Laser-induced Breakdown Spectroscopy Spectra Research Articles

Real-time LIBS monitoring of laser-based layered controlled paint removal from aircraft skin based on random forest.

Laser-induced breakdown spectroscopy (LIBS) is expected to be used for real-time monitoring and closed-loop control of laser-based layered controlled paint removal (LLCPR) from aircraft skin. However, the LIBS spectrum must be rapidly and accurately analyzed, and the monitoring criteria should be established based on machine learning algorithms. Hence, this study develops a self-built LIBS monitoring platform for the paint removal process utilizing a high-frequency (kilohertz-level) nanosecond infrared pulsed laser and collects the LIBS spectrum during the laser removal process of the top coating (TC), primer (PR), and aluminum substrate (AS). After subtracting the spectrum's continuous background and screening the key features, we construct a classification model of three types of spectra (TC, PR, and AS) based on a random forest algorithm, and the real-time monitoring criterion based on the classification model and multiple LIBS spectra was established and verified experimentally. The results show that the classification accuracy is 98.89%, the time-consuming classification is about 0.03ms per spectrum, and the monitoring results of the paint removal process are consistent with the macroscopic observation and microscopic profile analysis results of the samples. Overall, this research provides core technical support for the real-time monitoring and closed-loop control of LLCPR from aircraft skin.

Effect of cavity-confinement and microwave-assistance on the sensitivity of LIBS for detecting Cu content in pig feed

The laser-induced breakdown spectroscopy (LIBS) is a potential tool for detecting heavy metals in pig feed. In order to implement a highly sensitive LIBS system required for the detection of Copper in pig feed and achieve better detection results, this work combines cavity-confinement and microwave-assistance with the LIBS system. Based on the characteristic spectral intensity of Cu I 324.75 nm, four factors including delay time, laser energy, cavity-confinement diameter, and microwave-assisted power are optimized by using single factor method and response surface methodology (RSM) for obtaining suitable experimental parameters and detection performance. The detection results show that as compared with the single factor method, the optimal experimental parameters obtained by using RSM increase the intensity of the characteristic spectral lines by 21.69% and the detection limit is as low as 0.878 mg/kg. As compared with the traditional LIBS techniques, the use of cavity-confinement combined with microwave-assistance increase the detection sensitivity of LIBS technique by 18.6-fold. The results show that RSM effectively analyzes the interactions between factors and their effects on the response values to effectively obtain better values for each factor. This enhances the LIBS spectra by cavity-confinement and microwave-assistance, and improves the sensitivity of detection of Cu in the pig feeds, which is suitable for realizing the quantitative detection of low concentration elements in pig feeds.

Using biospeckle and LIBS techniques with artificial intelligence to monitor phthalocyanine-gold nanoconjugates as a new drug delivery mediator for in vivo PDT

Photodynamic therapy (PDT) is a promising treatment method that generates reactive oxygen species by exciting a photosensitizer in the presence of light. The positive treatment outcome of PDT is enhanced by the photosensitizer’s ability to get out quickly from normal tissue. This study non-destructively evaluated the biocompatibility of novel synthesized phthalocyanine-gold nanoconjugates (Pc-Au NCs) as a drug delivery mediator for in vivo PDT. We first proposed a novel noncontact and safe biospeckle optical imaging technique to visualize the quantitative changes in the internal dynamics of biological tissue in response to PDT treatment. Second, laser-induced breakdown spectroscopy (LIBS) was utilized to emphasize the accumulation of gold nanoparticles (Au NPs) within the normal and cancerous tissue, which in turn verifies the cytotoxic and treatment effects of the Pc-Au NCs. The biospeckle images and LIBS spectra were obtained from tumor and normal tissues of male BALB/c malignancy-bearing mice before and after exposure to PDT. Then, they were statistically treated via different artificial intelligence techniques. For biospeckle analysis, the proposed method was established from the combination of second and higher-order statistical features extracted from the gray-level co-occurrence matrix (GLCM) and local contrast analysis (LCA), respectively, through principal component analysis (PCA). Regarding LIBS analysis, the powerful, most popular ensemble sequential learning algorithm Adaptive Boost was implemented on highly uncorrelated variables extracted from the raw spectra. The results, confirmed by the histological investigation, indicated the potential of the biospeckle and LIBS statistical properties in demonstrating the efficiency of the Pc-Au NCs as a drug delivery mediator for in vivo PDT.

