Characterization and multivariate analysis of engineering properties of red sunflower seed weevil-infested achenes

Red sunflower seed weevil (RSSW, Smicronyx fulvus ) infestation compromises the structural integrity of sunflower seeds, reducing quality and marketability across North America. While most research has focused on pest biology, few studies have quantified engineering properties relevant to harvest and postharvest separation. Moreover, prior work has relied on univariate analyses, limiting the ability to capture interrelated trait changes. To address this gap, five sunflower hybrids representing different infestation levels were evaluated for physical, mechanical, and aerodynamic properties. Fixed-effects regression models were used to remove hybrid × environment effects and isolate infestation influence before multivariate analysis. Principal component analysis (PCA) and clustering were then applied to model-adjusted data (residuals) to identify dominant trait patterns and diagnostic indicators of damage. Results showed that infestation had a significant effect ( p < 0.05) on true density (ρt), rupture force (Fr), and length (L), reflecting measurable deterioration in seed compactness and structural stability. PCA revealed that the first four components explained 72% of total variance, driven mainly by density, size, and mechanical related traits, while clustering analyses grouped hybrids into three distinct multivariate classes. The combined regression-PCA-clustering framework demonstrated that ρt, Fr, and L are robust, non-destructive indicators for distinguishing infested from healthy seeds. Integrating these measurable parameters with airflow or imaging-based systems offers a pathway for high-throughput, sensor-integrated detection and sorting. These findings directly support ongoing USDA-ARS and regional research efforts in the Northern Great Plains to improve postharvest seed quality, streamline harvesting operations, and enable data-driven phenotyping in sunflower breeding and processing pipelines. • True density and rupture force effectively differentiate RSSW-infested seeds. • Regression-PCA-clustering framework isolates infestation-induced trait variation. • Infestation weakens achene compactness without altering aerodynamic response. • Identified traits enable non-destructive detection for automated seed sorting.