Abstract Laser-induced breakdown spectroscopy (LIBS) coupled with particle swarm optimization-kernel extreme learning machine (PSO-KELM) method was developed for classification and identification of six types Salvia miltiorrhiza samples in different regions. The spectral data of 15 Salvia miltiorrhiza samples were collected by LIBS spectrometer. An unsupervised classification model based on principal components analysis (PCA) was employed first for the classification of Salvia miltiorrhiza in different regions. The results showed that only Salvia miltiorrhiza samples from Gansu and Sichuan Province can be easily distinguished, and the samples in other regions present a bigger challenge in classification based on PCA. A supervised classification model based on KELM was then developed for the classification of Salvia miltiorrhiza, and two methods of random forest (RF) and PSO were used as the variable selection method to eliminate useless information and improve classification ability of the KELM model. The results showed that PSO-KELM model has a better classification result with a classification accuracy of 94.87%. Comparing the results with that obtained by particle swarm optimization-least squares support vector machines (PSO-LSSVM) and PSO-RF model, the PSO-KELM model possess the best classification performance. The overall results demonstrate that LIBS technique combined with PSO-KELM method would be a promising method for classification and identification of Salvia miltiorrhiza samples in different regions.

中文翻译：

---

通过激光诱导击穿光谱（LIBS）和粒子群优化-内核极限学习机（PSO-KELM）根据地理区域快速区分丹参

摘要 建立了激光诱导击穿光谱（LIBS）结合粒子群优化核极限学习机（PSO-KELM）方法对不同地区的六种丹参样品进行分类鉴定。LIBS光谱仪采集了15份丹参样品的光谱数据。首先采用基于主成分分析（PCA）的无监督分类模型对不同地区的丹参进行分类。结果表明，只有甘肃和四川省的丹参样品可以轻松区分，其他地区的样品在基于PCA的分类上面临更大的挑战。然后开发了基于 KELM 的监督分类模型用于丹参的分类，并采用随机森林(RF)和PSO两种方法作为变量选择方法，剔除无用信息，提高KELM模型的分类能力。结果表明，PSO-KELM模型具有更好的分类效果，分类准确率为94.87%。与粒子群优化-最小二乘支持向量机（PSO-LSSVM）和PSO-RF模型得到的结果相比，PSO-KELM模型具有最好的分类性能。总体结果表明，LIBS 技术与 PSO-KELM 方法相结合将是一种很有前景的方法，可以对不同地区的丹参样品进行分类和鉴定。结果表明，PSO-KELM模型具有更好的分类效果，分类准确率为94.87%。与粒子群优化-最小二乘支持向量机（PSO-LSSVM）和PSO-RF模型得到的结果相比，PSO-KELM模型具有最好的分类性能。总体结果表明，LIBS 技术结合 PSO-KELM 方法将是一种很有前景的方法，可以对不同地区的丹参样品进行分类和鉴定。结果表明，PSO-KELM模型具有更好的分类效果，分类准确率为94.87%。与粒子群优化-最小二乘支持向量机（PSO-LSSVM）和PSO-RF模型得到的结果相比，PSO-KELM模型具有最好的分类性能。总体结果表明，LIBS 技术结合 PSO-KELM 方法将是一种很有前景的方法，可以对不同地区的丹参样品进行分类和鉴定。