Optimization of Nigella Sativa Extraction in Olive Oil and Development of a Nondestructive Method for Thymoquinone Quantification: A Comprehensive Approach by Multivariate Statistical Methods Publisher



Namini N ; Sabourian R ; Khanavi M ; Khoshayand MR ; Amini M ; Tamiji Z ; Hajimahmoodi M
Source: Journal of Food Measurement and Characterization Published:2026

Abstract

This study explores the process of extracting bioactive compounds from Nigella sativa seeds utilizing olive oil as a green solvent, optimized through multivariate statistical methods. Nigella sativa, known for its medicinal and culinary applications, contains thymoquinone (TQ) as its key bioactive component. The study investigates the potential of olive oil as a sustainable solvent, adhering to green chemistry principles for TQ extraction from Nigella sativa seeds. Response Surface Methodology (RSM) was employed to optimize the process of extraction, utilizing a Box-Behnken design, focusing on extraction time, temperature, and seed-to-oil ratio optimization to minimize anisidine value and maximize TQ content and induction time (InT). The optimum extract was charachterized using spectrophotometry, HPLC and GC-FID instruments. A second-order polynomial model was developed, and the responses for TQ and InT were statistically significant (p-value < 0.0001) with strong predictive capabilities (R2 = 0.9608, 0.9809 respectively). In the optimum extraction condition TQ content (1166.02 µg/mL), InT (40.78 h) and anisidine value (1.087) was obtained after 24-h extraction at 45 °C and a ratio of 1 w/w and predominant fatty acids were oleic, linoleic and palmitic acid. In addition, total sterol and total tocopherol contents were 918.6 µg/mL and 108.4 µg/mL respectively. The adequacy of the models was confirmed by the non-significant lack-of-fit tests. Multi-response optimization using the desirability function simultaneously maximized TQ content and InT with desirability value of 0.974 under optimal conditions. Validation of optimization confirmed the predictive reliability of the developed model. Additionally, a nondestructive multivariate statistical model for prediction of TQ content was developed ustilizing near infrared spectroscopy an chemometric. The developed multivariate model, obtained after SNV preprocessing, successfully predicts TQ content: root-mean-square error of calibration (RMSEC): 3.529, Rcal2: 0.99; RMSE cross-validation (RMSECV): 9.467, Rval2: 0.94; ratio of standard error of performance to standard deviation (RPD): 4.90; range error ratio (RER): 2.32. The findings reveal olive oil as solvent for extraction of TQ from Nigella sativa seeds, offering a sustainable alternative to traditional solvents and contributing to the development of natural and health-promoting products. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.