Laser-induced Breakdown Spectroscopy Technology Research Articles

Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna.

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.

Reassociation of Skeletal Remains Using Laser-Induced Breakdown Spectroscopy.

A major challenge in forensic anthropology and bioarcheology is the development of fast and effective methods for sorting commingled remains. This study assesses how portable laser-induced breakdown spectroscopy (LIBS) can be used to group skeletal remains based on their elemental profiles. LIBS spectra were acquired from the remains of 45 modern skeletons, with a total data set of 8388 profiles from 1284 bones. Spectral feature selection was conducted to reduce the spectral profiles to the peaks exhibiting the highest variation among individuals. Emission lines corresponding to 9 elements (Ca, P, C, K, Mg, Na, Al, Ba, and Sr) were found important for classification. Linear discriminant analysis (LDA) was concurrently used to classify each spectral profile. From the 45 individuals, each LIBS spectrum was successfully sorted to its corresponding skeleton with an average accuracy of 87%. These findings indicate that variation exists among the LIBS profiles of individuals' skeletal remains, highlighting the potential for portable LIBS technology to aid in the sorting of commingled remains.

Improved convolutional neural network-assisted laser-induced breakdown spectroscopy for identification of soil contamination types

Identification of soil contamination types is of great scientific significance for soil remediation and environmental pollution control. However, traditional identification methods for soil determination are time-consuming, laborious, and complicated. Here, we proposed an accurate method for identifying soil contamination types based on laser-induced breakdown spectroscopy (LIBS) and an improved convolutional neural network (CNN) model. The spectral feature extraction-based multiple attention residual network (SFEMARNet) model was constructed to extract detailed features by spectral feature extraction (SFE) modules, and highlight useful features by multiple attention residual (MAR) modules in LIBS spectral data. In addition, deep learning models and machine learning models were used to identify the data. The results showed that the SFEMARNet model achieved an accuracy of 98.75% on the test set. The recall, precision, and F1-score of the models reached 98.78%, 98.75%, and 98.76%, respectively, which were significantly better than the three deep learning models and of four machine learning models. It seems that the SFEMARNet model combined with LIBS technology may be a potential method for the accurate identification of soil contamination types.

A rapid in-situ hardness detection method for steel rails based on LIBS and machine learning

The railway, as a public transportation system, has played a significant role in global economic development. However, the pursuit of high speed and heavy load in trains has brought significant bending and shear stresses on the rails, making the performance of steel rails a notable focal point. The properties of steel rails are crucial factors determining the operational safety of high-speed trains, while the surface hardness is regarded as a key mechanical characteristic. This study is to develop a new rapid in-situ method to measure the hardness of steel rails, by employing Laser-Induced Breakdown Spectroscopy (LIBS) and specified analysis technology. Three distinct methods, including spectral line intensity ratios, plasma excitation temperature and machine learning, have been compared and analysed. Particularly, a multivariate model is established and optimized using the machine learning methods for U71Mn steel rail hardness analysis. In the machine learning algorithms, variance normalization is utilized, resulted in a significant improvement for the information retention during the data dimensionality reduction process. Subsequently, twelve algorithm combinations were explored, revealing that the Particle Swarm Optimization employed in Support Vector Regression (PSO-SVR) yielded the lowest mean squared error (MSE). Further refinement of the PSO-SVR was achieved through the incorporation of adaptive stochastic weights, resulting in an elevated coefficient of determination (R2) to 0.9876. Finally, the performance of the model on the new five samples was validated with an R2 of 0.9864. Otherwise, its potential applicability may be extended to broader domains, providing robust support for enhancing the precision and reliability of LIBS technology in surface hardness quantitative analysis.

