Abstract
利用激光诱导击穿光谱技术结合机器学习算法, 对东北5个产地(大兴安岭、集安、恒仁、石柱、抚松)的人参进行产地识别, 建立了主成分分析算法分别结合反向传播(BP)神经网络和支持向量机算法的人参产地识别模型. 实验采集了5个产地人参共657组在200—975 nm的激光诱导击穿光谱, 经光谱数据预处理后, 对C, Mg, Ca, Fe, H, N, O等元素的8条特征谱线进行主成分分析, 原光谱数据的前3个主成分累积贡献率达到92.50%, 且样品在主成分空间中呈现良好的聚集分类. 降维后的前3个主成分以2∶1进行随机抽取, 分别作为分类算法的训练集和测试集. 实验结果表明主成分分析结合BP神经网络及支持向量机的平均识别率分别为99.08%和99.5%. 发生误判的原因是集安和石柱两地地理环境的接近而导致的H, O两元素在Ca元素离子发射谱线下的归一化强度相似. 本研究为激光诱导击穿光谱技术在人参产地的快速识别提供了方法和参考.
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