Laser-induced breakdown spectroscopy (LIBS), as a new type of material analysis technology, has been widely used in many fields such as factory operation and maintenance, garbage collection, rock and mineral analysis, cultural heritage protection, environmental monitoring, biomedicine, food inspection, and homeland security in recent years due to its outstanding advantages such as simple sample preparation, non-contact measurement, strong field-adaptability, fast analysis speed, as well as simultaneous identification and quantitative analysis of various elements. In the process of gradually transitioning from laboratory to practical application, researchers from all over the world have developed various LIBS instruments, which can be roughly divided into three categories according to their size and usage characteristics: desktop, remote and portable LIBS. In this paper, the principle, hardware implementation and development of LIBS are systematically introduced, the existing LIBS instruments are classified and summarized, the advantages and challenges of the various devices are discussed in detail, and the future development is prospected.

Carbon content of fly ash is one of the important parameters for evaluating the combustion efficiency of coal-fired boilers, so its online measurement is of great significance for improving the energy saving level and intelligent development of coal-fired units. As a competitive on-line measurement technique, laser-induced breakdown spectroscopy (LIBS) has made great progress in fly ash carbon content measurement. The progress of basic research on LIBS measurement of carbon content in coal combustion fly ash is summarized. According to the differences in the morphology of fly ash samples during measurement, the optimization methods of LIBS measurement parameters in different measurement modes are sorted out firstly. Then, the current development trends of spectral data processing methods and quantitative analysis algorithms for LIBS measurement of fly ash carbon content are summarized. Finally, the current application of LIBS for fly ash carbon content measurement in real industry is reviewed, and the future research direction is prospected.

Based on the demand for laser-induced breakdown spectroscopy (LIBS) technology in various fields such as coal analysis, metallurgy and water quality testing, a compact rapid detection device is developed based on the principle of laser-induced breakdown spectroscopy. The developed LIBS spectrometer has good stability and high laser energy (0-100 mJ), and can perform fully automated spectral line comparison and selection for quantitative analysis, thus facilitating rapid LIBS detection of multiple samples and elements. On the basis of parameter optimization, the device was used to detect and analyze the silicon and carbon elements contained in 7 steel samples. Experimental results show that the optimal silicon and carbon characteristic spectral data can be obtained with a delay time of 1 μs, defocus amount of 0 mm, and laser energy of 60 mJ. Under the optimal experimental conditions, quantitative analysis of the seven samples was conducted, and calibration fitting curves for silicon and carbon elements were established using the internal standard method, yielding correlation coefficients of 0.986 and 0.999, respectively. The relative standard error of the relative intensity ratio does not exceed 5%, and the predicted values are close to the actual values. The results in this work validate the excellent analytical performance of the instrument, which is of great significance for the development of multi-element detection and portable LIBS instruments.

Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy technique based on transient plasma, and imaging observation is crucial for LIBS research. An economically viable industrial color camera is employed to facilitate LIBS plasma imaging, utilizing RGB channels of the camera to segregate plasma imaging outcomes across distinct bands, and consequently achieving the dynamic observation of elemental evolution within corresponding bands. The efficacy of this approach was verified through experiment with copper-zinc alloy targets, and the results showed that the proposed method can effectively characterize the expansion of various elements (Zn, Cu, O). Furthermore, combined with bandpass filters, the method can achieve plasma imaging within narrower spectral bands. This validation underscores the feasibility of leveraging the RGB channel of color cameras for plasma imaging analysis, and the proposed method can provide a pragmatic technical avenue for fundamental LIBS research.

To further improve the quantitative analysis accuracy of one-point calibration laser-induced breakdown spectroscopy (OPC-LIBS), the OPC-LIBS based on Saha-Boltzmann plot and self-absorption correction was proposed. Ti, V and Al in two titanium alloy samples were quantified using the method, and the quantitative results were compared with those of OPC-LIBS based on Boltzmann plot, OPC-LIBS based on Saha-Boltzmann plot, and OPC-LIBS based on Boltzmann plot and self-absorption correction. The results show that the proposed method can obtain the smallest quantitative error among the four methods, with mean relative errors (MREs) of 4.11% and 3.99% for the three elements in two titanium alloy samples, respectively, indicating that the proposed method can effectively improve the quantitative analysis accuracy of OPC-LIBS.

