The spectral fusion of laser-induced breakdown spectroscopy (LIBS) and mid-infrared spectroscopy (MIR) coupled with random forest (RF) for the quantitative analysis of soil pH

Chen, Tingting; Men, Jing; Zhao, Mingjing; Zhang, Tianlong; Li, Hua

doi:10.1039/d0ja00524j

Cited by 11 publications

(4 citation statements)

References 32 publications

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“…For improving the performance of the calibration model, spectral pretreatment and variable selection strategy need to be implied in the modeling process. Common spectral pretreatment methods for LIBS mainly consist of baseline correction, noise filtering, overlapping, peak resolution, and data compression, such as multivariate scattering correction (MSC), standard normal variation (SNV), Savitzky-Golay (SG) convolution derivation, wavelet transform (WT), first-order derivation (D1st), second-order derivation (D2nd), etc. − Similarly, feature selection refers to selecting the optimal feature from the original feature subset based on an evaluation criterion, which is an essential approach to improve the performance of machine learning algorithms . Variable importance measurement (VIM), variable importance projection (VIP), mutual information (MI), successive projections algorithm (SPA), genetic algorithm (GA), etc.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning

et al. 2023

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Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R 2 P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.

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Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning

et al. 2023

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“…27 Chen et al combined standard normal variation (SNV) spectral preprocessing and feature variable extraction methods to establish an RF calibration model based on the mid-infrared (MIR) spectra and LIBS spectra for qualitative analysis of soil pH. 18 In our previous research, we investigated the laser trapping−LIBS combined with RF to quantitatively analyze four metal elements in a single particle. 28 However, being a single particle, in the process of LIBS signal acquisition, the size of the single particle and other factors will cause instability of the spectral signal.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Multivariate calibration methods based on machine learning have been regarded as promising methods in modern spectral analysis techniques, with the advantages of higher accuracy and precision . It has been demonstrated that the accuracy of quantitative analysis of LIBS is greatly improved through spectral pretreatment and an appropriate variable extraction method from the complex LIBS spectral data matrix for eliminating the interference and then establishing the calibration model by machine learning methods, such as the random forest (RF), extreme learning machine (ELM), , artificial neural network (ANN), and support vector machine (SVM) . Among them, RF is of particular interest as a senior integrated machine learning method that has the ability to overcome over-fitting and has a large tolerance to noise and outliers .…”

Section: Introductionmentioning

confidence: 99%

“…Feng et al combined LIBS and the wavelet transform (WT)-VIM-RF algorithm to evaluate the pollution risk of the Cu element in atmospheric samples . Chen et al combined standard normal variation (SNV) spectral preprocessing and feature variable extraction methods to establish an RF calibration model based on the mid-infrared (MIR) spectra and LIBS spectra for qualitative analysis of soil pH . In our previous research, we investigated the laser trapping–LIBS combined with RF to quantitatively analyze four metal elements in a single particle .…”

Section: Introductionmentioning

confidence: 99%

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Multi-element Quantitative Analysis of Single Micro-sized Suspended Particles in Air with High Accuracy Based on Random Forest and Variable Selection Strategies

et al. 2022

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The chemical compositions of atmospheric particles have been studied for several decades, and the traditional techniques for particle analysis usually require time-consuming sample preparation. Within this study, simultaneous quantitative detection of multiple metallic species (Zn, Cu, and Ni) in single micro-sized suspended particles was investigated by combining random forest (RF) and variable selection strategies. Laser-induced breakdown spectra of 15 polluted black carbon samples were applied for establishing the RF model, and the movmean smoothing spectral pretreatment method and variable selection methods [variable importance measurement (VIM), genetic algorithm (GA), and variable importance projection (VIP)] were proposed. Finally, the optimized RF calibration model with the evaluation indicators of mean relative error (MRE), root-mean-square error (RMSE), and coefficient of determination (R 2) was constructed based on the optimal input variables and model parameters. Compared with the univariate regression method, the VIP-RF (Zn) and VIM-RF (Cu and Ni) models showed a better correlation relationship (R p 2 = 0.9662 for Zn, R p 2 = 0.9596 for Cu, and R p 2 = 0.9548 for Ni). For Zn, Cu, and Ni, the values of RMSEP (RMSE of prediction) decreased by 116.44, 68.94, and 102.10 ppm, while the values of MREP (MRE of prediction) decreased by 67, 55, and 48%, respectively. The values of ratio of prediction to deviation (RPD) of VIP-RF (Zn), VIM-RF (Cu), and VIM-RF (Ni) models were 5.4, 5.0, and 4.7, respectively. The performance of this combined approach displays a notable accuracy improvement in the quantitative analysis of single particles, suggesting that it is a promising tool for real-time air particulate matter pollution monitoring and control in the future.

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Discrimination of Radix Astragali according to geographical regions by data fusion of laser induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) combined with random forest (RF)

Wei

Feng

et al. 2022

Chinese Journal of Analytical Chemistry

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The spectral fusion of laser-induced breakdown spectroscopy (LIBS) and mid-infrared spectroscopy (MIR) coupled with random forest (RF) for the quantitative analysis of soil pH

Abstract: Soil pH is one of the important properties of soil. The quickly and accurately determination of the pH of soil is key to realizing precision agriculture and understanding soil characteristics...

Cited by 11 publications

References 32 publications

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning

Multi-element Quantitative Analysis of Single Micro-sized Suspended Particles in Air with High Accuracy Based on Random Forest and Variable Selection Strategies

Discrimination of Radix Astragali according to geographical regions by data fusion of laser induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) combined with random forest (RF)

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