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.

Blood cancer diagnosis using hyperspectral imaging combined with the forward searching method and machine learning.

Hyperspectral imaging (HSI), a widely used biosensing technique, has been applied to tumor detection. Rapid, accurate, and low-cost detection of blood cancer using hyperspectral technology remains a challenge. We developed a new method to discriminate blood cancer using hyperspectral imaging (HSI) and the forward searching method (FSM). Four commonly used classification models are applied for four types of blood cancer spectra recognition. The support vector machine (SVM) model with the highest recognition accuracy (94.5%) combined with HSI achieves high-precision tumor identification. For higher recognition accuracy and lower hardware barriers, based on the selection probabilities of spectral lines calculated by a multi-objective atomic orbital search method, the FSM is proposed for HSI feature selection. With the proposed method, the wavelength band range of the spectrum is reduced by at least 50%. Compared with the traditional dimensionality reduction methods, the FSM can obtain a higher accuracy rate with lower hardware requirements. These results show that our proposed method can achieve non-invasive rapid screening of blood cancers with lower hardware requirements. Therefore, HSI assisted with FSM and SVM hybrid models can be a powerful and promising tool for blood cancer detection.

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.

Self-absorption correction method for one-point calibration laser-induced breakdown spectroscopy.

As an important variant of calibration-free laser-induced breakdown spectroscopy (CF-LIBS), one-point calibration LIBS (OPC-LIBS) corrects the Boltzmann plot of the unknown sample by using one known sample and obtains higher quantitative accuracy than CF-LIBS. However, the self-absorption effect restricts its accuracy. In this work, a new self-absorption correction (SAC) method for OPC-LIBS is proposed to solve this problem. This method uses an algorithm to correct the self-absorption and does not require the calculation of the self-absorption coefficient. To verify the effectiveness of this SAC method, Ti, V, and Al elements in two titanium alloys were determined by classical OPC-LIBS and OPC-LIBS with SAC. The average relative errors (AREs) of all elements in the two samples were decreased from 8.78% and 9.28% to 8.07% and 7.56%, respectively. The results demonstrated the effectiveness of this SAC method for OPC-LIBS.

Indirect quantitative analysis of soluble solid content in citrus by the leaves using hyperspectral imaging combined with machine learning.

Due to the effect of bagging on fruit growth, non-destructive and in situ soluble solid content (SSC) in citrus detection remains a challenge. In this work, a new method for accurately quantifying SSC in citrus using hyperspectral imaging of citrus leaves was proposed. Sixty-five Ehime Kashi No. 28 citruses with surrounding leaves picked at two different times were picked for the experiment. Using the principal components analysis combined with Gaussian process regression model, the correlation coefficients of prediction-real value by citrus and its leaves in cross-validation were 0.972 and 0.986, respectively. In addition, the relationship between citrus leaves and SSC content was further explored, and the possible relationship between chlorophyll in leaves and SSC of citrus was analyzed. Comparing the quantitative analysis results by citrus and its leaves, the results show that the proposed method is a non-destructive and reliable method for determining the SSC by citrus leaves and has broad application prospects in indirect detection of citrus.

Stable sensing platform for diagnosing electrolyte disturbance using laser-induced breakdown spectroscopy.

Electrolyte disturbance is very common and harmful, increasing the mortality of critical patients. Hence, rapid and accurate detection of electrolyte levels is vital in clinical practice. Laser-induced breakdown spectroscopy (LIBS) has the advantage of rapid and simultaneous detection of multiple elements, which meets the needs of clinical electrolyte detection. However, the cracking caused by serum drying and the effect of the coffee-ring led to the unstable spectral signal of LIBS and inaccurate detection results. Herein, we propose the ordered microarray silicon substrates (OMSS) obtained by laser microprocessing, to solve the disturbance caused by cracking and the coffee-ring effect in LIBS detection. Moreover, the area of OMSS is optimized to obtain the optimal LIBS detection effect; only a 10 uL serum sample is required. Compared with the silicon wafer substrates, the relative standard deviation (RSD) of the serum LIBS spectral reduces from above 80.00% to below 15.00% by the optimized OMSS, improving the spectral stability. Furthermore, the OMSS is combined with LIBS to form a sensing platform for electrolyte disturbance detection. A set of electrolyte disturbance simulation samples (80% of the ingredients are human serum) was prepared for this platform evaluation. Finally, the platform can achieve an accurate quantitative detection of Na and K elements (Na: RSD < 6.00%, R2 = 0.991; K: RSD < 4.00%, R2 = 0.981), and the detection time is within 5 min. The LIBS sensing platform has a good prospect in clinical electrolyte detection and other blood-related clinical diagnoses.

