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Poor spectral stability seriously hinders the wide application of laser-induced breakdown spectroscopy (LIBS), so how to improve its stability is the focus, hotspot, and difficulty of current research. In this study, to achieve high precision quantitative analysis under complex detection conditions, utilizing the fusion of multi-dimensional plasma information and the integration of physical models and algorithmic models, a spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning (SBESC-PISF) was proposed. In this method, based on the statistical properties of LIBS spectra, the actual obtained spectra were decomposed into three parts: the ideal spectral intensity related only to the element concentration, and the spectral bias and spectral error caused by the fluctuation of complex high-dimensional plasma parameters. Further, the deep learning methods were used to fully excavate all the effective features in the plasma images and spectra to invert the complex high-dimensional plasma parameters according to the physical models. Finally, the estimation models of spectral bias and spectral error were established based on these features, to realize the high-precision correction of spectral intensity. To verify the feasibility of SBESC-PISF, the spectra of aluminum alloy samples obtained under three complex detection conditions were used for analysis. Under the experimental condition of laser energy fluctuation, after correction by SBESC-PISF, R2 of the three calibration curves was all increased to 0.999, RMSE and STD of the validation set (RMSEV, STDV) were reduced by 55.246 % and 50.167 %, respectively. Under the experimental condition of defocusing amount fluctuation, R2 was also all increased to 0.999, RMSEV and STDV were decreased by 58.201 % and 51.006 %, respectively. When the laser energy and defocusing amount fluctuate simultaneously, R2 was increased to 0.999, 0.996 and 0.988, RMSEV and STDV were reduced by 58.776 % and 54.397 %, respectively. These experimental results demonstrate that the spectral fluctuation correction of SBESC-PISF under complex detection conditions is effective and has wide applicability.

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Oral cancer is the most common malignancy in many developing countries, such as India, due to increased consumption of smokeless tobacco. The trace elemental components in commercially packaged forms of tobacco can play a significant role in the pathogenesis of oral cancer. To qualitatively assess the trace elements in various types of commercially packaged forms of tobacco using laser-induced breakdown spectroscopy (LIBS). Two popular varieties of 'Paan masala' that contained a mixture of slaked lime with areca nut, catechu, and other flavouring agents (tobacco was absent) and four types of packaged tobacco were obtained from 'Paan' shops. The contents in the packets were made into pellets using a hydraulic press and subjected to elemental analysis using LIBS. A ten-trial experiment was carried out on all six pellets. The National Institute of Standards and Technology (NIST) database was used to assess the emission lines. The elements obtained from commercially packaged tobacco and Paan masala were similar: calcium (Ca), iron (Fe), aluminium (Al), nickel (Ni), and chromium (Cr). Substances that cause DNA damage and carcinogenesis are inorganic elements such as nickel. Our study revealed that carcinogens such as nickel are present in the commercially packaged forms of tobacco and 'Paan masala' samples.

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BACKGROUND: Laser-induced breakdown spectroscopy (LIBS) is a versatile analytical technique for element determination in solids, liquids, and gases. However, LIBS suffers from low detection sensitivity and high relative standard deviation (RSD), restricting its large-scale applications. the process of a physical sampling can, in some cases, compromise the mechanical strength of the component under examination. It should be considered that too large laser energy is bound to cause damage to samples which cannot be tolerated in the process of safe production in the nuclear industry. It is necessary to find a method to obtain high elemental signal intensity in low energy laser. RESULTS: Here, we present a novel approach by integrating microwave plasma torch (MPT) with LIBS, referred to as MPT-LIBS, which effectively addresses the limitations associated with traditional LIBS. The MPT-LIBS technique is evaluated using Cu samples with a low laser pulse energy of 0.55 mJ. A remarkable enhancement factor of over 70 for Cu I 521.82 nm line is demonstrated, while that of Cu I 324.75 nm and 327.40 nm lines exceeding two orders of magnitude. Furthermore, the RSDs of all Cu spectral lines are reduced, especially for Cu I 521.82 nm, which is decreased from 11.48 % to 1.36 %. This indicates a significant improvement in signal stability. Characterization of the tested samples using con-focal microscopy reveals that the ablation area of MPT-LIBS is only 1.36 times of that of LIBS. The limit of detection of Cu I 324.75 nm line is reduced from 52.8 ppk to 319 ppm. SIGNIFICANCE AND NOVELTY: This study not only offers valuable guidance for improving signal stability and the limit of detection in LIBS, but also demonstrates minimal sample damage due to its low ablation amount. Consequently, the proposed methodology has the potential to significantly advance LIBS technology, expanding its applicability in industrial applications.

