基于质构检测的仿指压触生物力与海参体形协同响应机制研究

质构是食品最重要的品质指标之一。基于压触的检测本质为分子内和分子间应力、应变场的复杂关联关系，目前通过统一规格规避形变与触力的相互影响，致使二者的协同响应机制尚不清晰，各向异性形变响应对检测的干扰无法消除。本项目以形状复杂的海参为研究对象，在微观层面明确胶原纤维与受力、形变的关联性，以感官评定为仿生模板构建检测系统，输出生物力和时间序列图像以表征仿指压触和体形的协同响应。面向与生物力具有时间对应性的体形轮廓，分析并提取尺度不变的相对形态学特征，建立时、空域融合的质构信息模型，明确生物力与体形协同响应关系；针对海参压缩和回弹的动态变化，利用图形能量和二维离散小波纹理分析，挖掘海参体形响应运动学规则，揭示体形响应与质构特性的映射规律。在此基础上，通过模糊网络构建生物力与体形信息的质构检测模型，建立仿指压触生物力与体形协同响应检测机制，为具有复杂边界形状的食品质构无损检测提供新思路。

Texture is one of the most important quality indexes of sea cucumber. Detection nature based on tactile perception is the complex association of tramolecular and intermolecular stress and strain field. Interaction between deformation and pressing force was avoided through unified specification. Currently, the response mechanism between the two is not clear, and the deformation interference for texture detection can’t be eliminated. The sea cucumber with complex profile is applied to the research. At the micro level, the relationship among collagenous fiber ,pressure and deformation is revealed. Bionic cooperative response detection system is built through sensory evaluation simulating. Bionic finger pressing force and deformation are represented by biomechanical force and serial images which are exported from the system. Specific to the profile corresponding to instantaneous biomechanical force, the scale invariant relative morphological features are extracted. The fusion model of time and spatial domain texture information is established. Collaborative response relationship of biomechanical force and deformation are illuminated. Specific to the contraction and resilience process of sea cucumber, through graphics energy and two-dimension discrete wavelet texture analysis, kinematics regularity of figure is excavated, and the law of mapping between figure response and texture parameters is revealed. On this basis, the information of pressing biomechanical force of bionic finger and dynamic figure are fused to build fuzzy detection model. Based on texture detection, the collaborative response mechanism in multiple domain is established. The new idea for food texture detection of complex figure is proposed.