Controlling capillary flow by using laser-fabricated acrylic substrates with rectangular grooves for the improvement of repeatability of laser induced breakdown spectroscopy liquid analysis

To enhance the consistency of Laser-Induced Breakdown Spectroscopy (LIBS) in detecting dried liquid droplets, a rectangular groove substrate prepared by laser etching of a polymethyl methacrylate (PMMA) acrylic board is proposed in this work to suppress the Coffee Ring Effect (CRE). Located at the corner of the substrate, the right-angled heat source structure induces a significant inward thermal capillary flow within the groove, thus inhibiting solute capillary flow. At the later stages of heating, the edge deposition that occurs during evaporation is suppressed by the sawtooth-like microscopic grooves, along with the capillary effects in associated with the liquid. The rationale of CRE suppression by the rectangular groove substrate is theoredically analyzed based on the coupled thermal-solute capillary flow theory. With LIBS used to scan the samples within rectangular and circular groove substrates, it is revealed that the relative standard deviation (RSD) of spectral intensities for Cd I 259.28 nm and Cu I 324.27 nm in the rectangular groove substrate with a depth of 0.1 mm (RSD = 4.2%, RSD = 2.1%) is less significant than in the circular groove substrate (RSD = 41.2%, RSD = 39.3%). On average, the RSD is 7.6% and 7.3%, respectively, which indicates the effect of the rectangular groove substrate on CRE suppression. On this basis, calibration curves are drawn for Cd and Cu to indicate the good fitting results for the rectangular groove substrate, with the determination coefficient (R2) reaching 0.997 and 0.996 for Cd and Cu, respectively. The limit of detection (LOD) is 0.48 μg/mL and 0.35 μg/mL, respectively. The cross-validation root mean square error (RMSECV) is 2.02 μg/mL and 1.36 μg/mL, respectively. These results demonstrate that the rectangular groove substrate is effective in suppressing the CRE, and in enhancing the sensitivity of detection and the accuracy of heavy metal prediction, which provides guidance for platform selection in the pre-processing stage of LIBS technology.

Rapid and accurate identification of bacteria utilizing laser-induced breakdown spectroscopy.

Timely and accurate identification of harmful bacterial species in the environment is paramount for preventing the spread of diseases and ensuring food safety. In this study, laser-induced breakdown spectroscopy technology was utilized, combined with four machine learning methods - KNN, PCA-KNN, RF, and SVM, to conduct classification and identification research on 7 different types of bacteria, adhering to various substrate materials. The experimental results showed that despite the nearly identical elemental composition of these bacteria, differences in the intensity of elemental spectral lines provide crucial information for identification of bacteria. Under conditions of high-purity aluminum substrate, the identification rates of the four modeling methods reached 74.91%, 84.05%, 85.36%, and 96.07%, respectively. In contrast, under graphite substrate conditions, the corresponding identification rates reached 96.87%, 98.11%, 98.93%, and 100%. Graphite is found to be more suitable as a substrate material for bacterial classification, attributed to the fact that more characteristic spectral lines are excited in bacteria under graphite substrate conditions. Additionally, the emission spectral lines of graphite itself are relatively scarce, resulting in less interference with other elemental spectral lines of bacteria. Meanwhile, SVM exhibited the highest precision rate and recall rate, reaching up to 1, making it the most effective classification method in this experiment. This study provides a valuable approach for the rapid and accurate identification of bacterial species based on LIBS, as well as substrate selection, enhancing efficient microbial identification capabilities in fields related to social security and military 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.

Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy.

Precisely distinguishing between malignant and benign lung tumors is pivotal for suggesting therapeutic strategies and enhancing prognosis, yet this differentiation remains a daunting task. The growth rates, metastatic potentials, and prognoses of benign and malignant tumors differ significantly. Developing specialized treatment protocols tailored to various tumor types is essential for enhancing patient survival outcomes. Employing laser-induced breakdown spectroscopy (LIBS) in conjunction with a deep learning methodology, we attained a high-precision differential diagnosis of malignant and benign lung tumors. First, LIBS spectra of malignant tumors, benign tumors, and normal tissues were collected. The spectra were preprocessed and Z score normalized. Then, the intensities of the Mg II 279.6, Mg I 285.2, Ca II 393.4, Cu II 518.3, and Na I 589.6 nm lines were analyzed in the spectra of the three tissues. The analytical results show that the elemental lines have different contents in the three tissues and can be used as a basis for distinguishing between the three tissues. Finally, the RF-1D ResNet model was constructed by combining the feature importance assessment method of random forest (RF) and one-dimensional residual network (1D ResNet). The classification accuracy, precision, sensitivity, and specificity of the RF-1D ResNet model were 91.1%, 91.6%, 91.3%, and 91.3%, respectively. And the model demonstrates superior performance with an area under the curve (AUC) value of 0.99. The above results show that combining LIBS with deep learning is an effective way to diagnose malignant and benign tumors.

