基于激光多普勒测振技术的梨坚实度在线实时无损检测方法研究

Firmness is a major attribute that has a strong effect on consumer perception of pear fruit quality. Accurate detection of pear firmness is very important for grading, transporting, storing, ripeness process monitoring, maturity assessment, shelf life prediction and so on. Offline non-destructive inspection of fruit internal quality by laser Doppler vibrometry (LDV) was explored under the support of NSFC and 863 Program. The results of previous researches and preliminary experiments proved that it is possible to detect the firmness of fruits by LDV. Based on above conclusions, online non-destructive detection of ‘Cuiguan’ pear by LDV is proposed in this research. The research will focus on: analyzing change pattern of major chemical components of cell wall during pear softening process to reveal their relationships with firmness; investigating the pattern of vibration deformation by finite element analysis of pear vibration modal to locate of excitation and response measurement points; setting up an online vibration spectrum measurement device for non-destructive detection of pear firmness; and building pear firmness detection model with sample size compensation function. The conclusions of this research would be useful for developing online non-destructive inspection equipment to evaluate fruit internal quality by providing a new method.

梨的坚实度是强烈影响消费者对梨品质认知的主要属性。准确地检测梨的坚实度对于采后分级、贮运、成熟度评估、成熟过程监测、货架期预测等都具有重要意义。本课题组在国家基金和"863"计划子课题的资助下，在静态条件下利用激光多普勒测振技术在农产品内部品质无损检测方面做了积极的探索性研究，前期研究和预实验的结果证明了基于激光多普勒测振技术无损检测水果坚实度的可行性。本课题组将在此基础上，以浙江省种植面积最大的翠冠梨为研究对象，分析梨软化过程中细胞壁主要化学成分含量的变化规律，揭示其与果肉坚实度之间的相关关系，进行梨振动模态的有限元分析，解析梨振动变形的规律，提出最佳激励及响应感测位置，构建梨振动波谱在线实时无损检测装置，建立可根据样本的大小进行自修正的梨坚实度检测模型，最终获得基于激光多普勒测振技术的梨坚实度在线实时无损检测方法，为研发具有我国自主知识产权的水果内部品质在线无损检测装备提供新方法。

坚实度是水果品质的重要指标，对于采后分级、贮运、成熟度评估、成熟过程监测、货架期预测等都具有重要意义。本项目利用激光多普勒测振技术在非接触式测量微弱振动方面的优势，探索了基于激光多普勒测振技术的梨坚实度在线实时无损检测方法，为水果品质在线检测提供了新方法和新装备。本项目建立了梨的有限元仿真模型，明确了梨第二至五共振频率对应的振型，基于有限元谐响应分析解析了弹性模量、密度、泊松比和质量对梨共振频率的影响，研究了梨软化过程中细胞壁主要化学成分的变化规律及其与坚实度之间的关系，研制了非接触式激振单元，构建了梨坚实度在线实时无损检测系统，获取了梨坚实度相关的振动参数（共振峰中心频率f ，共振峰宽度w ，共振峰面积S 和共振峰峰值A），利用BP神经网络算法建立了梨坚实度定性检测模型（校正集总的准确率为94.74%，预测集总的准确率为89.47%），利用多元线性回归算法建立了可对果重和果形进行自修正的梨坚实度检测模型（rc=0.8808，RMSEC=0.3316），从而获得了基于激光多普勒测振技术的梨坚实度在线实时无损检测方法。本项目发表/录用学术期刊论文和会议论文6篇，其中SCI期刊论文5篇、国际会议EI论文1篇，申请发明专利5项（授权1项，实质审查1项）、实用新型专利5项（授权4项），获得软件登记2项，通过项目培养博士研究生1名，硕士研究生2名（其中1名由硕士转为博士）。