Applications of laser-induced breakdown spectroscopy (LIBS) combined with machine learning in geochemical and environmental resources exploration

Transformer-based deep learning models for quantification of La, Ce, and Nd in rare earth ores using laser-induced breakdown spectroscopy

Rare earth elements like lanthanum (La), cerium (Ce), and neodymium (Nd) are vital for high-tech industries, and their real-time quantitative analysis is crucial for refining rare earth ores. Laser induced breakdown spectroscopy (LIBS) is an effective tool for such analysis but faces challenges due to the matrix effects and spectral overlaps. This paper proposes a LIBS quantitative analysis model based on the iTransformer-Bidirectional long short-term memory (iTBi) deep learning algorithm, and further integrates the iTBi model with the random forest (RF) algorithm to form the iTBi-RF-LIBS ensemble model. These methods uses 35 samples, with concentration ranges for La, Ce, and Nd from 0wt% to 1.924wt%, 0wt% to 2.917wt%, and 0wt% to 1.492wt%, respectively. Compared to univariate analysis, BiLSTM, RF, and backpropagation neural network models, the iTBi-LIBS and iTBi-RF-LIBS models show significant advantages. The iTBi-LIBS model achieves calibration coefficients (R2) of 0.989, 0.983, and 0.994 for La, Ce, and Nd, respectively, with mean absolute prediction errors (MAEP) of 0.075wt%, 0.167wt%, and 0.067wt%, and root mean square prediction errors (RMSEP) of 0.095wt%, 0.225wt%, and 0.083wt%, respectively. The R2 values for the iTBi-RF-LIBS model are improved to 0.993, 0.992, and 0.996 for La, Ce, and Nd, respectively, with MAEP reduced to 0.059wt%, 0.135wt%, and 0.053wt%, and RMSEP reduced to 0.075wt%, 0.184wt%, and 0.069wt%, respectively. These results indicate that both the iTBi-LIBS model and the iTBi-RF-LIBS ensemble model effectively reduce matrix effects and spectral overlap interferences, providing a feasible technical pathway for the precise determination of La, Ce, and Nd element concentrations in rare earth ores.

Detection of heavy metals in soil using Au@SiO2 nanoparticles and surface microstructure combined with laser-induced breakdown spectroscopy

The detection of heavy metals in soil is of great scientific significance for food security and human health. However, traditional detection methods are complicated, time-consuming, and labor-intensive. Herein, we developed a novel method using Au@SiO2 nanoparticles (NPs) and surface microstructure combined with laser-induced breakdown spectroscopy (Au@SiO2 NPs-SMS-LIBS) for the rapid detection of lead (Pb), chromium (Cr), and copper (Cu) in soil samples. The surface microstructures and Au@SiO2 NPs were prepared to improve detection sensitivity and stability. The limits of detection (LODs) for Pb, Cr, and Cu were 0.36 mg/kg, 0.32 mg/kg, and 0.28 mg/kg, respectively, with relative standard deviations (RSDs) of 5.47-6.72 %. The mechanisms of spectral performance enhancement of LIBS detection were thoroughly investigated. Furthermore, the stacking combination model was developed to improve quantitative accuracy, with the correlation of the prediction set (Rp2) for Pb, Cr, and Cu being 0.9285, 0.8625, and 0.9160, respectively. This work offers a very promising solution to improve the sensitivity, stability, and accuracy of heavy metal detection. The developed method holds great application potential for large-scale soil assessments and real-time heavy metal pollution monitoring.

Advanced recycling and identification system for discarded capacitors utilizing laser-induced breakdown spectroscopy technology

In the modern electronics industry, with the rapid development of technology and the quick turnover of electronic products, the production of electronic waste (e-waste) has also dramatically increased. Among these, discarded capacitors are a significant component of e-waste. These old capacitors not only contain harmful chemicals but are also rich in economically recoverable precious metals like Nb and Ag. This study specifically aims to enhance the classification of discarded capacitors to enable more efficient recycling and resource recovery.Traditional methods of capacitor classification mainly rely on manual identification, which is inefficient and limited in accuracy. To enhance the efficiency and accuracy of classification, this study introduces, for the first time, the combination of Laser-Induced Breakdown Spectroscopy (LIBS) technology and machine learning for the classification of capacitors. The Backpropagation Artificial Neural Network (BP-ANN) algorithms can be trained to automatically identify and classify discarded capacitors. To achieve better performance, we developed a novel algorithm called the Optimized Feature Extraction Variance Algorithm (OFEVA), which addresses the limitations of existing methods by significantly improving the accuracy of the classification model. Compared to training with principal component scores data from traditional Principal Component Analysis (PCA), training with OFEVA achieves higher classification accuracy and computational efficiency.This innovative approach not only helps increase the recycling rate of discarded capacitors and reduce environmental pollution but also provides significant technical support for the reuse of resources, thereby making an important contribution to the fields of environmental protection and resource recycling. In addition, the spectral lines of pure niobium have been calibrated for the first time in this paper, providing important data for further spectroscopic studies.