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

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

Meat species identification accuracy improvement using sample set portioning based on joint x-y distance and laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was suitable for the identification of meat species due to fast and less sample preparation. However, the problem of low accuracy rate of the recognition model caused by improper selection of training set samples by random split has severely restricted the development of LIBS in meat detection. Sample set portioning based on the joint x-y distance (SPXY) method was applied for dividing the meat spectra into a training set and a test set. Then, the five kinds of meat samples (shrimp, chicken, beef, scallop, and pig liver) were classified by the support vector machine (SVM). With the random split method, Kennard-Stone method, and SPXY method, the recognition accuracies of the SVM model were 90.44%, 91.95%, and 94.35%, respectively. The multidimensional scaling method was used to visualize the results of the sample split for the interpretation of the classification. The results showed that the identification performance of the SPXY method combined with the SVM model was best, and the accuracy rates of shrimp, chicken, beef, scallop, and pig liver were 100.00%, 100.00%, 100.00%, 78.57%, and 92.00%, respectively. Moreover, to verify the broad adaptability of the SPXY method, the linear discriminant analysis model, the K-nearest neighbor model, and the ensemble learning model were applied as the meat species identification model. The results demonstrated that the accuracy rate of the classification model can be improved with the SPXY method. In light of the findings, the proposed sample portioning method can improve the accuracy rate of the recognition model using LIBS.

FROZEN! Intracellular multi-electrolyte analysis measures millimolar lithium in mammalian cells.

Lithium salts are commonly used as medication for Bipolar Disorder (BD) and depression. However, there are limited methods to quantify intracellular lithium. Most methods to analyze intracellular electrolytes require tedious sample processing, specialized and often expensive machinery, sometimes involving harmful chemicals, and a bulk amount of the sample. In this work, we report a novel method (FROZEN!) based on cell isolation (from the surrounding medium) through rapid de-ionized water cleaning, followed by flash freezing for preservation. SKOV3 cells were cultured in normal medium and a medium containing 1.0 mM lithium. Lithium and other intracellular electrolytes in the isolated and preserved cells were simultaneously analyzed with laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence spectroscopy (XRF). Key electrolytes such as sodium, potassium, and magnesium, along with lithium, were detectable at the single-cell level. We found that cells cultured in the lithium medium have an intracellular lithium concentration of 0.5 mM. Concurrently, the intracellular concentrations of other positively charged electrolytes (sodium, potassium, and magnesium) were reduced by the presence of lithium. FROZEN! will greatly facilitate research in intracellular electrolyte balance during drug treatment, or other physiological stresses. In particular, the cell isolation and preservation steps can be easily performed by many laboratories worldwide, after which the samples are sent to an analytical laboratory for electrolyte analysis.

Stable and ultrasensitive analysis of organic pollutants and heavy metals by dried droplet method with superhydrophobic-induced enrichment.

Herein, a dried droplet method (DDM) with superhydrophobic-induced enrichment is reported for stable and ultrasensitive analysis of organic pollutants and heavy metals. A superhydrophobic (SHB) substrate was prepared as an analytical detection platform for the DDM. This SHB substrate was synthesized by sequentially coating polydimethylsiloxane (PDMS) and titanium dioxide nanoparticles (TiO2 NPs) onto glass substrate surface. In the droplet drying process, the SHB substrate was demonstrated to suppress the coffee ring effect (CRE) and enriched analyte concentration. Combining with Raman spectroscopy for analysis of methylene blue (MB), and with laser-induced breakdown spectroscopy (LIBS) for analysis of chromium (Cr), the results indicated high stability and ultra-sensitivity for organic pollutants and heavy metals detection. Overall, the DDM with superhydrophobic-induced enrichment has big potential in applications requiring stable and ultrasensitive analysis.

Identification of Graves' ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method.

Diagnosis of the Graves' ophthalmology remains a significant challenge. We identified between Graves' ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves' ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves' ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves' ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves' ophthalmology.

