Abstract A nondestructive and rapid method has been put forward to grade tobacco leaves in the paper. The method is based on a combination of a hand-held near-infrared spectrometer and a particle swarm optimization-extreme learning machine algorithm. Firstly, the spectral data of the training samples are collected directly from the tobacco leaves nondestructively by using a hand-held near infrared spectrometer without any pretreatment. Secondly, the training models of different classes are built using particle swarm optimization-extreme learning machine algorithm. Finally, the classes of test samples can be predicted by using the developed models. Besides, the classification results of particle swarm optimization-extreme learning machine algorithm are also compared with that of the traditional linear discriminant analysis, support vector machine, and extreme learning machine algorithms, respectively. The experimental results show the classification accuracy of the particle swarm optimization-extreme learning machine algorithm is comparable after the parameter optimization. It indicates that the interplay between the hand-held near-infrared spectroscopy technology and particle swarm optimization-extreme learning machine algorithm will provide a novel classification method for grading tobacco leaves in the purchasing process on the spot.

中文翻译：

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基于粒子群优化-极限学习机算法的手持式近红外光谱仪无损快速分级烟叶

摘要 本文提出了一种无损、快速的烟叶分级方法。该方法基于手持式近红外光谱仪和粒子群优化-极限学习机算法相结合。首先，使用手持式近红外光谱仪直接从烟叶中无损采集训练样本的光谱数据，无需任何预处理。其次，使用粒子群优化-极限学习机算法建立不同类别的训练模型。最后，可以使用开发的模型预测测试样本的类别。此外，还将粒子群优化-极限学习机算法的分类结果与传统的线性判别分析、支持向量机、和极限学习机算法。实验结果表明，粒子群优化-极限学习机算法经过参数优化后的分类精度具有可比性。表明手持近红外光谱技术与粒子群优化-极限学习机算法的相互作用将为现场采购过程中的烟叶分级提供一种新的分类方法。