Saffron, often referred to as “red gold,” is a highly valued spice obtained from the dried stigmas of Crocus sativus L., a species belonging to the Iridaceae family. Its worth lies in its distinctive quality and sensory properties, particularly its aroma, color, and taste (Tsimidou and Tarantilis, 2017). The plant thrives in light, well-drained soils, as adverse environmental conditions or excessive moisture can compromise its quality (Rubert et al., 2016). Major producing countries include Iran, Spain, India, and Greece. Among the processing steps, drying plays a pivotal role. The conditions used vary considerably between regions and are often based on traditional knowledge, available tools, and empirical experience (Carmona et al., 2005). Such variations have a strong impact on the chemical composition and, consequently, on the sensory characteristics of saffron, including color intensity, aroma, and bitterness.
In this study, a metabolomic approach using ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry (UHPLC-Q-Orbitrap-MS/MS) was applied to saffron samples subjected to different drying protocols. The resulting data were processed through multivariate statistical analyses and machine learning algorithms to identify patterns and discriminant markers associated with the drying treatments. This integrated strategy enabled the evaluation of chemical variability and the identification of potential biomarkers linked to specific drying conditions, thereby deepening the understanding of how post-harvest processes influence saffron quality. The findings provide valuable insights for the standardization and optimization of saffron production, with implications for quality control and product traceability.
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