Quantitative Analysis of Multi-Elements in a Micron-Sized Single Particle Based on Laser-Induced Breakdown Spectroscopy Signal Enhancement of an Optical Fiber Collimated System

With rapid, energy-intensive, and coal-fueled economic growth, global air quality is deteriorating, and particulate matter pollution has emerged as one of the major public health problems worldwide. It is extremely urgent to achieve carbon emission reduction and air pollution prevention and control, aiming at the common problem of weak and unstable signals of characteristic elements in the application of laser-induced breakdown spectroscopy (LIBS) technology for trace element detection. In this study, the influence of the optical fiber collimation signal enhancement method on the LIBS signal was explored. Then, the influence of the LIBS signal enhancement system based on an optical fiber collimated system on LIBS spectral signal intensity and signal-to-noise ratio (SNR) was compared, and the influences of different spectral preprocessing methods and different variable selection methods on the prediction performance of the random forest (RF) calibration model were investigated. Finally, the Savitzky-Golay convolution derivative (SG)-variable importance projection (VIP)-mutual information (MI)-RF (Zn), first-order derivative (D1st)-variable importance measurement (VIM)-successive projections algorithm (SPA)-RF (Cu), and D1st-VIM-MI-RF (Ni) optimal models were constructed according to the optimal spectral preprocessing method and the optimal hybrid variable selection method. The prediction performances of their optimal RF model after SG-VIP-MI (Zn), D1st-VIM-SPA (Cu), and D1st-VIM-MI (Ni) spectral preprocessing and hybrid variable selection method are presented as follows: Zn (Rp2 = 0.9860; MREP = 0.0590), Cu (Rp2 = 0.9817; MREP = 0.0405), and Ni (Rp2 = 0.9856; MREP = 0.0875). The above results demonstrate that the RF calibration model based on the optical fiber collimated LIBS signal enhancement method, the optimal spectral preprocessing method, and variable selection strategy overcome the key problems of low SNR and low quantitative accuracy in single particle detection. It is expected to provide a theoretical basis and technical support for in situ online rapid monitoring of particulate matter.

Probing the synergistic effects of amino compounds in mitigating oxidation in 2D Ti3C2T x MXene nanosheets in aqueous environments

The shelf life of 2D MXenes in functional devices and colloidal dispersions is compromised due to oxidation in the aqueous system. Herein, a systematic investigation was carried out to explore the potential of various amino compounds as antioxidants for Ti3C2T x MXenes. A range of basic, acidic, and neutral amino acids were examined for their effectiveness, where certain antioxidants failed to protect MXenes from oxidation, while others accelerated their decomposition. Serine, threonine, and asparagine demonstrated excellent antioxidant activity, likely due to their evenly distributed molecular charges. These neutral amino acids outperformed glutamic acid, which possesses an electron-withdrawing COOH group, and lysine, with an electron-donating NH2 group. Serine-functionalized MXene realized a stabilization time constant reaching 128 days, as determined by first-order reaction kinetics. Additionally, when employed as supercapacitor electrodes, aged MXene and serine-functionalized MXene exhibited specific capacitance values of 219.2 and 280.3 F g-1 at 5 A g-1 after 6 weeks, respectively. This work addresses the gap in the practical work on ionic stabilization of 2D MXenes and has significant implications for the long-term colloidal storage of MXenes using greener chemicals.

Exploring the impact mechanism of ambient gas properties on laser-induced breakdown spectroscopy to guide the raw signal improvement

BACKGROUND

Laser-induced breakdown spectroscopy (LIBS) has long been regarded as the future superstar for chemical analysis. However, hindered by the fact that the signal source of LIBS is a spatially and temporally unstable plasma that interacts dramatically with ambient gases, LIBS has always suffered from poor signal quality, especially low signal repeatability. Although ambient gases act as one of the most direct and critical factors affecting LIBS signals, a clear understanding on how ambient gas properties impact LIBS signals is still lacking to act as guideline for the signal quality improvement.