Stability improvement for dried droplet pretreatment by suppression of coffee ring effect using electrochemical anodized nanoporous tin dioxide substrate.

A novel nanoporous analytical platform is reported to improve the stability of the dried droplet method (DDM). This nanoporous platform was made of tin dioxide (Np SnO2) substrate by electrochemical anodization from tin (Sn) slide. The DDM is a widely used sample pretreatment in analytical chemistry that involves placing a droplet of solution onto the substrate and drying for analytical testing. However, during the droplet drying process, the solutes would converge at the droplet edge and cause inhomogeneous solutes distribution. This is the coffee ring effect (CRE). The Np SnO2 has irregular nanopores, which allows droplet solutions to penetrate into the substrate rather than spreading out, effectively suppressing CRE. Theoretical models were built to explain the formation of CRE on blank tin (Sn) substrate and suppression of CRE on Np SnO2. Better results were obtained in detecting lithium (Li) using the Np SnO2 by laser-induced breakdown spectroscopy (LIBS). The line scanning results indicated that the Li emission line (670.8 nm) intensities on Np SnO2 substrate had lower relative standard deviation (RSD = 3.3%) than those on Sn substrate (RSD = 31.5%), which illustrate suppression of CRE and stability improvement on Np SnO2 substrate. Furthermore, Li calibration curves were built for LIBS with DDM. The curve using Np SnO2 substrate had better linearity (R2 = 0.997), higher precision (RSD = 4.2%), and higher sensitivity (LOD = 0.13 mg/L) than that by Sn substrate (R2 = 0.954, RSD = 17%, and LOD = 1.21 mg/L). All in all, the anodic Np SnO2 substrate can suppress CRE in DDM and hence improve the stability and precision of subsequent analysis. Graphical abstract.

Blood cancer diagnosis using ensemble learning based on a random subspace method in laser-induced breakdown spectroscopy.

There are two main challenges in the diagnosis of blood cancer. The first is to diagnose cancer from healthy control, and the second is to identify the types of blood cancer. The chemometrics method combined with laser-induced breakdown spectroscopy (LIBS) can be used for cancer detection. However, chemometrics methods were easily influenced by the spectral feature redundancy and noise, resulting in low accuracy rate because of their simple structure. We proposed an approach using LIBS combined with the ensemble learning based on the random subspace method (RSM). The serum samples were dripped onto a boric acid substrate for LIBS spectrum collection. The complete blood cancer sample set include leukemia [acute myeloid leukemia (AML) and chronic myelogenous leukemia (CML)], multiple myeloma (MM), and lymphoma. The results showed that the accuracy rates using k nearest neighbors (kNN) and linear discriminant analysis (LDA) only were 88.14% and 94.45%, respectively, while using RSM with LDA (RSM-LDA), the average accuracy rate was improved from 94.45% to 98.34%. Furthermore, the variable importance of spectral lines (Na, K, Mg, Ca, H, O, N, C-N) were evaluated by the RSM-LDA model, which can improve the recognition ability of blood cancer types. Comparing the RSM-LDA model and only with LDA, the results showed that the average accuracy rate for cancer type identification was improved from 80.4% to 91.0%. These results demonstrate that LIBS combined with the RSM-LDA model can discriminate the blood cancer from the health control, as well as the recognition the types for blood cancers.

Half-life determination of inorganic-organic hybrid nanomaterials in mice using laser-induced breakdown spectroscopy.

Inorganic or inorganic-organic hybrid nanomaterials have great potential for applications in the biomedical fields. Biological half-life is an essential pharmacokinetic parameter for these materials to function in vivo. Compared to inductively coupled plasma mass spectrometry (ICP-MS), which is the gold standard, laser-induced breakdown spectroscopy (LIBS) is a faster and more efficient elemental detection method. We investigated an efficient way to quantify the metabolic rate using LIBS. Nanoparticle platforms, such as manganese dioxide-bovine serum albumin (MnO2-BSA) or boehmite-bovine serum albumin (AlO(OH)-BSA) were injected into mice through intravenous administration for LIBS spectrum acquisition. First, the spectral background was corrected using the polynomial fitting method; The spectral interference was eliminated by Lorentz fitting for each LIBS spectrum simultaneously. The support vector regression (SVR) was then used for LIBS quantitative analyses. Finally, the LIBS results were compared with the ICP-MS ones. The half-lives of MnO2-BSA calculated by LIBS and ICP-MS were 2.49 and 2.42 h, respectively. For AlO(OH)-BSA, the half-lives detected by LIBS and ICP-MS were 3.46 and 3.57 h, respectively. The relative error of LIBS is within 5% compared to ICP-MS. The results demonstrate that LIBS is a valuable tool for quantifying the metabolic rates with a high degree of accuracy.

