Identification and classification of imported iron ores according to their production countries and brands are required for quality control and safety in the iron ore trade. In this work, a method based on laser-induced breakdown spectroscopy (LIBS) combined with principal component analysis (PCA) and scaled conjugate gradient (SCG) algorithm in an artificial neural network (ANN) was used to identify 138 brands of iron ore samples from Australia, Brazil and South Africa. Quadratic fitting, Savitzky–Golay polynomial smoothing and Multiplicative Scatter Correction were applied to preprocess spectra in combination. The pretreatment data remove the noise and enhance the sensitivity of the spectra. PCA was employed to reduce the dimensionality of the data. In particular, the loadings for PC1 and PC2 included Fe, Na, Ca, Mg and Al lines. Among them, the first 3 were the most important with the largest contributions to the result of classification. With the developed PCA-ANN model, the accuracy rates of classification according to the production countries reached 100% for the validation and test data sets, respectively. With the final brand classification model, after being optimized for the number of neurons in the hidden layer, the accuracy rates of classification reached 99.19% and 99.19%, respectively, for the validation and test data sets.

中文翻译：

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结合主成分分析和人工神经网络的激光诱导击穿光谱法对铁矿石品牌进行分类识别

根据进口铁矿石的生产国和品牌对进口铁矿石进行识别和分类，对于铁矿石贸易的质量控制和安全性是必需的。在这项工作中，基于人工神经网络（ANN）的基于激光诱导击穿光谱（LIBS）结合主成分分析（PCA）和比例共轭梯度（SCG）算法的方法被用来识别138个品牌的铁矿石样品来自澳大利亚，巴西和南非。二次拟合，Savitzky-Golay多项式平滑和乘法散射校正被组合用于预处理光谱。预处理数据消除了噪声并增强了光谱的灵敏度。使用PCA来减少数据的维数。特别是，PC1和PC2的负载量包括Fe，Na，Ca，Mg和Al线。其中，前三个是最重要的，对分类结果的贡献最大。使用开发的PCA-ANN模型，对于验证和测试数据集，根据生产国分类的准确率分别达到100％。使用最终的品牌分类模型，在针对隐藏层中的神经元数量进行优化之后，对于验证和测试数据集，分类的准确率分别达到了99.19％和99.19％。