Multidimensional characterization of Ni-Zn ferrite films based on laser-induced breakdown spectroscopy technology

Existing experimental results have suggested that doping different elements will significantly affect the properties of the thin film produced using the target material. In this study, the properties of the film were studied under different powers and different pressures through the magnetron sputtering method of preparation of three elements containing nickel, zinc, and iron thin film. First, the film is subjected to laser-induced breakdown spectroscopy (LIBS) detection, and the obtained spectral data are processed to obtain the energy spectrum and two-dimensional (2D) distribution of the elements. Next, the film thickness was examined under a scanning electron microscope (SEM) to verify the feasibility of LIBS technology in film thickness measurement. Another set of experiments was performed to estimate the film thickness based on the obtained data to determine the accuracy of film thickness measurement using LIBS technology. Subsequently, the transmittal rate and energy-dispersive X-ray spectroscopy (EDS) of the thin film that was obtained by the sputtering of Nickel-zinc ferrite film were examined. The optical band gap of the thin film was determined following the transmittal rate of the thin film. Besides, the variation trend of the optical band gap was plotted and then compared with the LIBS spectral data. The statistical data of the element proportion were obtained through EDS, and the calibration curve was generated in combination with the LIBS spectral data. As revealed by the above-mentioned results, the LIBS is capable of analyzing the change of element content in the binary doped thin films.

Evolution and interpretation of LIBS signals based on laser selective layered paint removal from aircraft composite tail

The reliability and controllability of laser selective layered paint removal depend on effective online monitoring technology. In the process of laser cleaning the surface paint layer of the aircraft composite tail, this paper studied the evolution and interpretation of laser induced breakdown spectroscopy (LIBS) signals. The laser power is adjusted to achieve different depths of the laser cleaned composite surface paint layer to compare the Pearson linear correlation between the LIBS online spectrum and the reference spectrum and decode the characteristic peak signal of the LIBS spectrum. The linear correlation of the LIBS spectra and the evolution of the characteristic peaks of the LIBS spectra are mutually verified to establish the intrinsic correlation between the controllability of layered paint removal and the variation of the LIBS spectra, which demonstrates the feasibility of the LIBS technology for online monitoring and feedback of laser paint removal. The results show that the Pearson correlation coefficient r was 0.87–0.89 for the laser cleaning depth range of 44.79–51.50 μm, the layered boundary of laser cleaning the topcoat without damaging the primer is reached, it is proved that the Pearson correlation coefficient r can determine the laser paint removal process. This research compared the different LIBS spectra with cleaning depths at 44.79 and 51.50 μm. It was found that the characteristic peak of strontium element Sr I at wavelength 460.66 nm could characterize the delamination boundary of laser paint removal from aircraft composite tail.

Identification and classification of recyclable waste using laser-induced breakdown spectroscopy technology

The management and disposal of waste is a severe social issue and an essential part of ecological sustainability. As an important component of the green, low-carbon, and recycling economic system, the identification and classification of recyclable waste is the premise of its reuse and energy conservation. The main issues at hand are to improve the classification accuracy and reliability of recyclable waste and to achieve automatic classification. The methods based on physical characteristics and image-based methods are inaccurate and unreliable. The current spectroscopy methods need to process the detected samples in advance, unsuitable for automatic detection. Based on material composition properties, the Laser-Induced Breakdown Spectroscopy (LIBS) technology is here proposed to accurately and reliably identify and classify recyclable waste into six categories at the level of consumer, such as paper, plastic, glass, metal, textile, and wood. The method is also used to subclassify the same category of waste for reuse at the level of a recycling factory. We subclassified metals into iron, stainless steel, copper, and aluminum and plastics into polyvinylchloride, polyoxymethylene, acrylonitrile-butadiene-styrene, polyamide, polyethylene, and polytetrafluoroethylene. The drop-dimension methods of LIBS spectra of waste were researched to eliminate noise and redundant information by principal component analysis (PCA) and linear discriminant analysis (LDA), respectively. Their clustering effects were analyzed to choose a suitable dimension. Combining the random forest (RF), back propagation neural network (BPNN), and convolutional neural network (CNN), we established and compared five classification models, PCA + RF, PCA + BPNN, LDA + RF, LDA + BPNN, and 1D-CNN. For the classification of six categories, the accuracies of proposed classification models are all more than 96%, and LDA(5D) + RF has 100% accuracy and optimal classification performance indices. For the subclassification of metals and plastics, PCA(8D) + RF has the highest classification accuracy of 98.77% and 99.52%, respectively.

