A multiscale computation study on bruise susceptibility of blueberries from mechanical impact

Authors: HOU, JIXIN - University Of Georgia; Park, Bosoon; LI, CHANGYING - University Of Florida; WANG, XIANQIAO - University Of Georgia

Submitted to: Postharvest Biology and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/3/2023
Publication Date: 11/10/2023
Citation: Hou, J., Park, B., Li, C., Wang, X. 2023. A multiscale computation study on bruise susceptibility of blueberries from mechanical impact. Postharvest Biology and Technology. https://doi.org/10.1016/j.postharvbio.2023.112660.
DOI: https://doi.org/10.1016/j.postharvbio.2023.112660

Interpretive Summary: Blueberries are emerging as one of the most popular fruits worldwide due to their abundant health benefits such as anti-inflammatory properties, support for ocular health, and mitigation of cardiovascular disease risk. However, alongside high demand for blueberries, a significant number of berries with damages such as splitting, mold, decay, and bruising, incurred during harvesting and postharvest handling processes are wasted due to quality deterioration, resulting in losses of up to 51% from production to consumption. Among these damages, bruising stands out as the most prevalent issue. Bruising is a significant concern during the mechanical harvesting and postharvest handling process of fresh-market blueberries. Therefore, predicting bruising accurately is essential for characterizing and enhancing the quality of blueberries. In this study, the material properties of blueberries were measured using compression tests. Subsequently, a multiscale finite element model was constructed based on the anatomical structures of blueberries and employed to simulate the bruise susceptibility during drop impacts. The influence of drop height, impact angle, and contact surface materials on the blueberry bruising was investigated, and empirical models to predict bruise susceptibility were developed specifically for each contact surface material.

Technical Abstract: Impact bruising is a prevalent form of mechanical damage that occurs in blueberries during harvesting and postharvest handling processes. The accurate quantification of bruising remains a challenging task, primarily due to the limitations of available technologies. To address this issue, herein, the data-enabled computational method was employed to evaluate the bruise susceptibility of blueberries under various free drop scenarios. A multiscale computational model, comprising the skin, flesh, and seeds, was constructed based on the anatomical structures of blueberries. The mechanical properties were obtained through compression tests, and a linear elastic-plastic model was identified for capturing the mechanical responses of blueberries under compression. To validate the computational model, free drop experiments were conducted, and the equivalent plastic strain (PEEQ) metric from simulations effectively captured bruising distribution in drop impacts with a bruising threshold value set at 0.1. A total of 120 cases were simulated, considering various drop heights, impact angles, and contact surface materials. Based on simulation results, empirical models to predict bruise susceptibility for each contact surface material using the response surface methods were developed. The results revealed that bruise susceptibility was influenced by the modulus ratios between the contact surface material and blueberry with a linear positive relationship to drop heights. In addition, the effect of impact angle was contingent on the drop height. At lower drop heights, greater bruising occurred at vertical impact angles. As the drop height increased, the blueberries became more susceptible to bruising at the horizontal impact angles. The specific transition in drop height varied with contact surface materials. These results demonstrate the capability of the multiscale computational model to simulate drop scenarios and assess the bruise susceptibility of blueberries. The theoretical insights provided by this study offer valuable guidance for optimizing fresh-market blueberry mechanical harvesting machine design and postharvest handling processes for longer shelf-life.