RESULTS

In this work, the impact mechanism of three main ambient gas properties, including specific heat ratio (γ), molar mass (M), and ionization energy (E) was investigated by ignoring secondary properties, accurately proportioning gas mixtures, and experimental comparative study by applying various plasma diagnosis methods. The results indicate that these three properties impact signal repeatability by impacting the intensity of the back-pressing process within the plasma, where the sound speed determined by γ and M plays an important role. The properties impact the signal intensity by impacting three energy transfer processes in the plasma, including the laser energy absorption, the energy allocation between gas and sample species, and the heat dissipation. Based on the impact mechanism, guidelines of regulating the ambient gas properties to improvement LIBS signal were further provided.

SIGNIFICANCE

For the first time, the impact mechanism of main ambient gas properties on LIBS signals was clearly and intuitively revealed. The impact mechanism not only provided a deeper understanding of the plasma evolution process but also provided practical guidelines for improving LIBS signal quality, facilitating accurate quantification of the LIBS technique.

Assessment of groundwater chemistry to predict arsenic contamination from a canal commanded area: applications of different machine learning models

Groundwater arsenic (As), contamination is a significant issue worldwide including China and Pakistan, particularly in canal command areas. In this study, 131 groundwater samples were collected, and three machine learning models [Random Forest (RF), Logistic Regression (LR), and Artificial Neural Network (ANN)] were employed to predict As concentration. Descriptive statistics helped to conclude that all of the samples were inside the permitted limit of WHO for pH, Ca, Mg, Turbidity, Cl, K, Na, SO4, NO3, F and beyond limit of WHO for EC, HCO3, TDS, and As. RF suggested a median drop in Gini node impurity across all tree divisions. This predicted As contamination in samples due to presence of TDS, EC, HCO3- and turbidity in upper end of graph which expressed significance of these factors in contaminating water with Arsenic. Moreover, these factors were found positively correlated with Ar contamination. LR model expressed about best fitness of model. ANN classified large data set into two classes i.e. (1) Inside limit of WHO and (2) and outside limit of WHO. Total dissolved solids (TDS), turbidity, sodium (Na) and electrical conductivity (EC) were positively correlated with Ar (Arsenic concentration) in the collected samples. pH and K were negatively associated with Arsenic concentration of the observed samples. Confusion matrices and ROC-AUC scores evaluated that RF, model outperforming than LR, and ANN, in accuracy and sensitivity. Key variables influencing As concentration in the groundwater resources of the study area were identified, such parameters include TDS, chloride (Cl), bicarbonate (HCO3-) and turbidity. The study provided the complete profile of the 131 water samples which can be used to make strategies for the minimization of ground Water contamination for Rohri canal command area. Moreover, the steps can be taken to control the discussed parameters inside the WHO limit.

On-The-Flight trapping, LIBS analysis and discrimination of single meteorite particles generated by laser ablation

BACKGROUND

Thousands of micrometeorites fall to the Earth on a daily basis. Most of these meteorites have a rocky composition, but others are mainly composed of iron and nickel. Due to their small size, often ca. 100 μm in diameter, the process of searching for, collecting, and identifying these samples is remarkably tedious. In this work, we introduce a minimally invasive methodology for evaluating the full elemental composition of micrometeorites using optical emission spectroscopy of single particles produced by laser ablation of bulk targets.

RESULTS

Bulk meteorite samples were directly ablated within an ablation cell. From few micrograms of ablated matrix, we originated dry aerosols consisting of multielemental particles which were representative of the sample chemistry. SEM images confirmed that the generated particles exhibited spherical geometry. Particles were first optically trapped in air and, then, analyzed by laser-induced breakdown spectroscopy (LIBS). LIBS spectra evidenced compositional differences among samples. For example, Campo de Cielo meteorite featured a high iron content due to its metallic nature whereas LIBS results for Jbilet Winselwan and NWA 869 suggested that pyroxene components dominated the composition of the samples. In contrast, NWA 13739 and Vaca Muerta contained high aluminum intensity, thus indicating that the feldspathic component was dominant, as then verified by XRD. The intra-sample compositional variability were quite satisfactory, as revealed by the RSD data, below 45 %. For quantitative analysis, the percentages of FeO, SiO2, Al2O3, Na2O, MgO, TiO2, CaO, K2O, MnO, SrO, Cr2O3, and Li2O were calculated using CF-LIBS.

SIGNIFICANCE

This work demonstrates the applicability of laser excitation of individual particles in an optical trap for the multielemental analysis of meteorites. The methodology provides a complete overview of the samples, is capable of classificating them according to their main phases and yield preliminary quantitative information about those phases. Therefore, we present a minimally destructive pathway to be used as the first step in the inspection of these delicate samples. If the particles represent nanostructures inherent to the meteorites, it would constitute a major step in the analysis of extraterrestrial material that may provide fundamental insights into the structure and composition of the original materials at the microscale, a topic that remains an active area of research worldwide.

