Fast Detection of Heavy Metal Content in Fritillaria thunbergii by Laser-Induced Breakdown Spectroscopy with PSO-BP and SSA-BP Analysis

Abstract

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 Fritillaria thunbergii. 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 (Rp2) 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 Fritillaria thunbergii.

Atomic spectrometry update – a review of advances in environmental analysis

“…In the LIBS analysis of plants , a back-propagation neural network that had been optimised using a sparrow search algorithm was used 168 successfully to predict concentrations of Cd, Cu and Pb in Fritillaria thunbergii . Fang et al 169 explored the possibility of analysing Cd- and Pb-spiked laurel leaves without the need for grinding and pelleting.…”

Rapid Indentification of Auramine O Dyeing Adulteration in Dendrobium officinale, Saffron and Curcuma by SERS Raman Spectroscopy Combined with SSA-BP Neural Networks Model

“…The algorithm categorizes the sparrow population into three groups: discoverers, joiners, and scouts. By considering their predatory behaviors, the search for targets can be optimized [47]. It has the characteristics of simple results, few control parameters, and strong local search ability.…”

“…The sparrow search algorithm (SSA) is a new algorithm of swarm intelligence optimization algorithm, which has the characteristics of strong optimization ability and small error. It can not only accelerate the convergence speed of the backpropagation (BP) neural network but also avoid the result falling into local extreme value [47,48]. Therefore, this technique has widespread applications in gas detection [49], trajectory prediction [50,51], and other relevant industries.…”

“…Therefore, this technique has widespread applications in gas detection [49], trajectory prediction [50,51], and other relevant industries. Luo [47] combined SSA-BP with laser-induced breakdown spectroscopy to rapidly detect the contents of Cd, Cu, and Pb in Fritillaria thunbergii, with R p 2 of 0.972, 0.991, and 0.956, respectively. Nevertheless, the integration of SERS with SSA-BP in conventional Chinese medicine has not been documented.…”

High precision and fast classification of different dimensions of Baijiu using an OptGSCV quadratic optimization network combined with AS-LIBS

“…27,28 In recent years, most of the chemometric methods for LIBS spectral data analysis have combined advanced machine learning models based on multivariate statistical techniques and various intelligent optimization algorithms. 29 Xinmeng Luo et al 30 used a particle swarm optimized (PSO) back-propagation (BP) neural network combined with LIBS data to achieve rapid detection of heavy metal content in Pinus sylvestris. Jie Ren et al 31 optimized the BP network using genetic algorithm (GA) and combined it with PSO model to achieve the detection of soil Cd content under double-pulse LIBS.…”