Simultaneous Compositional and Grain Size Measurements Using Laser Opto-Ultrasonic Dual Detection for Additive Manufacturing.

Metal-based additive manufacturing (AM) is a disruptive technique with great potential across multiple industries; however, its manufacturing quality is unstable, leading to an urgent requirement for component properties detection. The distribution of grain size has an important effect on many mechanical properties in AM, while the distribution of added elements, such as titanium (Ti), has a measurable effect on the grain size of an aluminum (Al) alloy. Therefore, the detection of the distributions of grain size and elements is of great significance for AM. In this study, we investigated the distribution of grain size and elements simultaneously for wire + arc additive manufacturing (WAAM) with an Al alloy using laser opto-ultrasonic dual (LOUD) detection. The average grain size obtained from the acoustic attenuation of ultrasonic signals was consistent with the results of electron backscatter diffraction (EBSD), with a coefficient of determination (R2) of 0.981 for linear fitting. The Ti element distribution obtained from optical spectra showed that the enrichment of Ti corresponded to the grain refinement area in the detected area. The X-ray diffraction (XRD) spectra showed that the spectral peaks were moved from Al to AlTi and Al2Ti forms in the Ti-rich areas, which confirmed the LOUD results. The results indicated that LOUD detection holds promise for becoming an effective method of analyzing the mechanical and chemical properties of components simultaneously, which could help explain the complex physical and chemical changes in AM and ultimately improve the manufacturing quality.

A plasma-image-assisted method for matrix effect correction in laser-induced breakdown spectroscopy.

The matrix effect is one of the main bottlenecks for the laser-induced breakdown spectroscopy (LIBS) technique. In this work, image-assisted, laser-induced breakdown spectroscopy (IA-LIBS) based on the Lomakin-Scherbe formula was put forward as a correction to the matrix effect. The brightness and area information in the plasma image was extracted to correct the spectral line intensities among which the brightness information characterizes the plasma temperature, and the area information characterizes the ablative mass. To verify the feasibility of this method, the experiment was conducted on metal samples and pressed samples. The method was applied for quantitative analysis of copper (Cu), magnesium (Mg) in metal samples and chromium (Cr), manganese (Mn) in pressed samples. For the metal samples, after correcting the matrix effect by IA-LIBS, the determination coefficient R squared (R2) of Cu I 510.55 nm and Mg I 518.36 nm calibration curves were increased from 0.726 to 0.942 to 0.992 and 0.988, respectively. The root-mean-square-error of cross-validation (RMSECV) and the average relative error (ARE) decreased by 75.10% and 77.18%, respectively. For the pressed samples, R2 of Cr I 520.84 nm and Mn I 403.07 nm calibration curves corrected by IA-LIBS increased from 0.364 to 0.098 to 0.975 and 0.980; and RMSECV and ARE decreased by 77.88% and 83.83%, respectively. The experimental results showed that IA-LIBS had an obvious improvement on elimination of the matrix effect for the different samples and the different elements. Therefore, IA-LIBS will become a promising technology and will greatly promote the development of LIBS in various fields.

Multivariate quantitative analysis of metal elements in steel using laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS), an element-detection technology with the advantages of no sample preparation and in situ detection of metal samples, is suitable for the quantitative analysis of metal samples. However, severe spectral interference in the detection of metal samples makes the quantitative analysis difficult. Three quantitative analysis methods, including single-variable calibration, partial least squares regression (PLSR), and support vector regression (SVR), are used to conduct the quantitative analysis of four common metal elements (Manganese (Mn), Chromium (Cr), Vanadium (V), and Titanium (Ti)). The PLSR model adds interference spectrum lines to the model for linear modeling, while the SVR model adds interference spectrum lines to the model for nonlinear modeling. The quantitative analysis results of the nonlinear SVR model are the best. The R square (R2) values of Mn, Cr, V, and Ti are 0.993, 0.995, 0.990, and 0.992, respectively. The root-mean-squared errors of the prediction set of Mn, Cr, V, and Ti are 0.044, 0.045, 0.011, and 0.014, respectively. Therefore, the results of PLSR and SVR are better than the calibration curves of the spectral intensity and concentration due to the influence of multivariate factors. SVR has almost no element bias, while PLSR and the single-variable calibration model have different quantitative results due to the different degrees of influence on spectral lines. These results demonstrate that the combined influence of the spectral interference, background noise, and self-absorption can be suppressed by the nonlinear quantitative analysis model in the steel field using LIBS.