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique

BACKGROUND

Laser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem. It is clear that a method to enhance the inherent transferability of spectral original models is urgently needed.

RESULTS

We proposed an innovative fusion methodology, named laser-induced breakdown spectroscopy fusion laser-induced plasma acoustic spectroscopy (LIBS-LIPAS), to enhance the transferability of support vector machine (SVM) original models across LIBS systems with varying laser beams. The methodology was demonstrated using nickel-based high-temperature alloy samples. Here, the area-full width at half maximum (AFCEI) Composite Evaluation Index was proposed for extracting critical features from LIBS. Further enhancing the transferability of the model, the laser-induced plasma acoustic signal was transformed from the time domain to the frequency domain. Subsequently, the feature-level fusion method was employed to improve the classification accuracy of the transferred LIBS system to 97.8 %. A decision-level fusion approach (amalgamating LIBS, LIPAS, and feature-level fusion models) achieved an exemplary accuracy of 99 %. Finally, the adaptability of the method was demonstrated using titanium alloy samples.

SIGNIFICANCE AND NOVELTY

In this work, based on plasma radiation models, we simultaneously captured LIBS and LIPAS, and proposed the fusion of these two distinct yet origin-consistent signals, significantly enhancing the transferability of the LIBS original model. The methodology proposed holds significant potential to advance LIBS technology and broaden its applicability in analytical chemistry research and industrial applications.

Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy

Driven by the "double carbon" strategy, petroleum coke short-term demand is growing rapidly as a negative electrode material for artificial graphite. The analysis of petroleum coke physicochemical properties has always been an important part of its research, encompassing significant indicators such as ash content, volatile matter and calorific value. A strategy based on laser-induced breakdown spectroscopy (LIBS) in combination with chemometrics is proposed to realize the rapid and accurate quantification of the above properties. LIBS spectra of 46 petroleum coke samples were collected, and an original random forest (RF) calibration model was constructed by optimizing the pretreatment parameters. The RF calibration model was further optimized based on variable importance measures (VIM) and variable importance in projection (VIP) methods. After variable selection, the elemental spectral lines related to ash content, volatile matter and calorific value modeling were screened out, thus initially exploring the correlation between these properties and elements. Under the optimized spectral pretreatment method, VI threshold and model parameters, the mean relative error (MREP) of the prediction set of ash content, volatile matter and calorific value were 0.0881, 0.0527 and 0.006, the root mean square error (RMSEP) of the prediction set of ash content, volatile matter and calorific value were 0.0471%, 0.6178% and 0.2697 MJ kg-1, respectively, and the determination coefficient (RP2) of the prediction set was 0.9187, 0.9820 and 0.9510, respectively. The combination of LIBS technology and chemometric methods can provide powerful technical means for the analysis and evaluation of the physicochemical properties of petroleum coke.

Plasma parameters correction method based on plasma image-spectrum fusion for matrix effect elimination in LIBS

Matrix effect is one of the obstacles that hinders the rapid development of laser-induced breakdown spectroscopy (LIBS), and it is currently a hot, challenging, and focal point in research. To eliminate the matrix effect, this study proposed a plasma parameters correction method based on plasma image-spectrum fusion (PPC-PISF). This method corrects the total number density, plasma temperature, and electron number density variations caused by matrix effect using effective features in plasma images and spectra. To verify the feasibility of this method, experiments were conducted on pressed and metal samples, and the results were compared with those corrected by image-assisted LIBS (IA-LIBS). For the pressed samples, after correction by PPC-PISF, the R2 of the calibration curves all improved to above 0.993, the average root-mean-square error (RMSE) decreased by 41.05%, and the average relative error (ARE) decreased by 59.35% evenly in comparison to IA-LIBS. For the metal samples, after correction by PPC-PISF, the R2 of the calibration curves all increased to above 0.997. Additionally, the RMSE decreased by 29.63% evenly, the average ARE decreased by 38.74% compared to IA-LIBS. The experimental results indicate that this method is an effective method for eliminating the matrix effect, promoting the further development of LIBS in industrial detection.