A data analysis method to rapidly characterize gallium concentration in plutonium matrices using LIBS

The processing of actinide samples is a complex and costly endeavor that requires compositional analysis at various stages. Laser-induced breakdown spectroscopy (LIBS) has been used to analyze actinide-containing samples in many nuclear applications including waste management, fuel processing and forensics. The LIBS spectrum obtained from actinide materials are generally extremely complex, exhibiting many thousands of strong emission lines. This makes it difficult to identify other elements within the sample of interest, given the rich and dominant actinide spectrum. In this article we describe a recent effort to identify and quantify impurities and alloying constituents in plutonium matrices using a hand-held LIBS instrument that is used to rapidly and efficiently measure an emission spectrum from a material sample. We tabulate the emission line positions and intensities of plutonium. We report the development of machine-learning software that can identify gallium and quantify its concentration in plutonium matrices. This work has the potential to provide a rapid and nearly non-destructive technique that allows more confidence in characterizing the composition of materials that are present within complex actinide associated targets. We describe how our LIBS measurements and data analysis methods have successfully quantified the gallium concentration in a variety of samples.

Geochemical identification and classification of cherts using handheld laser induced breakdown spectroscopy (LIBS) supported by supervised machine learning algorithms

The elemental analysis of cherts is very important for understanding their geological history, formation and origin. In this work, chert samples collected from a mining complex located in the Gargano promontory (Apulia, Italy) have been investigated by laser-induced breakdown spectroscopy (LIBS) using a commercial handheld (h) LIBS instrument. In order to discriminate chert samples according to their mining site, the broadband LIBS spectra acquired on the specimens were subjected to multivariate data analysis. In particular, machine learning (ML) algorithms, including discriminant analysis (DA), support vector machine (SVM), and k-nearest neighbors (KNN), were applied to the data set from the averaged LIBS spectra of each sample dimensionally reduced by principal components analysis (PCA). Among the classifiers tested, the highest accuracy (72.34%) in identifying the chemical features of five different chert suites and discriminating their sources was achieved by Leave-One-Out Cross-Validation (LOO-CV) using the cosine KNN ML algorithm. The exclusion of PC1, which appeared to be mainly influenced by elements featuring large intra-class variation and engraved the clusterization, allowed to improve the accuracy of the classification reaching the value of 80.85% using the linear DA algorithm. The classification and discrimination between 2 classes (1 × 1) reached a high rate of correct predictions, which implied that this discrimination could be used as a subsequent mean to confirm if the class predicted in the previously performed multiclass classification was correct. In conclusion, the results of this study demonstrated that even chemically similar geological materials could be discriminated using LIBS analysis combined with appropriate multivariate chemometric data processing, in particular with the aim of recognizing and differentiating specific geological chert sources and identifying the origin of chert archaeological artifacts.

Wavelength Calibration for the LIBS Spectra of the Zhurong Mars Rover

China’s first Mars rover, Zhurong, landed on the southern region of Utopia Planitia, Mars, on 14 May 2021 (UTC). Zhurong is equipped with the Mars Surface Composition Detection Package (MarSCoDe), which analyzes the Martian surface’s material composition. Composed of laser-induced breakdown spectroscopy (LIBS), short-wave infrared spectroscopy (SWIR), and a microimaging camera, MarsCoDe can work at a distance of 1.6–7 m to analyze element abundance and the mineralogy of targets on the Martian surface. Analysis shows that the wavelengths of MarSCoDe onboard LIBS spectra acquired within the same probe period will have different degrees of drift, leading to deviation in qualitative and quantitative elemental analysis. This paper finds that the spectrum drift follows a quadratic function relationship with the CCD temperature of the MarSCoDe spectrometer, based on which a wavelength calibration method is established. According to the function, the drift of a certain channel is calculated by the corresponding CCD temperature, and then the wavelength of the spectrum is calibrated by the drift. The accuracy of this calibration method for the position of peak wavelength in the LIBS spectrum can reach about 1/5 of the apparatus spectral width, and the cross-validation analysis using a norite standard sample shows that it is comparable to the wavelength calibration accuracy of the ChemCam onboard data product.