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We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m. A compact non-gated Czerny-Turner charge-coupled device (CCD) spectrometer (CT-CCD) is utilized to analyze the optical response. The single lens and CT-CCD combination not only reduces the cost of the detection system by tenfold, but also decreases the collection system size and weight compared to heavy telescopic-based intensified CCD systems. All the samples investigated in this study were collected from a local recycling plant. All the measurements were performed with only a single laser shot which enables rapid identification while probing a large number of samples in real time. Furthermore, principal component analysis has shown excellent separation among the samples and an artificial neural network analysis has revealed that plastic waste can be identified within ∼10 ms only (testing time) with accuracies up to ∼99%. Finally, these results have the potential to build a compact and low-cost ST-LIBS detection system for the rapid identification of plastic waste for real-time waste management applications.

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Detection of the elemental and molecular structural distribution with high resolution and miniaturization of unknown minerals is a main bottleneck in deep space exploration and geology analysis. The aim is to enhance the accuracy of the chemical analysis of micro-samples by combining the distribution information from Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS). The existing Raman-LIBS imaging methods are difficult to balance the imaging performance and system volume. There is an urgent need to develop a Raman-LIBS imaging method with miniaturization, and high imaging performance. A miniaturized Raman-LIBS imaging instrument based on the micro-electro-mechanical (MEMS) mirror has been developed to overcome these challenges. The instrument utilizes dual 2D MEMS mirror scanning technology to shorten the optical length of the system and improve the detection efficiency of hybrid spectral signals. The optical probe measures 94 mm × 66 mm, and has an axial focusing ability of approximately 40 nm, with a lateral resolution of approximately 700 nm for Raman maps and 9.5 µm for LIBS maps. As a proof experiment, 3D high-resolution Raman-LIBS hybrid spectral distribution maps of meteorite Tisserlitine 001 were obtained. The attainment of high imaging performance and miniaturization in hybrid spectral imaging is crucial for on-site chemical analysis. The proposed instrument enables in-situ spectrum and multispectral imaging with miniaturization, high spatial resolution, and high stability. The instrument is a powerful tool for composition and structural information characterization in the fields of space exploration and geological exploration.

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Lithium compounds such as lithium hydride (LiH) and lithium hydroxide (LiOH) have a wide range of industrial applications, but are highly reactive in environments with H2O and CO2. These reactions lead to the ingrowth of secondary lithium compounds, which can alter the homogeneity and affect the application of particular lithium chemicals. This study performed an exploratory analysis of different lithium compounds using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Machine learning models are trained on the recorded spectral data to discriminate emission features that differ between LiH, LiOH, and Li2CO3 to perform high-fidelity classification. Support vector machine classifiers yield perfect prediction accuracy between the three compounds with optimal training time. Multivariate methods are then used to produce regression models quantifying the ingrowth of LiOH in LiH. Performing a mid-level data fusion of selected LIBS and Raman features with partial least-squares regression produces the superlative model with a root mean square error of 2.5 wt% and a detection limit of 6.3 wt%.

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Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring.