Fast analysis of straw proximates based on partial least squares using near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is a rapid measurement technique based on the spectroscopic absorption bands of specific functional groups within biomass. Its main advantages include simple preparation, precise analysis, and the ability to analyze multiple components simultaneously. Fast analysis of straw proximates (moisture, ash, and fixed carbon) has been investigated by means of NIRS. A total of 144 samples were collected, the spectral data were analyzed by partial least squares (PLS) regression and support vector regression (SVR) with four wavelength selection methods. PLS combined with competitive adaptive reweighted sampling (CARS) provided excellent predictive performance for moisture, ash, and fixed carbon. For moisture prediction, the values of RP2, RMSEP and RPD were 0.7202, 0.8196, and 2.11, respectively. For ash prediction, the values of RP2, RMSEP and RPD were 0.9307, 0.5901, and 3.69, respectively. For fixed carbon prediction, the values of RP2, RMSEP and RPD were 0.8504, 0.2735, and 2.76, respectively. Fast analysis of proximates of corn stover was possible using this NIRS system.

LIBS combined with SG-SPXY spectral data pre-processing for cement raw meal composition analysis

Rapid testing of cement raw meal plays a crucial role in the cement production process, so there is an urgent need for a fast and accurate testing method. In this paper, a method based on the Savitzky-Golay (SG) smoothing and sample set partitioning based on joint x-y distance (SPXY) spectral data pre-processing is proposed to improve the accuracy of the laser-induced breakdown spectroscopy (LIBS) technique for quantitative analysis of cement raw meal components. Firstly, the spectral data is denoised by SG smoothing, which effectively reduces the noise and baseline variations in the spectra. Then, the denoised data is divided into sample sets by combining the SPXY sample division method, which improves the efficiency of data analysis. Finally, the delineated data set is modeled for quantitative analysis by a back-propagation (BP) neural network. Compared to the modeling effect of the four oxide contents of CaO, S i O 2, A l 2 O 3, and F e 2 O 3 in the Hold-Out method, the correlation coefficient (R) was improved by 26%, 10%, 17%, and 4%, respectively. The root mean square error (RMSE) was reduced by 47%, 33%, 43%, and 21%, respectively. The mean absolute percentage error (MAPE) was reduced by 63%, 60%, 36%, and 51%, respectively. The results show that there is a significant improvement in the model effect, which can effectively improve the accuracy of quantitative analysis of cement raw meal composition by LIBS. This is of great significance for the real-time detection of cement raw meal composition analysis.

Extended total number density compensation for uranium determination by laser-induced breakdown spectroscopy

BACKGROUND

Variations in plasma properties among spectra and samples lead to significant signal uncertainty and matrix effects in laser-induced breakdown spectroscopy (LIBS). To address this issue, direct compensation for plasma property variations is considered highly desirable. However, reliably compensating for the total number density variation is challenging due to inaccurate spectroscopic parameters. For reliable compensation, a total number density compensation (TNDC) method was presented in our recent work, but its applicability is limited to simple samples because of its strict assumptions. In this study, we propose a new pre-processing method, namely extended TNDC (ETNDC), to reduce signal uncertainty and matrix effects in the more complex analytical task of uranium determination.

RESULTS

ETNDC reflects the total number density variation with a weighted combination of spectral lines from all major elements and incorporates temperature and electron density compensation into the weighting coefficients. The method is evaluated on yellow cake samples and combined with regression models for uranium determination. Using the typical validation set and line combination, the mean relative standard deviation (RSD) of U II 417.159 nm in validation samples decreases from 4.92% to 2.27%, and the root mean square error of prediction (RMSEP) and the mean RSD of prediction results decrease from 4.81% to 1.93% and from 1.92% to 1.56%, respectively. Furthermore, the results of 10 validation sets and 216 line combinations show that ETNDC outperforms baseline methods in terms of average performance and robustness.

SIGNIFICANCE

For the first time, ETNDC explicitly addresses the temperature and electron density variations while compensating for the total number density variation, where the inaccurate spectroscopic parameters are avoided by fitting related quantities using concentration information. The method demonstrates effective and robust improvement in signal repeatability and analytical performance in uranium determination, facilitating accurate quantification of the LIBS technique.

