Use of predictive tools for the analysis of bioactive compounds in crocus SATIVUS L. flowers.

BACKGROUND: Crocus sativus L. (saffron) flowers have high bioactive potential because of their content of crocins, safranal and phenolic compounds. The development of predictive models based on near-infrared (NIR) spectroscopy and partial least squares regression to estimate the antioxidant capacity of saffron flower, evaluated using oxygen radical absorbance capacity (ORAC), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays, was investigated. RESULTS: In total, 231 samples were collected during the 2023 harvest season within the PDO 'La Mancha Saffron' (Spain). DPPH models showed the highest predictive performance [R2 p = 0.93; residual predictive deviation (RPD) = 3.87], followed by ORAC (R2 p = 0.93; RPD = 3.85) and ABTS (R2 p = 0.92; RPD = 3.40). Second derivative preprocessing combined with standard normal variate transformation improved model accuracy, especially for DPPH and ORAC. Correlation analysis with compounds determined by HPLC-diode array detection highlighted the contribution of crocins and flavonoids to DPPH and ABTS responses, whereas safranal was preferentially associated with ORAC. CONCLUSION: NIR spectroscopy coupled with chemometric modelling represents a rapid, sustainable, and reliable approach for predicting antioxidant capacity in C. sativus L. flowers. The developed models demonstrated strong potential for routine quality control and industrial valorization of saffron flower within circular economy strategies. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.