高级检索

基于激光诱导击穿光谱和神经网络的蛋壳研究

Research on eggshell via laser-induced breakdown spectroscopy and neural network

  • 摘要: 为了研究残缺蛋壳的分类方法以及某些蛋制品中存在的食品安全问题,采用激光诱导击穿光谱技术(LIBS)和反向传播神经网络(BPNN)相结合的方法开展了对于蛋壳元素的探究、不同种类蛋壳的甄别以及蛋壳中污染元素的检测工作。结果表明,鸭蛋壳中含有Si,Cu,Ca,Mg,C,Na和Al等元素;采用LIBS测量并标定污染的皮蛋壳中的元素组成,成功探测到了明显的铅元素特征峰;对鸡蛋壳、鸭蛋壳和鹌鹑蛋壳进行快速的甄别,得到了94.167%的准确率;对鸭蛋壳和皮蛋壳进行不同制作方法的蛋壳分类,获得了97.5%的准确率。LIBS与BPNN的结合为蛋壳的分类与甄别提供了一个新的思路与研究方法。

     

    Abstract: In order to study the classification method of incomplete eggshells and the food safety problems in some egg products, laser-induced breakdown spectroscopy(LIBS) and back propagation neural network(BPNN) were combined to explore the elements of eggshells, to distinguish different types of eggshells, and to detect pollution elements in eggshells, respectively. The results show that the duck eggshell contains Si, Cu, Ca, Mg, C, Na, Al and other elements. LIBS was applied to measure and calibrate the element composition in contaminated preserved eggshells, and the obvious lead characteristic peaks were successfully detected. LIBS and BPNN were combined to quickly distinguish eggshell, duck eggshell, and quail eggshell, and an accuracy rate of 94.167% was obtained. The same method was employed to study on duck eggshell and preserved eggshell to explore the classification of eggshells with different production methods, and an accuracy rate of 97.5% was obtained. The combination of LIBS and BPNN provides a new research method for egg classification and distinguishing.

     

/

返回文章
返回