Laser Ablation Plasmas and Spectroscopy for Nuclear Applications

The development of measurement methodologies to detect and monitor nuclear-relevant materials remains a consistent and significant interest across the nuclear energy, nonproliferation, safeguards, and forensics communities. Optical spectroscopy of laser-produced plasmas is becoming an increasingly popular diagnostic technique to measure radiological and nuclear materials in the field without sample preparation, where current capabilities encompass the standoff, isotopically resolved and phase-identifiable (e.g., UO and UO2 ) detection of elements across the periodic table. These methods rely on the process of laser ablation (LA), where a high-powered pulsed laser is used to excite a sample (solid, liquid, or gas) into a luminous microplasma that rapidly undergoes de-excitation through the emission of electromagnetic radiation, which serves as a spectroscopic fingerprint for that sample. This review focuses on LA plasmas and spectroscopy for nuclear applications, covering topics from the wide-area environmental sampling and atmospheric sensing of radionuclides to recent implementations of multivariate machine learning methods that work to enable the real-time analysis of spectrochemical measurements with an emphasis on fundamental research and development activities over the past two decades. Background on the physical breakdown mechanisms and interactions of matter with nanosecond and ultrafast laser pulses that lead to the generation of laser-produced microplasmas is provided, followed by a description of the transient spatiotemporal plasma conditions that control the behavior of spectroscopic signatures recorded by analytical methods in atomic and molecular spectroscopy. High-temperature chemical and thermodynamic processes governing reactive LA plasmas are also examined alongside investigations into the condensation pathways of the plasma, which are believed to serve as chemical surrogates for fallout particles formed in nuclear fireballs. Laser-supported absorption waves and laser-induced shockwaves that accompany LA plasmas are also discussed, which could provide insights into atmospheric ionization phenomena from strong shocks following nuclear detonations. Furthermore, the standoff detection of trace radioactive aerosols and fission gases is reviewed in the context of monitoring atmospheric radiation plumes and off-gas streams of molten salt reactors. Finally, concluding remarks will present future outlooks on the role of LA plasma spectroscopy in the nuclear community.

Rapid quantitative analysis of rare earth elements Lu and Y in rare earth ores by laser induced breakdown spectroscopy combined with iPLS-VIP and partial least squares

Rare earth ores are complex in composition and diverse in mineral composition, requiring high technical requirements for the selection of rare earth ores. It is of great significance to explore the on-site rapid detection and analysis methods of rare earth elements in rare earth ores. Laser induced breakdown spectroscopy (LIBS) is an important tool to detect rare earth ores, which can be used for in situ analyses without complicated sample preparation. In this study, a rapid quantitative analysis method for rare earth elements Lu and Y in rare earth ores was established by LIBS combined with an iPLS-VIP hybrid variable selection strategy and partial least squares (PLS) method. First, the LIBS spectra of 25 samples were studied using laser induced breakdown spectrometry. Second, taking the spectrum processed by wavelet transform (WT) as the input variables, PLS calibration models based on interval partial least squares (iPLS), variable importance projection (VIP) and iPLS-VIP hybrid variable selection were constructed to quantitatively analyze rare earth elements Lu and Y, respectively. The results show that the WT-iPLS-VIP-PLS calibration model has better prediction performance for rare earth elements Lu and Y, and the optimal coefficient of determination (R²) of Lu and Y were 0.9897 and 0.9833, the root mean square error (RMSE) were 0.8150 μg g^-1 and 97.1047 μg g^-1, and the mean relative error (MRE) were 0.0754 and 0.0766, respectively. It shows that LIBS technology combined with the iPLS-VIP and PLS calibration model provides a new method for in situ quantitative analysis of rare earth elements in rare earth ores.

Visualization and accuracy improvement of soil classification using laser-induced breakdown spectroscopy with deep learning

Deep learning method is applied to spectral detection due to the advantage of not needing feature engineering. In this work, the deep neural network (DNN) model is designed to perform data mining on the laser-induced breakdown spectroscopy (LIBS) spectra of the ore. The potential of heat diffusion for an affinity-based transition embedding model is first used to perform nonlinear mapping of fully connected layer data in the DNN model. Compared with traditional methods, the DNN model has the highest recognition accuracy rate (75.92%). A training set update method based on DNN output is proposed, and the final model has a recognition accuracy of 85.54%. The method of training set update proposed in this work can not only obtain the sample labels quickly but also improve the accuracy of deep learning models. The results demonstrate that LIBS combined with the DNN model is a valuable tool for ore classification at a high accuracy rate.

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

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 (R²_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.

Detection of heavy metal ions using laser-induced breakdown spectroscopy combined with filter paper modified with PtAg bimetallic nanoparticles

