Modeling and quantifying the synergistic impact of Tribolium castaneum infestation and storage temperature on lipid deterioration in wheat flour

Post-harvest losses in wheat flour, a key finished grain product, arise significantly from insect infestation and biochemical deterioration during storage. This study investigated the combined effects of the red flour beetle ( Tribolium castaneum ), a major secondary pest, and storage temperature on lipid deterioration, measured by fatty acid value (FAV). Over 150 days, flour samples were stored under varying temperatures (ambient, 15, 25, 30 °C) and infested with different initial insect densities (0,2,4,6,8 adults/500g). Results demonstrated a strong synergistic interaction: both higher temperatures and insect densities significantly accelerated the increase in FAV. To accurately analyze the repeated-measures data exhibiting heteroscedasticity, a Generalized Least Squares (GLS) model was developed. The model quantified the significant influence of temperature, time (including its quadratic and periodic trends), and insect density on FAV. It exhibited excellent performance, with a high goodness-of-fit ( R 2 = 0.9262) and superior predictive ability (RPD = 4.44). Notably, at 30 °C with an insect density of 8 adults/500g, FAV increased by an average of 45.09 units compared to the baseline (0 adults, 15 °C). This study provides a novel, quantitative predictive tool that bridges pest ecology and quality monitoring, offering critical data-driven insights for optimizing integrated pest management (IPM) strategies and enabling risk-based decision-making in bulk flour storage to reduce economic losses. • A GLS model quantifies the synergistic impact of T. castaneum density and storage temperature on flour lipid deterioration. • I nfestation accelerates FAV increase in a density-dependent manner at 25–30 °C, but not at 15 °C. • The GLS model handles complex data ( R 2 = 0.926, RPD = 4.44), enabling acuurate prediction of quality loss. • At 30°C with 8 adults/500g , FAV increased by 45.09 units beyond temperature effects alone. • This predictive tool supports improved IPM and risk-based “First-Expired, First-Out” flour storage .