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 (R2P = 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.

Compositional Variations in Sedimentary Deposits in Gale Crater as Observed by ChemCam Passive and Active Spectra

AbstractDuring the first 2934 sols of the Curiosity rover's mission 33,468 passive visible/near‐infrared (NIR) reflectance spectra were taken of the surface by the mast‐mounted Chemistry and Camera (ChemCam) instrument on a range of target types. ChemCam spectra of bedrock targets from the Murray and Carolyn Shoemaker formations on Mt. Sharp were investigated using principal component analysis and various spectral parameters including the band depth at 535 nm and the slope between 840 and 750 nm. Four end‐member spectra were identified. Passive spectra were compared to Laser Induced Breakdown Spectroscopy (LIBS) data to search for correlations between spectral properties and elemental abundances. The correlation coefficient between FeOT reported by LIBS and BD535 from passive spectra was used to search for regions where iron may have been added to the bedrock through oxidation of ferrous‐bearing fluids but no correlations were found. Rocks in the Blunts Point‐Sutton Island transition that have unique spectral properties compared to surrounding rocks, that is flat NIR slopes and weak 535 nm absorptions, are associated with higher Mn and Mg in the LIBS spectra of bedrock. Additionally, calcium‐sulfate cements, previously identified by Ca and S enrichments in the LIBS spectra of bedrock, were also shown to be associated with spectral trends seen in Blunts Point. A shift toward a steeper NIR slope is seen in the Hutton interval, indicative of changing depositional conditions or increased diagenesis.

Evaluation of the nutrients in milk supplements suitable for children using LIBS and PAS technique

The presence of macro and micro nutrients essential for human health in four milk supplements is quickly identified using Laser-induced Breakdown Spectroscopy (LIBS) and Photoacoustic Spectroscopy (PAS). In addition to the minerals reported by the manufacturers, some additional spectral signatures of the elements like Na and K are also observed in the LIBS spectra which are not listed by some manufacturers. The ratio of various minerals like Ca/Mg, Na/Mg, etc. has been evaluated, and found a good correlation with the nutritional value listed on the product packaging. The organic elements C, H, N, and O together with the CN molecular band observed in the LIBS spectra of milk supplement indicate the presence of organic molecules. PAS is used for the identification of organic compounds such as Vitamins, Proteins, Fat, Taurine, and Sucrose in milk supplements. The results of the present study help in selecting milk supplements suitable for children suffering from chronic kidney diseases and diabetes. Based on the presence of minerals and their relative concentration, milk supplements are classified into three groups by applying Principal Component Analysis (PCA). Therefore, the results from the present work extend the application of LIBS and PAS together with PCA for the identification of the nutrients in milk supplements.

Discrimination of Salvia miltiorrhiza from Different Geographical Origins by Laser-Induced Breakdown Spectroscopy (LIBS) with Convolutional Neural Network (CNN)

A method for discriminating Salvia miltiorrhiza from different geographical origins was developed using laser-induced breakdown spectroscopy (LIBS) with convolutional neural network (CNN). The LIBS spectra of Salvia miltiorrhiza samples from six geographical locations were preprocessed with the maximum minimum normalization method. The classification model for discriminating these samples was developed by using a one-dimensional convolutional neural network. The discrimination accuracy of the developed CNN model reached 97.09%. Compared with support vector machine and k-nearest neighbor methods, the CNN model showed higher discrimination accuracy. The results demonstrate that the combination of LIBS and CNN is suitable for discriminating Salvia miltiorrhiza from different geographical locations.