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OBJECTIVES: Inadequate resection margins of less than 5 mm impair local tumor control. This weak point in oncological safety is exacerbated in bone-infiltrating tumors because rapid bone analysis procedures do not exist. This study aims to assess the bony resection margin status of bone-invasive oral cancer using laser-induced breakdown spectroscopy (LIBS). MATERIALS AND METHODS: LIBS experiments were performed on natively lasered, tumor-infiltrated mandibular cross-sections from 10 patients. In total, 5,336 spectra were recorded at defined distances from the tumor border. Resection margins < 1 mm were defined as very close, from 1-5 mm as close, and > 5 mm as clear. The spectra were histologically validated. Based on the LIBS spectra, the discriminatory power of potassium (K) and soluble calcium (Ca) between bone-infiltrating tumor tissue and very close, close, and clear resection margins was determined. RESULTS: LIBS-derived electrolyte emission values of K and soluble Ca as well as histological parameters for bone neogenesis/fibrosis and lymphocyte/macrophage infiltrates differ significantly between bone-infiltrating tumor tissue spectra and healthy bone spectra from very close, close, and clear resection margins (p < 0.0001). Using LIBS, the transition from very close resection margins to bone-infiltrating tumor tissue can be determined with a sensitivity of 95.0%, and the transition from clear to close resection margins can be determined with a sensitivity of 85.3%. CONCLUSIONS: LIBS can reliably determine the boundary of bone-infiltrating tumors and might provide an orientation for determining a clear resection margin. CLINICAL RELEVANCE: LIBS could facilitate intraoperative decision-making and avoid inadequate resection margins in bone-invasive oral cancer.

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Rapid and reliable identification of mineral species is a challenging but crucial task with promising application prospects in mineralogy, metallurgy, and geology. Spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) efficiently capture the elemental composition and structural information of minerals, making them a potential tool for in situ and real-time analysis of minerals. This study introduces an integrated LIBS-RS system and the fusion of LIBS and RS spectra coupled with machine learning to classify six different types of natural mineral. In order to visualize the separability of different mineral species clearly, the spectral data were projected into low-dimensional space through t-distributed stochastic neighbor embedding (t-SNE). Additionally, the Fisher score (FS) was used to identify important variables that contribute to the data classification, and the corresponding chemical elements and molecular bonds were then interpreted. The between-minerals difference in the feature spectral intensity of LIBS and RS variables could also be observed. After the minerals spectra were pre-processed, the relationship between spectral intensity and the mineral category was modeled using machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The results show that K-ELM and PLS-DA based on the fusion LIBS-RS data achieved the highest accuracy of 98.4%. These findings demonstrate the feasibility of the integrated LIBS-RS system combined with machine learning for the fast and reliable classification of minerals.

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To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen's kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model's values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

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Excessive emissions of heavy metals not only cause environmental pollution but also pose a direct threat to human health. Therefore, rapid and accurate detection of heavy metals in the environment is of great significance. Herein, we propose a method based on laser-induced breakdown spectroscopy (LIBS) combined with filter paper modified with bovine serum albumin-protected gold nanoclusters (LIBS-FP-AuNCs) for the rapid and sensitive detection of Cr3+ and Mn2+. The filter paper modified with AuNCs was used to selectively enrich Cr3+ and Mn2+. Combined with the multi-element detection capability of LIBS, this method achieved the simultaneous rapid detection of Cr3+ and Mn2+. Both elements showed linear ranges for concentrations of 10-1000 µg L-1, with limits of detection of 7.5 and 9.0 µg L-1 for Cr3+ and Mn2+, respectively. This method was successfully applied to the determination of Cr3+ and Mn2+ in real water samples, with satisfactory recoveries ranging from 94.6% to 105.1%. This method has potential application in the analysis of heavy metal pollution.

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Coal is the primary energy source in China, widely used in energy production, industrial processes, and chemical engineering. Due to the complexity and diversity of coal quality, there is an urgent need for new technologies to achieve rapid and accurate detection and analysis of coal, aiming to improve coal resource utilization and reduce pollutant emissions. This study proposes a rapid quantitative analysis of coal using laser-induced breakdown spectroscopy combined with the random forest algorithm. Firstly, a Q-switched Nd: YAG laser at 1064 nm was employed to ablate coal samples, generating plasma, and spectral data were collected using a spectrometer. Secondly, the study explores the impact of different parameters in the preprocessing method (wavelet transform) on the predictive performance of the random forest model. It identifies elements related to coal ash content and calorific value along with their spectral information. Subsequently, to further validate the predictive performance of the model, a comparison is made with models established using support vector machine, artificial neural network, and partial least squares. Finally, under optimal parameters for spectral information preprocessing (wavelet transform with Db4 as the base function and 3 decomposition levels), a model combining wavelet transform with Random Forest is established to predict and analyze the ash content and calorific value of coal. The results demonstrate that the Wavelet Transform-Random Forest model exhibits excellent predictive performance (coal ash content: R2 = 0.9470, RMSECV = 4.8594, RMSEP = 4.8450; coal calorific value: R2 = 0.9485, RMSECV = 1.5996, RMSEP = 1.5949). Therefore, laser-induced breakdown spectroscopy combined with the random forest algorithm is an effective method for rapid and accurate detection and analysis of coal. The predicted coal composition values show high accuracy, providing insights and methods for coal composition monitoring and analysis.