The elemental composition and content in Martian soil are important record carrier of geological evolutionary history, which can reflect the Martian environment, climate, and other information, so it is of great significance to detect and analyze Martian soil. A LIBS quantitative analysis method based on the combination of transfer component analysis (TCA) with random forest (RF) is proposed to predict the K2O mass fraction of Mars on-orbit standards. The spectral data of 383 standard samples in simulated Martian environment were selected as the training set, and the spectral data of 6 on-orbit standard samples in real Martian environment were selected as the test set. The RF model with 250 decision trees was established using the training set, and the mean absolute error (EMA), the root mean square error (ERMS) and the mean relative error (EMR) were 1.117, 1.148 and 10.104, respectively, indicating poor prediction performance. To shorten the distribution distance between the spectral data of the training set and the test set, the TCA-RF model is established and the parameters are adjusted. Compared with the RF model, the EMAERMS and EMR of the TCA-RF model are reduced by 90.7%, 88.1% and 94.1% respectively. Compared with the reference model MOC, a model based on the partial least squares regression combined with independent component analysis, the TCA-RF model is more accurate than the MOC model in predicting samples with K2O mass fraction higher than or equal to 0.15% in the test set. Therefore, it is indicated that the TCA-RF model can provide a new technical means for detecting the content of soil elements on Mars.

The SuperCam carried by the NASA's Perseverance rover can detect the surface material composition of Mars such as Mn. In order to determine the content of Mn on Mars, a quantitative method for Mn based on ensemble learning is proposed using the laser-induced breakdown spectroscopy (LIBS) dataset of geologic standards. A series of pre-processing such as spectral denosing and de-baselining are carried out firstly, then spectral deconvolution is performed to realize peak-fitting, and finally a quantitative method for Mn content prediction is established. The quantitative accuracy for Mn of the different quantitative methods were experimentally compared. The results show that, compared with the two traditional methods (LASSO and ElasticNet), the root-mean-square error of the proposed method based on ensemble learning is reduced by 49% and 30% on average, respectively, and the quantitative results of the new method are closer to the real values of the samples. This study shows that the ensemble learning based quantitative method is more suitable for Mars Mn quantification.

The calibration-free (CF) method is a quantification approach for laser-induced breakdown spectroscopy (LIBS) that enables the content determination of detected elements without a calibration curve. However, the actual quantitative results of traditional CF-LIBS often exhibit discrepancies with respect to the real values. In order to improve the accuracy of CF-LIBS quantitative analysis results, three standard Cu-Zn alloy targets with different mass ratios were used for the calculation of CF-LIBS quantitative analysis, and accurate quantification of the Zn/Cu quality ratio in the alloys was successfully achieved through utilization of "quasi-optically thin state" and "self-absorption correction". In the implementation, we firstly approached the theoretical intensity ratio of copper lines (I521.8 nm / I515.3 nm) by adjusting laser energies and detection delays, so as to find the "quasi-optically thin state" of the plasma. And then, under such a plasma state, the spectral intensity was well corrected by the self-absorption via referencing the corresponding coefficient. Based on the proposed method, the Cu/Zn quality ratio was accurately determined using CF-LIBS, exhibiting excellent agreement with the actual content. The quantitative biases of the three standard copper-zinc alloy targets were observed all less than 3.5%, indicating high precision of measurements. It proves that the developed method is effective in improving alloys quantitation of LIBS, and more applications are expected in other fields in the future.

Rare earths are important strategic reserve resources, which are widely used in industry, agriculture, military, etc. However, high-precision detection of trace rare earth elements based on the combination of laser⁃induced breakdown spectroscopy (LIBS) technology and conventional unit calibration methods still remains challenging. A multivariate linear calibration model is established using preferred multispectral lines, replacing the conventional unit calibration methods, to improve the accuracy of LIBS detection for four rare earth trace elements (La, Gd, Y, Yb). On the basis of optimizing experimental parameters (defocus distance, acquisition delay, and laser pulse energy), a multivariate linear calibration model for the four elements was established, and the root mean square errors (RMSE) of the calibration model under one to three characteristic spectral lines are compared based on validation samples. The results show that the RMSE of La, Gd, Y, and Yb decreased from 0.00341%, 0.00727%, 0.00662%, and 0.00653% in the single linear calibration fitting to 0.00203%, 0.00707%, 0.00586%, and 0.00356% in the ternary linear calibration fitting model. This result indicates that the use of LIBS multivariate linear calibration model can significantly reduce prediction error of rare earth elements, and the detection accuracy of rare earth elements can be effectively improved.

Real-time, online, accurate, and rapid detection of eutrophic element nitrogen in water was achieved using aerosol assisted laser-induced breakdown spectroscopy (LIBS) in this work. In response to the limitation that the liquid lifting capacity of traditional concentric atomizer is affected by liquid physical characteristics, a micro-volume syringe pump is used in this work to achieve the same liquid lifting volume of samples with different salinity. As a result, the average relative standard deviation(DRSAV) of the absolute intensity of N I peak at 746.831 nm is reduced from 19.51% to 6.78%, the limit of detection (LoD) is improved from 5.45 mg/L to 1.57 mg/L, and the linear correction coefficient R2, average relative error (ER), and root mean square error of cross validation (ERMSCV) are improved from 0.2830, 45.80%, and 23.96 mg/L to 0.9043, 10.33%, and 4.28 mg/L, respectively. The results of this work indicate that the liquid aerosol assisted LIBS with the assistance of a micro injection pump for sample injection can be applied for accurate and sensitive detection of nitrogen in water.