Deep learning regression for quantitative LIBS analysis

One of the most promising innovation strategies for sorting and recycling post-consumer aluminium scrap is using quantitative Laser-Induced Breakdown Spectroscopy (LIBS) analysis. However, existing methods to estimate alloying element concentrations based on LIBS spectra, such as linear univariate regression and Machine Learning models, are still too limited in their performance to achieve the accuracy demanded by the industry. Therefore, this study presents novel Deep Learning approaches and compares their performance to those of traditional univariate regression and Machine Learning methods in terms of RMSE, MAE, and R2 value. For this evaluation, two sample sets of aluminium pieces are used: one containing 27 certified aluminium reference samples and the second containing 733 post-consumer scrap pieces for which the ground truth concentrations are determined by X-Ray Fluorescence (XRF).Adopting multiple loss functions, one for each element, has proven its significant value for the regression performance. It improves the results for all performance metrics in the Scrap Sample set, and the same is true for the Reference Sample set, except for the coefficient of determination of Fe, Mn and Mg. In addition, the proposed methodology considers the learning prioritisation problem to prevent that learning the concentration of the base element is prioritised over the alloying elements. Although the effect of excluding the base alloy aluminium from the learning is small and not always positive for the performance, demonstrating this effect is also considered valuable. Since the average RMSE on the prediction is just 0.02 wt% for Al and Si, and not more than 0.01 wt% for Fe, Cu, Mn, Mg, and Zn, the best-performing Deep Learning model shows promise for the future of LIBS in metal sorting applications.

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.

Automatic Coal-Rock Recognition by Laser-Induced Breakdown Spectroscopy Combined with an Artificial Neural Network

Automatic coal-rock recognition (ACRR) is of considerable theoretical and practical significance for unmanned coal mining. To the best of our knowledge, this is the first study to assess laser-induced breakdown spectroscopy (LIBS) combined with an artificial neural network (ANN) for automatic coal-rock recognition. Each sample in this study was subjected to LIBS testing and spectrum collection 20 times in the air, and the average value was taken as the LIBS data. Spectral data were optimized and dimensionality reduction was performed using partial least-squares discriminant analysis (PLS-DA). The 10 selected wavelength lines were used to construct a simplified spectral model (SSM). The ANN based on SSM was designed to classify the coal and rock. The results demonstrated that LIBS combined with an ANN has a high recognition accuracy rate, providing a rapid and accurate coal-rock recognition method for unmanned coal mining.

Rapid Food Authentication Using a Portable Laser-Induced Breakdown Spectroscopy System.

Laser-induced breakdown spectroscopy (LIBS) is an atomic-emission spectroscopy technique that employs a focused laser beam to produce microplasma. Although LIBS was designed for applications in the field of materials science, it has lately been proposed as a method for the compositional analysis of agricultural goods. We deployed commercial handheld LIBS equipment to illustrate the performance of this promising optical technology in the context of food authentication, as the growing incidence of food fraud necessitates the development of novel portable methods for detection. We focused on regional agricultural commodities such as European Alpine-style cheeses, coffee, spices, balsamic vinegar, and vanilla extracts. Liquid examples, including seven balsamic vinegar products and six representatives of vanilla extract, were measured on a nitrocellulose membrane. No sample preparation was required for solid foods, which consisted of seven brands of coffee beans, sixteen varieties of Alpine-style cheeses, and eight different spices. The pre-processed and standardized LIBS spectra were used to train and test the elastic net-regularized multinomial classifier. The performance of the portable and benchtop LIBS systems was compared and described. The results indicate that field-deployable, portable LIBS devices provide a robust, accurate, and simple-to-use platform for agricultural product verification that requires minimal sample preparation, if any.

Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy.

Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice's value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample's elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92-100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.

Concept for an irradiation experiment to test a laser-induced breakdown spectroscopy off-gas sensor for molten salt systems

This paper focuses on laser-induced breakdown spectroscopy (LIBS) as an off-gas sensor and details a conceptual irradiation experiment to evaluate its performance in an environment that is more realistic of an operational molten salt reactor (MSR). MSRs are a promising advanced nuclear design that use high-temperature liquid salts as the coolant, fuel, or a combination of both. They will generate and subsequently evolve fission products into the reactor headspace during operation. These evolved fission products will necessitate an online off-gas system to treat the reactor cover gas before recycling it to the core. Such a system, especially during the early deployment phase, will benefit from online sensors to inform operators of the treatment system’s performance. Optical spectroscopy is well-suited to fit this task as it can be remotely deployed using fiber-optics and can detect most species at high frequencies. LIBS can detect nearly every element on the Periodic Table, stable or radioactive, making it an ideal candidate. The provided conceptual experiment discusses the facility requirements, salt capsule design, and detailed testing campaigns with corresponding simulated LIBS spectra.