Determination of trace heavy metal elements in aqueous solution using surface-enhanced laser-induced breakdown spectroscopy.

Heavy metal pollution is one of the main problems in water pollution, which is harmful to humans. Surface-enhanced laser-induced breakdown spectroscopy (SENLIBS) has been applied to detect trace amounts of heavy metal elements in aqueous solution; however, it is still a big challenge to explore the relationship between the LIBS detection sensitivity and the substrate's physical properties. In this work, four typical substrates, zinc (Zn), magnesium alloy (Mg), nickel (Ni), and silicon (Si), were compared; and the mechanism of spectral enhancement by different substrates in SENLIBS was investigated. The results indicated that the limit of detection (LoD) of heavy metal elements on different substrates is positively proportional to the boiling of the substrate. That is mainly because a higher plasma excitation temperature and electron density are obtained, leading to more intense collision between particles. The signal enhancement is associated with the lower boiling point of the substrate (corresponding to a lower ablation threshold and higher ablation quantity from the substrate). As a result, the best LoD was 0.0011 mg/L for chromium (Cr) and 0.004 mg/L for lead (Pb) on an optimal Zn substrate, respectively. The LoDs were sufficiently low to meet the drinking water sanitation standard. These results showed that the detection sensitivity of heavy metal elements in aqueous solution can be improved by choosing a substrate with a lower boiling point in SENLIBS.

Rapid quantitative analysis of adulterated rice with partial least squares regression using hyperspectral imaging system.

BACKGROUND: Rice adulteration in the food industry that infringes on the interests of consumers is considered very serious. To realize the rapid and precise quantitation of adulterated rice, a visible near infrared (VNIR) hyperspectral imaging system (380-1000 nm) was developed in the present study. A Savitsky-Golay first derivative (SG1) transform was utilized to eliminate the constant spectral baseline offset. Then, the adulterated levels of rice samples were quantified by partial least squares regression (PLSR). RESULTS: A SG1-PLSR model based on full-wavelength was attained with a coefficient of determination of prediction set (RP ) of 0.9909, root-mean-square error of prediction set (RMSEP ) of 0.0447 g kg-1 and residual predictive deviation (RPDP ) of 11.28. Furthermore, fifteen important wavelengths were selected based on the weighted regression coefficients (BW ) and a simplified model (PLSR-15) was established with RP of 0.9769, RMSEP of 0.0708 g kg-1 and RPDP of 3.49. Finally, two visualization maps produced by applying the optimal models (SG1-PLSR and PLSR-15) were used to visualize the adulterated levels of rice. CONCLUSION: These results demonstrate that VNIR hyperspectral imaging system is an effective tool for rapidly quantifying and visualizing the adulterated levels of rice. © 2019 Society of Chemical Industry.

Investigation of the self-absorption effect using time-resolved laser-induced breakdown spectroscopy.

Self-absorption seriously affects the accuracy and stability of quantitative analysis in laser-induced breakdown spectroscopy (LIBS). To reduce the effect of self-absorption, we investigated the temporal evolution of the self-absorption effect by establishing exponential calibration curves. Meanwhile, the temporal evolution mechanism of the self-absorption effect was also investigated. The results indicated that self-absorption was weak at the early stage of plasma expansion. For determination of manganese (Mn) in steel, as an example, the concentration of upper bound of linearity (Cint) was 2.000 wt. % at the early stage of plasma expansion (in a time window of 0.2-0.4 µs)-much higher than 0.363 wt. % at a traditional optimization time window (2-3 µs). The accuracy and stability of quantitative analysis at the time window of 0.2-0.4 µs was also much better than at the time window of 2-3 µs. This work provides a simple method for improving quantitative analysis performance and avoiding the self-absorption effect in LIBS.