基于生物光子的小麦新陈度快速无损检测  

Fast and Non-Destructive Determination on Fresh Degree of Wheat Kernels Based on Biophotons

作　　者：巩跃洪[1] 杨铁军[2] 梁义涛[1,3] 葛宏义 GONG Yue-hong;YANG Tie-jun;LIANG Yi-tao;GE Hong-yi(School of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China;School of Artificial Intelligence and Big Data,Henan University of Technology,Zhengzhou 450001,China;Key Laboratory of Grain Information Processing&Control,Ministry of Education,Henan University of Technology,Zhengzhou450001,China)

机构地区：[1]河南工业大学信息科学与工程学院,河南郑州450001 [2]河南工业大学人工智能与大数据学院,河南郑州450001 [3]河南工业大学粮食信息处理与控制教育部重点实验室,河南郑州450001

出　　处：《光谱学与光谱分析》2021年第7期2166-2170,共5页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(31171775,61705061,61975053);国家粮食公益性行业科研专项项目(201413001)资助。

摘　　要：小麦籽粒作为一种活的生命体,在正常储藏过程中,会不断消耗自身的营养物质来维持其生命活动。随着储藏时间的推移,小麦籽粒内部各种酶的活性减弱或丧失,自身呼吸强度逐步降低,原生质胶体结构松弛,籽粒的物理和化学状态发生改变,进而导致其后续食用和加工品质变劣。因此,对小麦新陈度的准确判定,是保证储藏小麦数量和质量的前提,对指导我国粮食储存具有重要的经济和社会意义。目前常用的小麦新陈度鉴定方法主要包括感官判定法和各种生化类方法;前者主要依赖操作者个人的主观经验,容易受到外界因素的干扰,可重复性较差,判定结果因人而异,只适合作小麦新陈度鉴定的辅助方法。后者虽然判定精度较高,但整个检测过程耗时过长,一般需要对待测样品进行复杂预处理,且检测过程中用到的多种化学试剂会对环境造成一定的污染。因此,迫切需要研究出一套快速、准确、绿色的小麦新陈度鉴定方法。利用生物光子仪器分别测试了5种不同储藏年份小麦样品的生物光子信号,并结合改进多尺度排列熵算法对2015年—2018年四种小麦样品的光子信号进行特征分析,最后借助反向传播神经网络对这4种不同储藏年份的小麦进行分类验证。实验结果表明,不同储藏年份小麦的自发光子量存在一定的差异,其中2019年小麦样品产生的光子数量明显高于其他年份的小麦样品,其余年份小麦样品光子数量的排列熵值随着储藏年限的增加而增大。对比实验结果显示,改进多尺度排列熵算法在很大程度上解决了由多尺度排列熵算法引起的信号抖动和突变问题,可以作为一种明显的特征来标识小麦的新陈度。最后借助BP神经网络进行分类测试,输出结果证明新构建的分类模型的准确度可以达到95%,能够实现对不同年份小麦新陈度的准确鉴别。Wheat kernels,as a type of living organisms,will continue consuming the nutrients of themselves to maintain their vital activities during the normal storage period.With the increase of storage time,various enzymes inside wheat kernels decrease or lose their activities,the intensity of respiration decreasing gradually,the colloid structure of protoplasm getting relaxed,and then the physical and chemical states of wheat kernels have changed,which result in the deterioration of subsequent edible and processing quality.Therefore,it is of great economic value and social significance for our country to carry out accurate fresh degree detection to stored wheat and ensure the quantity and quality of wheat kernels.The identification methods commonly used for fresh wheat degree mainly include sensory determination method and various biochemical methods.The former method with poor repeatability,mainly depending on the operator’s subjective experiences,is easily disturbed by external factors,and has an obvious error in the determination results,which is always used as a sort of auxiliary testing method in the aspect of wheat quality detection.Although the latter’s accuracy is high,the whole detection process is time-consuming,and it is usually involved in a complex pretreatment for the tested samples.Meanwhile,various chemical reagents used in the detection process may cause certain pollution to the environment.Thus,it is urgent to establish a fast,accurate and green identification method for the fresh wheat degree.Special biophotonic instruments have tested biophoton signals of stored wheat kernels in five different years in this paper,and then combined the improved multiscale permutation entropy algorithm to analyze the features of wheat biophoton signals in four years from 2015 to 2018,finally,taking advantage of backpropagation neural network to classify the fresh wheat degree in four years.Experimental results show that there exist certain differences in the spontaneous biophoton number of wheat kernels stored in di

关 键 词：生物光子 小麦 新陈度 改进多尺度排列熵 

分 类 号：O432.1[机械工程—光学工程]