Trait-Based Modeling of Surface Cooling Dynamics in Olive Fruit Using Thermal Imaging and Mixed-Effects Analysis

Abstract

Effective postharvest cooling of olive fruit is increasingly critical under rising harvest temperatures driven by climate change. This study models passive cooling dynamics using a trait-based, mixed-effects statistical framework. Ten olive groups-representing seven cultivars and different ripening or size stages-were subjected to controlled cooling conditions. Surface temperature was recorded using infrared thermal imaging, and morphological and compositional traits were quantified. Temperature decay was modeled using Newton's Law of Cooling, extended with a quadratic time term to capture nonlinear trajse thectories. A linear mixed-effects model was fitted to log-transformed, normalized temperature data, incorporating trait-by-time interactions and hierarchical random effects. The results confirmed that fruit weight, specific surface area (SSA), and specific heat capacity (SHC) are key drivers of cooling rate variability, consistent with theoretical expectations, but quantified here using a trait-based statistical model applied to olive fruit. The quadratic model consistently outperformed standard exponential models, revealing dynamic effects of traits on temperature decline. Residual variation at the group level pointed to additional unmeasured structural influences. This study demonstrates that olive fruit cooling behavior can be effectively predicted using interpretable, trait-dependent models. The findings offer a quantitative basis for optimizing postharvest cooling protocols and are particularly relevant for maintaining quality under high-temperature harvest conditions.