Development of a Polymer Spectral Database for Advanced Chemometric Analysis

The use of chemometric techniques with spectral data for sorting plastics to improve recycling rates have gained more attention in recent years. However, insufficient representation of polymer spectra in spectral databases has been one of the barriers to the further development of these techniques. This work aims to develop a polymer spectra dataset that builds upon existing spectral databases on two fronts. Firstly, the data collected includes Laser-induced Breakdown Spectroscopy (LIBS) data in addition to more commonly available Infrared (IR) and Raman data. Secondly, the dataset includes unaged and weathered conditions of the same sample. In total, the dataset includes 732 spectra, with the LIBS, IR and Raman spectra of 122 unique samples, both before and after accelerated weathering. The data collected were qualitatively analyzed and visualized. Further work will explore the effect of using hybrid spectroscopic methods on chemometrics analysis results.

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 of laser-induced breakdown spectroscopy data of mouse organs based on the feature extraction method.

At present, there is no comprehensive and systematic research on laser-induced breakdown spectroscopy (LIBS) data visualization. In particular, the LIBS spectra of biological samples have large noise and weak signals, which seriously affect the feature visualization. Here, three commonly used sample visualization methods were compared, and a new method was applied for tissue sample visualization. We used the LIBS mapping technique to obtain LIBS spectra of different organ slice samples from mice. LIBS spectral distribution was visualized after extracting the region of interest. The three spectral visualization methods are compared, and the performance of visualization algorithms is quantitatively analyzed. The potential of heat-diffusion for the affinity-based transition embedding (PHATE) method highlights the details of the LIBS spectral distribution while maintaining the overall structure of the data. The correlation coefficient between dimensionality reduction data and raw data is 0.97, and the average distance between samples of different categories is 0.64, which are superior to those of traditional principal component analysis (PCA), multidimensional scaling (MDS), and t-distributed stochastic neighbor embedding (t-SNE). The results show that the PHATE method can serve as a very promising LIBS spectral visualization tool.

Quantitative analysis of Cd based on the stress effect of minerals in rice by laser-induced breakdown spectroscopy.

Cadmium (Cd) is a highly toxic heavy metal that can accumulate in the food chain, posing a significant threat to human health. One of the key food sources through which Cd is often observed is rice. Therefore, determining heavy metals in rice is essential to assess the risk status of rice. Laser-induced breakdown spectroscopy (LIBS) has the advantages of simple sample preparation and fast analysis, which is expected to achieve real-time and rapid detection of rice. In this work, 40 naturally matured rice samples growing from the area that is possibly contaminated with Cd were collected to determine the Cd reference content in rice by graphite furnace atomic absorption spectroscopy as recommended by the Chinese National Standard. LIBS spectral acquisition and analysis are adopted as well. The Cd characteristic spectral lines were selected to predict the Cd content directly using PCA, PLSR, and ELM models, and the coefficient of determination (R2) of the models' training and prediction sets was 0.9278, 0.8920; 0.9036, 0.9771; 0.7940, and 0.8409, respectively. Further, based on the Cd stress effect in rice, the spectra of elements Mn, Mg, K, and Na with highly significant and significant correlation with Cd were selected and coupled with the Cd characteristic spectra to form a new matrix of the same size for quantitative analysis. Based on the stress effect, R2 of models' training and prediction sets was improved to 0.9786, 0.9753; 0.9395, 0.9900; 0.9798, and 0.9927, respectively. It is demonstrated that combining the stress effect when using LIBS for quantitative analysis of Cd in rice reduces the overfitting and further improves the model's prediction accuracy. This work indicates that using LIBS combined with suitable mathematical models to predict the Cd content of naturally matured rice based on stress effects in rice is feasible. It is promising to evaluate the safety of rice by analyzing LIBS spectra.