College of Mathematics and Computer Science, Zhejiang A&F University, 666 Wusu Street, Hangzhou 311300, China.
College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
Molecules. 2023 Apr 11;28(8):3360. doi: 10.3390/molecules28083360.
Fast detection of heavy metals is important to ensure the quality and safety of herbal medicines. In this study, laser-induced breakdown spectroscopy (LIBS) was applied to detect the heavy metal content (Cd, Cu, and Pb) in . Quantitative prediction models were established using a back-propagation neural network (BPNN) optimized using the particle swarm optimization (PSO) algorithm and sparrow search algorithm (SSA), called PSO-BP and SSA-BP, respectively. The results revealed that the BPNN models optimized by PSO and SSA had better accuracy than the BPNN model without optimization. The performance evaluation metrics of the PSO-BP and SSA-BP models were similar. However, the SSA-BP model had two advantages: it was faster and had higher prediction accuracy at low concentrations. For the three heavy metals Cd, Cu and Pb, the prediction correlation coefficient (R) values for the SSA-BP model were 0.972, 0.991 and 0.956; the prediction root mean square error (RMSEP) values were 5.553, 7.810 and 12.906 mg/kg; and the prediction relative percent deviation (RPD) values were 6.04, 10.34 and 4.94, respectively. Therefore, LIBS could be considered a constructive tool for the quantification of Cd, Cu and Pb contents in .
快速检测重金属对于确保中草药的质量和安全非常重要。本研究采用激光诱导击穿光谱（LIBS）技术检测 中的重金属含量（Cd、Cu 和 Pb）。使用粒子群优化（PSO）算法和麻雀搜索算法（SSA）优化的反向传播神经网络（BPNN）分别建立了 PSO-BP 和 SSA-BP 定量预测模型。结果表明，经 PSO 和 SSA 优化的 BPNN 模型比未经优化的 BPNN 模型具有更高的准确性。PSO-BP 和 SSA-BP 模型的性能评估指标相似，但 SSA-BP 模型具有两个优点：速度更快，在低浓度下具有更高的预测精度。对于 Cd、Cu 和 Pb 这三种重金属，SSA-BP 模型的预测相关系数（R）值分别为 0.972、0.991 和 0.956；预测均方根误差（RMSEP）值分别为 5.553、7.810 和 12.906 mg/kg；预测相对百分偏差（RPD）值分别为 6.04、10.34 和 4.94。因此，LIBS 可被视为定量检测 中 Cd、Cu 和 Pb 含量的一种有前途的工具。
