which are well known for their antioxidant properties and health benefits. Determining antioxidant capacity involves the use of different assays based on absorbance (DPPH, 2,2-diphenyl-1-picrylhydrazyl; and ABTS, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) and fluorescence (ORAC, Oxygen Radical Absorbance Capacity), which require reagents and long waiting times. Therefore, having a non-destructive technique capable of providing this information would be useful. To explore this, 120 olive leaf samples were analyzed using the three antioxidant assays to quantify their total antioxidant capacity. Predictive models were successfully developed for each of the three methods, achieving coefficients of determination (R2) between 0.9 and 1 across calibration, validation, and prediction. Additionally, high residual predictive deviation (RPD) values were obtained, indicating that the models exhibit strong reliability and predictive performance.