Quantitative analysis of Cu in Traditional Chinese medicinal materials using laser‐induced breakdown spectroscopy

Sun, Duixiong; Yang, Fengxia; Su, Maogen; Han, Weiwei; Dong, Chenzhong

doi:10.1002/mop.33240

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…For Cd, CARS showed the least variables (37), followed by RF (151) and UVE (192) and for Cu, CARS also showed the least variables (66), followed by RF (120), UVE (231) and finally for Pb, CARS showed the least variables (17), followed by UVE (93), RF (124), as can be seen from Figure 7 . All methods select the informative region around 200–1000 nm, which is consistent with the Cd, Cu and Pb results in previous literature [ 1 , 37 , 39 , 40 , 48 , 63 , 74 , 75 ], indicating that these variable intervals are the informative variables.…”

Section: Discussionsupporting

confidence: 88%

Heavy Metal Detection in Fritillaria thunbergii Using Laser-Induced Breakdown Spectroscopy Coupled with Variable Selection Algorithm and Chemometrics

Kabir

Guindo

Chen

et al. 2023

Foods

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Environmental and health risks associated with heavy metal pollution are serious. Human health can be adversely affected by the smallest amount of heavy metals. Modeling spectrum requires the careful selection of variables. Hence, simple variables that have a low level of interference and a high degree of precision are required for fast analysis and online detection. This study used laser-induced breakdown spectroscopy coupled with variable selection and chemometrics to simultaneously analyze heavy metals (Cd, Cu and Pb) in Fritillaria thunbergii. A total of three machine learning algorithms were utilized, including a gradient boosting machine (GBM), partial least squares regression (PLSR) and support vector regression (SVR). Three promising wavelength selection methods were evaluated for comparison, namely, a competitive adaptive reweighted sampling method (CARS), a random frog method (RF), and an uninformative variable elimination method (UVE). Compared to full wavelengths, the selected wavelengths produced excellent results. Overall, RC2, RV2, RP2, RSMEC, RSMEV and RSMEP for the selected variables are as follows: 0.9967, 0.8899, 0.9403, 1.9853 mg kg−1, 11.3934 mg kg−1, 8.5354 mg kg−1; 0.9933, 0.9316, 0.9665, 5.9332 mg kg−1, 18.3779 mg kg−1, 11.9356 mg kg−1; 0.9992, 0.9736, 0.9686, 1.6707 mg kg−1, 10.2323 mg kg−1, 10.1224 mg kg−1 were obtained for Cd Cu and Pb, respectively. Experimental results showed that all three methods could perform variable selection effectively, with GBM-UVE for Cd, SVR-RF for Pb, and GBM-CARS for Cu providing the best results. The results of the study suggest that LIBS coupled with wavelength selection can be used to detect heavy metals rapidly and accurately in Fritillaria by extracting only a few variables that contain useful information and eliminating non-informative variables.

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Section: Discussionsupporting

confidence: 88%

Heavy Metal Detection in Fritillaria thunbergii Using Laser-Induced Breakdown Spectroscopy Coupled with Variable Selection Algorithm and Chemometrics

Kabir

Guindo

Chen

et al. 2023

Foods

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show abstract

“…The characteristic spectral lines of specific elements can be found in the Atomic Spectral Database (ASD) of the National Institute of Standards and Technology (NIST) [ 35 , 36 ]. In quantitative analysis, in addition to traditional methods, such as internal and external scaling [ 37 ], machine learning methods, such as support vector machines (SVM) [ 38 ], principal component analysis (PCA) [ 39 ], the back propagation (BP) neural network [ 40 ], and partial least square (PLS) [ 41 ], are also becoming widely used. Machine learning methods, such as BP neural networks, SVM, and PCA, perform more satisfactorily than traditional methods such as PLS for LIBS spectral data mining.…”

Section: Instrument and Principle Of Libsmentioning

confidence: 99%

Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review

Yang

Ren

et al. 2022

Sensors

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Heavy metal pollution in agriculture is a significant problem that endangers human health. Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for material and elemental analysis, especially heavy metals, based on atomic emission spectroscopy. The LIBS technique has been widely used for rapid detection of heavy metals with its advantages of convenient operation, simultaneous detection of multi-elements, wide range of elements, and no requirement for the state and quantity of samples. However, the development of LIBS is limited by its detection sensitivity and limit of detection (LOD). Therefore, in order to improve the detection sensitivity and LOD of LIBS, it is necessary to enhance the LIBS signal to achieve the purpose of detecting heavy metal elements in agriculture. This review mainly introduces the basic instruments and principles of LIBS and summarizes the methods of enhanced LIBS signal detection of heavy metal elements in agriculture over the past 10 years. The three main approaches to enhancing LIBS are sample pretreatment, adding laser pulses, and using auxiliary devices. An enhanced LIBS signal may improve the LOD of heavy metal elements in agriculture and the sensitivity and stability of the LIBS technique. The enhanced LIBS technique will have a broader prospect in agricultural heavy metal monitoring and can provide technical support for developing heavy metal detection instruments.

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Discrimination of Salvia miltiorrhiza from Different Geographical Origins by Laser-Induced Breakdown Spectroscopy (LIBS) with Convolutional Neural Network (CNN)

et al. 2023

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Quantitative analysis of Cu in Traditional Chinese medicinal materials using laser‐induced breakdown spectroscopy

Cited by 3 publications

References 25 publications

Heavy Metal Detection in Fritillaria thunbergii Using Laser-Induced Breakdown Spectroscopy Coupled with Variable Selection Algorithm and Chemometrics

Heavy Metal Detection in Fritillaria thunbergii Using Laser-Induced Breakdown Spectroscopy Coupled with Variable Selection Algorithm and Chemometrics

Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review

Discrimination of Salvia miltiorrhiza from Different Geographical Origins by Laser-Induced Breakdown Spectroscopy (LIBS) with Convolutional Neural Network (CNN)

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