Study on Microdamage Quantitative Analysis of Cd and Pb in Leaves by Laser Induced Breakdown Spectroscopy

Li, Fang; Ma, Ming; Yin, Gaofang; Chen, Xiaowei; Chen, Fuqiang; Zhao, Nanjing

doi:10.3390/chemosensors10070242

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…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. For calibration, small (10 mm diameter) wafers were cut from leaves that had been soaked in solutions with different analyte concentrations for two days, rinsed with deionised water and dried before the laser interrogation.…”

Section: Analysis Of Soils Plants and Related Materialsmentioning

confidence: 99%

Atomic spectrometry update – a review of advances in environmental analysis

Bacon,

Butler,

Cairns

et al. 2024

J. Anal. At. Spectrom.

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Section: Analysis Of Soils Plants and Related Materialsmentioning

confidence: 99%

Atomic spectrometry update – a review of advances in environmental analysis

Bacon,

Butler,

Cairns

et al. 2024

J. Anal. At. Spectrom.

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“…Three Cd emission lines were pinpointed at wavelengths of 214.44, 226.50, and 228.80 nm. These three Cd emission lines are commonly utilized as analytical lines for Cd detection [17,19,21]. In addition, some other emission lines with higher intensity were also identified and are listed in Table 3.…”

Section: Spectral Profilementioning

confidence: 99%

“…The emission lines of LIBS that characterize the substance's features can be applied for qualitative and quantitative analysis in coal production [13], agriculture [14,15], soil [16], and so on. Cd detection using LIBS has been researched for use in lots of plants, such as cabbage [17], herbs [18,19], lettuce [20], and rice [21]. The bottleneck that causes a relatively lower measurement precision and accuracy of LIBS toxic metal quantification is signal uncertainty, which hinders further development [22].…”

Section: Introductionmentioning

confidence: 99%

Crater–Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy

Chen,

Li,

et al. 2024

Foods

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Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate cadmium (Cd) poses a significant environmental pollution risk and potential health hazards to humans. In this study, we employed laser-induced breakdown spectroscopy (LIBS) for the rapid detection of Cd. It is important to note that signal uncertainty can impact the quantification performance of LIBS. Hence, we proposed the crater–spectrum feature fusion method, which comprises ablation crater morphology compensation and characteristic peak ratio correction (CPRC), to explore the feasibility of signal uncertainty reduction. The crater morphology compensation method, namely, adding variables using multiple linear regression (MLR) analysis, decreased the root-mean-square error of the prediction set (RMSEP) from 7.0233 μg/g to 5.4043 μg/g. The prediction results were achieved after CPRC pretreatment using the calibration curve model with an RMSEP of 3.4980 μg/g, a limit of detection of 1.92 μg/g, and a limit of quantification of 6.41 μg/g. The crater–spectrum feature fusion method reached the lowest RMSEP of 2.8556 μg/g, based on a least-squares support vector machine (LSSVM) model. The preliminary results suggest the effectiveness of the crater–spectrum feature fusion method for detecting Cd. Furthermore, this method has the potential to be extended to detect other toxic metals in addition to Cd, which significantly contributes to ensuring the quality and safety of agricultural production.

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“…LIBS, with its capability of comprehensive elemental detection [ 22 , 23 , 24 ], can classify Chinese medicinal herbs by analyzing the elemental spatial information contained in the collected LIBS spectra. In recent years, several researchers have conducted relevant studies [ 25 , 26 , 27 , 28 , 29 , 30 ]. They have utilized LIBS technology for the detection of Chinese medicinal materials such as Dendrobium.…”

Section: Introductionmentioning

confidence: 99%

“…They have utilized LIBS technology for the detection of Chinese medicinal materials such as Dendrobium. Fang et al [ 25 ] proposed a method for analyzing LIBS data of damaged medicinal leaves, achieving the quantification of Cd and Pb in them. Zhu et al [ 26 ] used standard addition and internal standard methods to analyze LIBS spectral data, detecting harmful trace elements in licorice.…”

Section: Introductionmentioning

confidence: 99%

Identification of Dendrobium Using Laser-Induced Breakdown Spectroscopy in Combination with a Multivariate Algorithm Model

Zhang,

Liu,

et al. 2024

Foods

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Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs.

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Study on Microdamage Quantitative Analysis of Cd and Pb in Leaves by Laser Induced Breakdown Spectroscopy

Cited by 4 publications

References 12 publications

Atomic spectrometry update – a review of advances in environmental analysis

Atomic spectrometry update – a review of advances in environmental analysis

Crater–Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy

Identification of Dendrobium Using Laser-Induced Breakdown Spectroscopy in Combination with a Multivariate Algorithm Model

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