Quantitative Detection of the Raw Ore Turquoise Based on Laser-Induced Breakdown Spectroscopy Technology.

Turquoise is one of the key ingredients in some magical Tibetan medicines, and its quality and content directly affect the medicine's effectiveness. In this paper, laser-induced breakdown spectroscopy (LIBS) technology was first applied to detect the raw materials of Tibetan medicine. The traditional data analysis methods could not meet the practical requirements of modern Tibetan medicine factories due to matrix effects. The concept of correlation coefficient (ρ) in pattern recognition technique was introduced as an evaluation index, and the model was established based on the intensities of the four characteristic Al and Cu spectral lines of the samples for different contents of turquoise, which was applied to estimate the contents of turquoise in the samples to be tested. We detected the LIBS on 126 samples of raw ore from 42 areas in China and evaluated the turquoise content using self-developed software with an error of <10%. This paper's technical testing process and methods can also be applied to test other mineral compositions and provide technical support for modernizing and standardizing Tibetan medicines.

Quantitative Analysis of Steel Alloy Elements Based on LIBS and Deep Learning of Multi-Perspective Features

The Si and Mn contents in steel alloys are important characteristic indexes that influence the plasticity and welding properties of these alloys. In this work, the quantitative analysis methods for trace elements under complex alloy matrices by laser-induced breakdown spectroscopy (LIBS) are studied, which provide a foundation for utilizing LIBS technology in the rapid online detection of steel alloy properties. To improve the quantitative analysis accuracy of LIBS, deep learning algorithm methods are introduced. Given the characteristics of LIBS spectra, we explore multi-perspective feature extraction and backward differential methods to extract the spatio-temporal characteristics of LIBS spectra. The Text Convolutional Neural Network (TextCNN) model, combined with multi-perspective feature extraction, displays good stability and lower average relative errors (6.988% for Si, 6.280% for Mn) in the test set compared to the traditional quantitative analysis method and deep neural network (DNN) model. Finally, the backward differential method is employed to optimize the two-dimensional LIBS spectral input matrix, and the results indicate that the average relative errors of Si and Mn elements in the test set decrease to 5.139% and 3.939%, respectively. The method proposed in this work establishes a theoretical basis and technical support for precise prediction and online quality monitoring.

Porous Chitosan Composite Membrane Tandem Laser-Induced Breakdown Spectroscopy for Detection of Metal Elements in Liquid Samples

Laser-induced breakdown spectroscopy (LIBS) is currently one of the most popular techniques for direct element analysis of solid samples. However, when directly used for liquid sample analysis, there are disadvantages, including sample splashing, plasma quenching, and poor signal stability. These problems can be overcome through liquid-solid matrix conversion; at the same time, LIBS signal enhancement can be realized, and the sensitivity of detection of liquid samples can be improved. For this research, the authors used chitosan (CS) as a raw material, and introduced poly(vinyl alcohol) (PVA) and polyethyleneimine (PEI) to finally synthesize a new type of porous membrane material with better stability and more functional group content. The membrane was used as a liquid-solid conversion matrix material combined with LIBS technology to successfully achieve rapid separation and detection of Cu, Ag, Pb, and Cr, and the corresponding detection limits can reach 0.038, 0.069, 0.012, and 0.009 mg/L, respectively. This method further improves the sensitivity of the LIBS method. Combining it with membrane materials will replace inactive membranes and open up a new way for the rapid analysis of solution samples using LIBS technology.

