Recent advances in laser-induced breakdown spectroscopy quantification: From fundamental understanding to data processing

A spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning for improving the performance of LIBS

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

Determination of bioequivalence between generic and reference drugs using laser-induced breakdown spectroscopy

BACKGROUND

In vitro bioequivalence studies are strictly limited to the comparison of dissolution performance to a reference drug. These studies are performed without considering the chemical similarity between the generic and reference drug formulations. This work has focused on developing a groundbreaking method based on the laser-induced breakdown spectroscopy (LIBS) technique for the in vitro bioequivalence determination of immediate-release solid oral dosage form generic drugs and as an alternative method for establishing the biowaiver of in vivo generic drug studies.

RESULTS

The novel LIBS-based methodology to determine in vitro bioequivalence is fast, easy to perform, and can be carried out without the requirement of tedious and complicated sample pre-treatment, nor expensive instrumentals and reagents, almost directly on the drug samples. Furthermore, the proposed methodology demonstrated that it is enough to identify the spectrochemical similarity of the formulation between generic drugs to a reference drug through the chemometric study of their LIBS spectra, based on the determination of the differentiation and similarity factors, f1 and f2, respectively, used in the pharmaceutical industry in this purpose. After analysing their LIBS spectra, the generic drugs selected for this work have all been shown to be in vitro bioequivalent, given their f1 values of less than 15 and f2 values greater than 50, according to the technical regulations on which the American and European medicines agencies are based for the approval of registration for generic immediate-release solid oral dosage form drugs. This has been evidenced even for drugs from Class III and Class IV of the biopharmaceutical classification system, whose active principle nominal concentration is very low as 0.1 and 0.25 mg/tablet, respectively.

SIGNIFICANCE

for the first time the LIBS technique has been successfully used in an advanced application for the pharmaceutical industry. The proposed method constitutes a reliable and specialized methodology for the establishment of formulation similarity between two drugs, without the requirement of separate identification of each of their components, which is a new and potential tool to determine the in vitro bioequivalence for generic immediate-release solid oral dosage form drugs.

The signal quality improvement of laser-induced breakdown spectroscopy due to the microwave plasma torch modulation

BACKGROUND

Laser-induced breakdown spectroscopy (LIBS) is a versatile analytical technique for element determination in solids, liquids, and gases. However, LIBS suffers from low detection sensitivity and high relative standard deviation (RSD), restricting its large-scale applications. the process of a physical sampling can, in some cases, compromise the mechanical strength of the component under examination. It should be considered that too large laser energy is bound to cause damage to samples which cannot be tolerated in the process of safe production in the nuclear industry. It is necessary to find a method to obtain high elemental signal intensity in low energy laser.

RESULTS

Here, we present a novel approach by integrating microwave plasma torch (MPT) with LIBS, referred to as MPT-LIBS, which effectively addresses the limitations associated with traditional LIBS. The MPT-LIBS technique is evaluated using Cu samples with a low laser pulse energy of 0.55 mJ. A remarkable enhancement factor of over 70 for Cu I 521.82 nm line is demonstrated, while that of Cu I 324.75 nm and 327.40 nm lines exceeding two orders of magnitude. Furthermore, the RSDs of all Cu spectral lines are reduced, especially for Cu I 521.82 nm, which is decreased from 11.48 % to 1.36 %. This indicates a significant improvement in signal stability. Characterization of the tested samples using con-focal microscopy reveals that the ablation area of MPT-LIBS is only 1.36 times of that of LIBS. The limit of detection of Cu I 324.75 nm line is reduced from 52.8 ppk to 319 ppm.

SIGNIFICANCE AND NOVELTY

This study not only offers valuable guidance for improving signal stability and the limit of detection in LIBS, but also demonstrates minimal sample damage due to its low ablation amount. Consequently, the proposed methodology has the potential to significantly advance LIBS technology, expanding its applicability in industrial applications.

Elemental analysis of levitated solid samples by microwave-assisted laser induced breakdown spectroscopy