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Micro- and non-destructive methods of estimating compressive strength are useful for diagnosing the degradation of reinforced structures. The velocity of waves propagating through concrete can be measured using conventional non-destructive methods; however, the propagation path of waves varies depending on the distribution of coarse aggregate, resulting in variations in velocity at different measurement points. To address this issue, a method based on laser-induced breakdown spectroscopy and multivariate analysis was developed in this study for estimating the compressive strength of concrete non-destructively, ensuring the non-influence of the coarse aggregate spatial distribution. The method is based on the correlation between the emission intensity of the spectrum and the hardness of the object to be measured. Principal component analysis and partial least squares regression (PLSR) were used to extract the mortar spectrum, which determines the compressive strength of concrete, from a mixture of aggregate and mortar spectra. The compressive strength estimated based on the proposed method was consistent with the values obtained from the compressive strength test, which indicates the possibility of using multivariable analysis to estimate the compressive strength of concrete. Furthermore, the proposed method enabled on-site measurements through a simple experimental setup and insensitivity to spectral noise offered by PLSR.

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Petroleum hydrocarbon (PHC) contamination in soils is considered one of the most serious problems currently, of which the detection and identification is a fairly significant but challenging work. Conventional methods to do such work usually need complex sample pretreatment, consume much time and fail to do the in-situ detection. This paper set out to create a novel systematic methodology to realize the goals accurately and efficiently. Based on laser-induced breakdown spectroscopy (LIBS) and self-improved machine learning methods, the innovative methodology only uses extremely simple devices to do the real-time in situ detection and identification work and even realize the quantitative analysis of pollution level accurately. In the study, clean soils mixed with petroleum were served as polluted samples, clean soils to be the blank group for comparison. Based on the elemental information from the spectra obtained by LIBS, machine learning methods were improved and helped optimized the algorithm to identify the PHC polluted soil samples for the first time. Furthermore, a novel model was designed to perform the quantitative analysis of the concentration of PHC pollution in soils, which can be applied to detect the degree of PHC contamination in soils accurately. Finally, the harmful volatile component of the PHC polluted soils was also successfully and identified despite its extremely minimal content in the air. The newly-designed methodology is novel and efficient, which has extensive application prospect in the real-time in situ detection of petroleum hydrocarbon pollution.

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Lung cancer staging is crucial for personalized treatment and improved prognosis. We propose a novel bimodal diagnostic approach that integrates LIBS and Raman technologies into a single platform, enabling comprehensive tissue elemental and molecular analysis. This strategy identifies critical staging elements and molecular marker signatures of lung tumors. LIBS detects concentration patterns of elemental lines including Mg (I), Mg (II), Ca (I), Ca (II), Fe (I), and Cu (II). Concurrently, Raman spectroscopy identifies changes in molecular content, such as phenylalanine (1033 cm-1), tyrosine (1174 cm-1), tryptophan (1207 cm-1), amide III (1267 cm-1), and proteins (1126 cm-1 and 1447 cm-1), among others. The bimodal information is fused using a decision-level Bayesian fusion model, significantly enhancing the performance of the convolutional neural network architecture in classification algorithms, with an accuracy of 99.17 %, sensitivity of 99.17 %, and specificity of 99.88 %. This study provides a powerful new tool for the accurate staging and diagnosis of lung tumors.

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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.