LIBS-MLIF Method: Stromatolite Phosphorite Determination

The search for biominerals is one of the core targets in the deep space exploration mission. Stromatolite phosphorite is a typical biomineral that preserves early life on Earth. The enrichment of phosphate is closely related to microorganisms and their secretions. Laser-induced breakdown spectroscopy (LIBS) has become an essential payload in deep space exploration with the ability to analyze chemical elements remotely, rapidly, and in situ. This paper aims to evaluate the rapid identification of biological and non-biological minerals through a remote LIBS payload. LIBS is used for element analysis and mineral classification determination, and molecular laser-induced fluorescence (MLIF) is used to detect halogenated element F to support the existence of fluorapatite. This paper analyzes the LIBS-MLIF spectral characteristics of stromatolites and preliminarily evaluates the feasibility of P element quantification. The results show that LIBS technology can recognize biological and non-biological signals. This discovery is significant because it is not limited to detecting and analyzing element composition. It can also realize the detection of molecular spectrum based on selective extraction of CaF molecule. Therefore, the LIBS payload still has the potential to search for biomineral under the condition of adjusting the detection strategy.

Spectral preprocessing combined with feature selection improve model robustness for plastics samples classification by LIBS

Introduction: Nowadays, the widespread use of plastic products has significantly contributed towards environmental pollution caused by waste plastics. Laser-induced breakdown spectroscopy (LIBS), an emerging spectroscopic technology, has shown great potential for rapid sorting and recycling of plastics. However, the poor robustness of the classification model severely limits the large-scale application of LIBS technology in plastic sorting and recycling.Methods: In this research, we used spectral preprocessing combined with feature selection to improve the robustness of the support vector machine (SVM) classification model for four typical plastic samples (ABS, nylon, 3240, and its modified product FR-4). LIBS spectral data were collected under different experimental conditions, then we defined robustness over time (ROT), robustness over time and different focusing lenses (ROT&amp;amp;RFL), and robustness over time and different manufacturers (ROT&amp;amp;RDM) to assess model performance. The feature importance of the preprocessed spectra was evaluated using the Relief-F algorithm, and the maximum accuracy of the validation set was 92.6% when inputting the first 19 most important features. Eventually, the optimal model was used for the prediction of the test set.Results and discussion: The ROT of the original spectrum, spectrum preprocessing, and spectral preprocessing combined with feature selection were 58.4%, 79.1%, and 98.47%, respectively. Similarly, ROT&amp;amp;RFL for the same methods were 65.54%, 75%, and 95.25%, respectively. ROT&amp;amp;RDM were 65.5%, 67%, and 93.92%, respectively. The results demonstrate that spectral preprocessing combined with feature selection can significantly improve the robustness of the classification model, and the proposed method is feasible for plastic sorting and recycling.

The online in situ detection of plastic and its combustion smoke via laser-induced breakdown spectroscopy

With the development of modern industry, it can be said that from industrial production to the basic necessities of life, plastic products can be seen everywhere. What follows is the disposal of plastic waste. How to deal with plastic waste correctly is closely related to people’s health and the earth’s environment. In this paper, laser-induced breakdown spectroscopy is applied for the online in situ detection of plastic and its combustion smoke. According to the spectrum obtained, the main components of plastics are Ca, Mg, Na, K, C, H and O. In the spectrum of smoke, the characteristic peaks of the elements C, H, O and N are observed. In addition, the online detection of heavy metal pollution is simulated. Finally, principal component analysis and error back propagation artificial neural networks were applied for the identification of different plastics and their combustion smoke, and the identification accuracy reaches 92.73% and 77.42% respectively. All the results indicate that laser-induced breakdown spectroscopy technology has great potential in the identification of similar objects and the monitoring of air pollutants.

Detection of Cadmium in Sunscreen Based on Laser-Induced Breakdown Spectroscopy Technology

Detection of Cadmium in Sunscreen Based on Laser-Induced Breakdown Spectroscopy Technology

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.

Measurement of transient temperature using laser-induced breakdown spectroscopy (LIBS) with the surface temperature effect

A method based on LIBS technology for measuring transient surface temperatures had been proposed with an ultra-high time resolution of several μs.