The rapid and sensitive detection of heavy metal ions is important for environment and human health. Hence, the rapid and sensitive detection of multiple heavy metals simultaneously has become a critical issue. Here, we propose a method based on laser-induced breakdown spectroscopy (LIBS) combined with filter paper modified with PtAg bimetallic nanoparticles (BNPs) (LIBS-FP-PtAgBNPs) for the ultrasensitive detection of Hg²⁺, Cr³⁺, and Pb²⁺. The PtAgBNPs-modified filter paper was used to efficiently and specifically adsorb Hg, Cr, and Pb, and LIBS was used to detect the Hg, Cr, and Pb simultaneously. The limits of detection for Hg, Cr, and Pb were 0.5 μg/L (2.5 nM), 8 μg/L (0.15 μM), and 2 μg/L (9 nM), respectively. Furthermore, this method was successfully applied to determine the concentrations of Hg, Cr, and Pb in real spiked water samples. Compared with other methods based on nanoparticle sensing, LIBS-FP-PtAgBNPs is simpler to use and can achieve highly efficient enrichment, rapid separation, and sensitive detection of heavy metal ions. The optimal detections of Hg, Cr, and Pb were achieved in the pH range of 1-6. The developed method provides a new avenue to realize the rapid and sensitive detection of trace heavy metals in the environment.

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

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 (R²) 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_p² = 0.9662 for Zn, R_p² = 0.9596 for Cu, and R_p² = 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.

Spectroscopic techniques for authentication of animal origin foods

Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.

Rapid Determination and Quantification of Nutritional and Poisonous Metals in Vastly Consumed Ayurvedic Herbal Medicine (Rejuvenator Shilajit) by Humans Using Three Advanced Analytical Techniques

‏Shilajit is used commonly as Ayurvedic medicine worldwide which is Rasayana herbo-mineral substance and consumed to restore the energetic balance and to prevent diseases like cognitive disorders and Alzheimer. Locally, Shilajit is applied for patients diagnosed with bone fractures. For safety of the patients, the elemental analysis of Shilajit is imperative to evaluate its nutritional quality as well as contamination from heavy metals. The elemental composition of Shilajit was conducted using three advanced analytical techniques (LIBS, ICP, and EDX). For the comparative studies, the two Shilajit kinds mostly sold globally produced in India and Pakistan were collected. Our main focus is to highlight nutritional eminence and contamination of heavy metals to hinge on Shilajit therapeutic potential. In this work, laser-induced breakdown spectroscopy (LIBS) was applied for qualitative and quantitative analysis of the Shilajit. Our LIBS analysis revealed that Shilajit samples composed of several elements like Ca, S, K, Mg, Al, Na, Sr, Fe, P, Si, Mn, Ba, Zn, Ni, B, Cr, Co, Pb, Cu, As, Hg, Se, and Ti. Indian and Pakistani Shilajits were highly enriched with Ca, S, and K nutrients and contained Al, Sr, Mn, Ba, Zn, Ni, B, Cr, Pb, As, and Hg toxins in amounts that exceeded the standard permissible limit. Even though the content of most elements was comparable among both Shilajits, nutrients, and toxins, in general, were accentuated more in Indian Shilajit with the sole detection of Hg and Ti. The elemental quantification was done using self-developed calibration-free laser-induced breakdown spectroscopy (CF-LIBS) method, and LIBS results are in well agreement with the concentrations determined by standard ICP-OES/MS method. To verify our results by LIBS and ICP-OES/MS techniques, EDX spectroscopy was also conducted which confirmed the presence above mentioned elements. This work is highly significant for creating awareness among people suffering due to overdose of this product and save many human lives.

Application of Laser-Induced Breakdown Spectroscopy and Chemometrics for the Quality Evaluation of Foods with Medicinal Properties: A Review

Laser-induced Breakdown Spectroscopy (LIBS) is becoming an increasingly popular analytical technique for characterizing and identifying various products; its multi-element analysis, fast response, remote sensing, and sample preparation is minimal or nonexistent, and low running costs can significantly accelerate the analysis of foods with medicinal properties (FMPs). A comprehensive overview of recent advances in LIBS is presented, along with its future trends, viewpoints, and challenges. Besides reviewing its applications in both FMPs, it is intended to provide a concise description of the use of LIBS and chemometrics for the detection of FMPs, rather than a detailed description of the fundamentals of the technique, which others have already discussed. Finally, LIBS, like conventional approaches, has some limitations. However, it is a promising technique that may be employed as a routine analysis technique for FMPs when utilized effectively.

