Elements such as minerals and heavy metals play important roles in the nutrition and safety of agricultural products. It is necessary to develop rapid, online, real-time and in situ methods for monitoring the substances in farm products. Gannan navel oranges are a unique variety of fruit, which may be affected by Cu pollution due to abundant copper mines and other factors. An online identification and classification system based on laser-induced breakdown spectroscopy (LIBS) was developed to detect possible Cu residue in Gannan navel oranges. First, transmission and classification equipment for Gannan navel oranges was built. Second, an LIBS detection module was designed. Finally, a software system for the whole online detection platform was developed based on the C# programming language. The series of operations for the online detection system, which includes the loading, transmission, detection and classification of orange samples, can be controlled. Since the navel orange has an elliptical shape, the LIBS detection module was designed with a long focal length to reduce the influence of fruit plane size fluctuation. The long focal length was optimized to 698 mm, and the depth of field was ±6 mm. Furthermore, a parameter optimization model using a support vector machine (SVM) based on an improved genetic algorithm (IGA) is proposed to improve the classification effect of Gannan navel oranges. This model avoids the over-learning or under-learning caused by improper parameter selection in the regression prediction of SVM. The IGA is used to optimize the penalty parameter c and the kernel parameter g of SVM. LIBS spectral data from two types of navel orange samples with and without Cu contamination were selected as test datasets, and the classification results were compared with those of the standard genetic algorithm-support vector machine (GA-SVM). The investigation showed that the IGA-SVM can provide better classification of navel oranges based on analysis of the LIBS spectral data, and the classification accuracy can reach 98%, which provides significant guidance for the use of LIBS to quickly realize online screening of heavy metals in agriculture products.

中文翻译：

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基于 IGA 优化的 SVM 的 LIBS 在线鉴定和分类 Cu 污染的赣南脐橙

矿物质和重金属等元素对农产品的营养和安全起着重要作用。需要快速、在线、实时、就地开发农产品中物质的监测方法。赣南脐橙是一种特有的水果品种，由于铜矿资源丰富等因素，可能会受到铜污染。开发了基于激光诱导击穿光谱（LIBS）的在线鉴定和分类系统，用于检测赣南脐橙中可能存在的铜残留。一是建成赣南脐橙输送分级设备。其次，设计了LIBS检测模块。最后，基于C#编程语言开发了整个在线检测平台的软件系统。可控制在线检测系统的橙样上样、传输、检测、分类等一系列操作。由于脐橙呈椭圆形，LIBS检测模块采用长焦距设计，减少果面尺寸波动的影响。长焦距优化为698mm，景深为±6mm。此外，提出了一种基于改进遗传算法（IGA）的支持向量机（SVM）参数优化模型，以提高赣南脐橙的分类效果。该模型避免了SVM回归预测中参数选择不当造成的过度学习或学习不足。IGA用于优化惩罚参数 为提高赣南脐橙分类效果，提出了基于改进遗传算法（IGA）的支持向量机（SVM）参数优化模型。该模型避免了SVM回归预测中参数选择不当造成的过度学习或学习不足。IGA用于优化惩罚参数 为提高赣南脐橙分类效果，提出了基于改进遗传算法（IGA）的支持向量机（SVM）参数优化模型。该模型避免了SVM回归预测中参数选择不当造成的过度学习或学习不足。IGA用于优化惩罚参数c和SVM的核参数g。选取含Cu污染和不含Cu污染的两类脐橙样品的LIBS光谱数据作为测试数据集，并将分类结果与标准遗传算法-支持向量机（GA-SVM）的分类结果进行比较。调查表明，IGA-SVM基于LIBS光谱数据分析能够更好地对脐橙进行分类，分类准确率可达98%，为利用LIBS快速实现重金属在线筛查提供了重要指导。在农产品中。