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Laser-induced breakdown spectroscopy (LIBS) imaging has now a well-established position in the subject of spectral imaging, leveraging multi-element detection capabilities and fast acquisition rates to support applications both at academic and technological levels. In current applications, the standard processing pipeline to explore LIBS imaging data sets revolves around identifying an element that is suspected to exist within the sample and generating maps based on its characteristic emission lines. Such an approach requires some previous expert knowledge both on the technique and on the sample side, which hinders a wider and more transparent accessibility of the LIBS imaging technique by non-specialists. To address this issue, techniques based on visual analysis or peak finding algorithms are applied on the average or maximum spectrum, and may be employed for automatically identifying relevant spectral regions. Yet, maps containing relevant information may often be discarded due to low signal-to-noise ratios or interference with other elements. In this context, this work presents an agnostic processing pipeline based on a spatial information ratio metric that is computed in the Fourier space for each wavelength and that allows for the identification of relevant spectral ranges in LIBS. The results suggest a more robust and streamlined approach to feature extraction in LIBS imaging compared with traditional inspection of the spectra, which can introduce novel opportunities not only for spectral data analysis but also in the field of data compression.

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In this work, we present studies on the effect of laser wavelengths on the laser-induced plasma characterization using a femtosecond (fs) ytterbium-doped potassium-gadolinium tungstate (Yb:KGW) laser. Plasma plumes of copper, steel, ceramics, and glass samples were induced using a multiple shot of 200 fs laser pulses with 1030 nm and 343 nm wavelengths at fixed laser fluence (10.5J/cm2) and analyzed using the laser-induced breakdown spectroscopy (LIBS) technique. Time-resolved fs-LIBS measurements were performed on the same set of samples and under the same experimental conditions. For the calculation of plasma parameters, the set of spectral lines of Cu(I) (for copper sample), Fe(I) (for steel sample), and Ca(I), K(I) (for glass and ceramics samples) were observed. The plasma electron temperature and density were evaluated from the Boltzmann plots and Stark-broadening profiles of the plasma spectral lines, assuming the local thermodynamic equilibrium condition. Time-resolved plasma temperature and electron density for fs-LIBS using ultraviolet (UV) and infrared (IR) laser wavelengths were analyzed and no significant dependence on fs laser wavelength was observed for any of the samples. However, for all samples the signal-to-noise ratio (SNR) significantly increased using UV laser radiation: copper (∼100%), steel (∼300%), glass (∼400%), and ceramics (∼40%). Therefore, by using a fs UV laser wavelength for laser-induced breakdown spectroscopy experiments, for certain materials the SNR and at the same time the limit of detection can be significantly enhanced.

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In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566 % and 0.1024 %. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

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The sensitive and stable detection of trace heavy metals in liquid is crucial given its profound impact on various aspects of human life. Currently, nanoparticle-enhanced laser-induced breakdown spectroscopy (NELIBS) with dried droplet method (DDM) is widely applied for heavy metals detection. Nevertheless, the coffee ring effect (CRE) in DDM affects the stability, accuracy, and sensitivity of NELIBS. Here, we developed a slippery surface-aggregated substrate (SS substrate) to suppress the CRE and enrich analytes, and form a plasmonic platform for NELIBS detection. The SS substrate was prepared by infiltrating perfluorinated lubricant into the pores of PTFE membrane. The droplet, with targeted elements and gold nanoparticles, was dried on the SS substate to form the plasmonic platform for NELIBS analysis. Then, trace heavy metal elements copper (Cu) and manganese (Mn) were analyzed by NELIBS. The results of Cu (RSD = 5.60%, LoD = 3.72 µg/L) and Mn (RSD = 7.42%, LoD = 6.37 µg/L), illustrated the CRE suppression and analytes enrichment by the SS substrate. The results verified the realization of stable, accurate and sensitive NELIBS detection. And the LoDs succeeded to reach the standard limit of China (GB/T 14848-2017). Furthermore, the results for groundwater detection (relative error: 5.92% (Cu) and 4.74% (Mn)), comparing NELIBS and inductively coupled plasma mass spectrometry (ICP-MS), validated the feasibility of the SS substrate in practical applications. In summary, the SS substrate exhibits immense potential for practical application such as water quality detection and supervision.