Metronidazole-loaded gold nanoparticles in natural rubber latex as a potential wound dressing

Gold nanoparticles (AuNPs) have shown interesting properties and specific biofunctions, providing benefits and new opportunities for controlled release systems. In this research, we demonstrated the use of natural rubber latex (NRL) from Hevea brasiliensis as a carrier of AuNPs and the antibiotic metronidazole (MET). We prepared AuNP-MET-NRL and characterized by physicochemical, biological and in vitro release assays. The effect of AuNPs on MET release was evaluated using UV-Vis and Laser-Induced Breakdown Spectroscopy (LIBS) techniques. AuNPs synthesized by Turkevich and Frens method resulted in a spherical shape with diameters of 34.8 ± 5.5 nm. We verified that there was no emergence or disappearance of new vibrational bands. Qualitatively and quantitatively, we showed that the MET crystals dispersed throughout the NRL. The Young's modulus and elongation values at dressing rupture were in the range appropriate for human skin application. 64.70% of the AuNP-MET complex was released within 100 h, exhibiting a second-order exponential release profile. The LIBS technique allowed monitoring of the AuNP release, indicating the Au emission peak reduction at 267.57 nm over time. Moreover, the dressing displayed an excellent hemocompatibility and fibroblast cell viability. These results demonstrated that the AuNP-MET-NRL wound dressing is a promising approach for dermal applications.

A method for improving the accuracy of calibration-free laser-induced breakdown spectroscopy by exploiting self-absorption

The existence of the self-absorption effect results in a nonlinear relationship between spectral intensity and elemental concentration, which dramatically affect the quantitative accuracy of laser-induced breakdown spectroscopy (LIBS), especially calibration-free LIBS (CF-LIBS). In this work, the CF-LIBS with columnar density and standard reference line (CF-LIBS with CD-SRL) was proposed to improve the quantitative accuracy of CF-LIBS analysis by exploiting self-absorption. Our method allows using self-absorbed lines to perform the calibration-free approach directly and does not require self-absorption correction algorithms. To verify this method, the experiment was conducted both on aluminium-bronze and aluminium alloy samples. Compared with classical CF-LIBS, the average errors (AEs) of CF-LIBS with CD-SRL were decreased from 3.20%, 3.22%, 3.15% and 3.01%-0.95%, 1.00%, 1.16% and 1.78%, respectively for four aluminium-bronze alloy samples. The AEs were decreased from 0.66%, 0.70%, 0.89% and 1.30%-0.43%, 0.61%, 0.77% and 0.33%, respectively for four aluminium alloy samples. The experimental results demonstrated that CF-LIBS with CD-SRL provided higher quantitative accuracy and stronger adaptability than classical CF-LIBS, which is quite helpful for the practical application of CF-LIBS.

Low-cost smartphone-based LIBS combined with deep learning image processing for accurate lithology recognition

A low-cost and multi-channel smartphone-based spectrometer was developed for LIBS. As the CMOS detector is two-dimensional, simultaneous multichannel detection was achieved by coupling a linear array of fibres for light collection. Thus, besides the atomic information, the spectral images containing the propagation and spatial distribution characters of a laser induced plasma plume could be recorded. With these additional features, accurate rock type prediction was achieved by processing the raw data directly through a deep learning model.

Pollution risk estimation of the Cu element in atmospheric sedimentation samples by laser induced breakdown spectroscopy (LIBS) combined with random forest (RF)

Laser-induced breakdown spectroscopy (LIBS) combined with the random forest (RF) algorithm was proposed to predict three pollution indexes (geo-accumulation index, enrichment factor, and potential ecological risk index) of the Cu element in atmospheric sedimentation samples to evaluate the pollution risk. To begin with, the LIBS spectra of 15 atmospheric sedimentation samples from different locations were collected and the copper element was identified using the National Institute of Standards and Technology (NIST) database. Then, the influence of different spectral pretreatment methods (MSC, WT and D_1st) on the predictive performance of the RF was discussed according to the calibration set with the determination coefficient (R_c²) and mean relative error (MREC) as evaluation indexes. Next, in order to obtain a better RF calibration model, a variable importance (VI) measurement was applied to select input variables from LIBS spectral data based on the optimal spectral pretreatment method, and the optimal variable importance threshold was selected as the input variable to establish the RF calibration model. Finally, the predictive performance of the optimal RF calibration model was verified using the prediction set with the determination coefficient (R_p²) and the mean relative error (MREP). The results show that R_p² of the geo-accumulation index, enrichment factor and potential ecological risk index is up to 0.9971, 0.9919 and 0.9290, respectively, and MREP of the three indexes is 0.0234, 0.1173 and 0.0810, respectively; the average relative standard deviation (RSD) of the prediction set for the three indexes is 2.16%, 5.78% and 0.71%, respectively. Furthermore, it can be inferred that Cu was at levels corresponding to serious pollution primarily because of anthropogenic activities based on the predictive I_geo, E_r and RI values. Therefore, LIBS combined with the RF algorithm is a promising means which can achieve fast and simple estimation of the pollution risk degree of Cu in atmospheric sedimentation samples without complicated sample preparation to provide a basis for pollution prevention and control measures.