In response to the shortcomings of the current methods for detecting heavy metal elements in liquid, the laser-induced breakdown spectroscopy-glow discharge (LIBS-GD) combined method is introduced for detecting Cu element in the Yellow River water. By measuring the Cu mass concentration of different standard solutions, a standard curve for Cu is established using the internal standard method, resulting in a detection limit of Cu element of 0.045 mg/L. The Cu mass concentration of the Yellow River water at different sampling points is measured experimentally using LIBS-GD, and the measurement results are compared with those of flame atomic absorption spectroscopy (AAS) method. The results show that the two methods have good consistency. The LIBS-GD combined technique demonstrates good performance in detecting Cu element, offering a more convenient and efficient option for heavy metal elements detection in water body.

Titanium alloy has excellent properties such as low density, high strength and corrosion resistance. It has been widely used in aviation manufacturing fields such as compressor disks and blades manufacturing. However, as the hardness of titanium alloy materials is low, friction loss and other problems are prone to occur during service, which reduces the service life of components. Therefore, in‑situ and real-time hardness monitoring is of great significance for the performance evaluation of titanium alloy components during use. The different hardness of TC4 titanium alloy samples after heat treatment was characterized by laser-induced breakdown spectroscopy (LIBS) in this work. The experimental results showed that the laser-induced plasma temperature of the material increases with the increase of the sample hardness, indicating that laser-induced plasma temperature can be used as an important characterization parameter of metal hardness. Furthermore, the internal mechanism of the influence of material hardness change on plasma temperature was revealed.

Flotation is an important step in the ore dressing process, and the slurry grade during the flotation process is an important indicator that needs to be grasped in real-time in the ore dressing process. The authors' laboratory has developed an online slurry composition analyzer based on laser-induced breakdown spectroscopy(LIBS), SIA-LIBSlurry, which can measure the content of each element in the slurry during the flotation process in real time by collecting spectral data. However, the spectral data of iron ore slurry are of high dimensionality, and the strong multiple covariance and nonlinearity between the data increase the complexity of modeling. To address this issue, two variable selection algorithms are compared: the competitive adaptive reweighting algorithm (CARS) and the successive projection algorithm (SPA), and then the two algorithms with support vector machine regression (SVR) are combined to establish a quantitative analysis model. The results show that the SVR model built with the full spectrum of 6116 variables has low accuracy, with a root mean square error of prediction of 1.45%; the CARS-SVR model built with the 231 variables screened by CARS had improved predictive ability, with a root mean square error of prediction of 1.09%; and the best prediction is achieved by the SPA-SVR, a model built with the 12 variables screened by SPA, with a root mean square error of prediction down to 0.97%. Therefore, it is indicated that the SPA-SVR model has a high prediction accuracy, which helps to improve the accuracy of online monitoring of the SIA-LIBSlurry analyzer.

It is significant for archaeology to detect fragments of ceramics on-line and rapidly. Laser-induced breakdown spectroscopy (LIBS) technique is well suited for the classification detection of ceramic fragments online for its characteristics such as non-contact, fast, and no sample preparation. A LIBS system equipped with a self-developed micro nanosecond laser was used to analyze the fragments of ceramic, and the principal components and loadings of ceramic spectral data were extracted using principal component analysis (PCA) to analyze the ancient ceramic fragment samples. The analysis results show that the ceramic fragments can be identified by using PCA, but similar element compositions cannot be accurately distinguished. Further classification using linear discriminant analysis (LDA) show that the spectra of the ancient ceramic fragment samples can be correctly classified. Finally, LDA classification algorithm was used to classify and identify all 18 ceramic debris samples, and the total classification accuracy is 98.15%. The results show that the use of micro-LIBS equipment can quickly and accurately identify cultural relics fragments, which can provide effective help for the protection and restoration of cultural relics fragments.

Geological analysis can provide important information and basis for geological resource exploration. Laser-induced breakdown spectroscopy (LIBS) can provide a rapid, accurate, and in-situ discriminant method for rock analysis. The application of high repetition rate LIBS technology to lithology analysis of rock samples, combined with the convolutional neural network (CNN) model for classification, not only solves the problems of large volume and heavy weight of traditional high-energy single-pulse lasers, but also overcomes the shortcomings of poor universality of traditional machine learning algorithm models, and also comforms to the development trend of LIBS technology towards portability, miniaturization, precision and intelligence. A high repetition rate LIBS experimental platform is used to collect the spectra of rock compression samples, and the rock samples are divided into 5 and 9 categories according to the origin and lithology of the rocks, and then 1D-CNN and ResNet34 convolutional neural network models are used to classify them. The results show that the combination of high repetition frequency LIBS and CNN can achieve rock classification, with the optimal results of 99.43% and 97.14% respectively. Finally, based on MATLAB App Designer, a system software for rock lithology analysis is developed, which realizes the rapid real-time classification of rocks and greatly improves the efficiency of geological exploration.