A novel analysis technique of elements at ambient conditions has been developed. The technique is based on microwave-assisted laser-induced breakdown spectroscopy (MW-LIBS) applied to acoustically levitated samples. The technique has been demonstrated using three solid samples with different properties and compositions. These are ore containing multiple elements (OREAS 520), aluminium oxide (Al3O2) and gypsum (CaSO4·2H2O). The mass of samples was 21 mg, 23 mg, and 55 mg for gypsum, mineral ore, and Al3O2, respectively. Significant signal enhancements were recorded for a variety of elements, using microwave-assisted laser-induced breakdown spectroscopy and levitation (MW-LIBS-Levitation). The signal enhancement for Mn I (403.07 nm), Al I (396.13 nm) and Ca II (393.85 nm) was determined as 123, 46, and 63 times, respectively. Moreover, it was found that MW-LIBS-Levitation minimises the self-absorption of the Ca I (422.67 nm) and Na I (588.99 nm and 589.59 nm) spectral lines. In addition to the signal enhancements, the levitation process produces a spinning motion in the solids with an angular frequency of 7 Hz. This feature benefits laser-based analysis as a fresh sample is introduced at each laser pulse, eliminating the need for the usual mechanical devices. Based on the single-shot analysis, it was found that ∼80% of the laser pulses produced successful MW-LIBS-Levitation detection, confirming an impressive repeatability of the process. This contactless analytical technique can be applied in ambient pressure and temperature conditions with high sensitivity, which can benefit disciplines such as forensics science, isotope analysis, and medical analysis, where the sample availability is often diminutive.

Identification of Dendrobium Using Laser-Induced Breakdown Spectroscopy in Combination with a Multivariate Algorithm Model

Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs.

Crater-Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy

Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate cadmium (Cd) poses a significant environmental pollution risk and potential health hazards to humans. In this study, we employed laser-induced breakdown spectroscopy (LIBS) for the rapid detection of Cd. It is important to note that signal uncertainty can impact the quantification performance of LIBS. Hence, we proposed the crater-spectrum feature fusion method, which comprises ablation crater morphology compensation and characteristic peak ratio correction (CPRC), to explore the feasibility of signal uncertainty reduction. The crater morphology compensation method, namely, adding variables using multiple linear regression (MLR) analysis, decreased the root-mean-square error of the prediction set (RMSEP) from 7.0233 μg/g to 5.4043 μg/g. The prediction results were achieved after CPRC pretreatment using the calibration curve model with an RMSEP of 3.4980 μg/g, a limit of detection of 1.92 μg/g, and a limit of quantification of 6.41 μg/g. The crater-spectrum feature fusion method reached the lowest RMSEP of 2.8556 μg/g, based on a least-squares support vector machine (LSSVM) model. The preliminary results suggest the effectiveness of the crater-spectrum feature fusion method for detecting Cd. Furthermore, this method has the potential to be extended to detect other toxic metals in addition to Cd, which significantly contributes to ensuring the quality and safety of agricultural production.

Minimally destructive laser-induced breakdown spectroscopy of brass assisted by a low-power atmospheric pressure plasma jet

Minimizing sample damage is crucial in laser-induced breakdown spectroscopy (LIBS) for applications involving valuable samples and elemental mapping. In this study, we introduced a low-power atmospheric pressure plasma jet (APPJ) to reduce sample damage by obtaining LIBS signals at significantly lower laser fluences. The proposed technique, APPJ-assisted LIBS (APPJ-LIBS), utilized an argon APPJ to provide seed electrons and enhance the excitation. The APPJ was generated by a 10 kHz alternating current power supply and made contact with the surface of a brass sample at a 30° angle. An infrared nanosecond Nd:YAG laser was focused onto the contacting zone, allowing the resulting laser-induced plasma to evolve within the surrounding APPJ and produce optical emission. The optimized APPJ-LIBS system reduced the laser fluence threshold for spectral detection of the brass sample by 97 %, from 1.43 J/cm2 to 0.05 J/cm2, which represented the lowest laser fluence threshold reported in LIBS studies on copper-based materials. Micrographs of the sample surface showed no visible damage after the APPJ-LIBS measurement at a near-threshold laser fluence and an APPJ input power as low as 6.0 W. Furthermore, gated images showed the plasma evolution in APPJ-LIBS and confirmed the excitation capability of the APPJ for the laser-ablated materials.

Extended total number density compensation for uranium determination by laser-induced breakdown spectroscopy

BACKGROUND

Variations in plasma properties among spectra and samples lead to significant signal uncertainty and matrix effects in laser-induced breakdown spectroscopy (LIBS). To address this issue, direct compensation for plasma property variations is considered highly desirable. However, reliably compensating for the total number density variation is challenging due to inaccurate spectroscopic parameters. For reliable compensation, a total number density compensation (TNDC) method was presented in our recent work, but its applicability is limited to simple samples because of its strict assumptions. In this study, we propose a new pre-processing method, namely extended TNDC (ETNDC), to reduce signal uncertainty and matrix effects in the more complex analytical task of uranium determination.

RESULTS

ETNDC reflects the total number density variation with a weighted combination of spectral lines from all major elements and incorporates temperature and electron density compensation into the weighting coefficients. The method is evaluated on yellow cake samples and combined with regression models for uranium determination. Using the typical validation set and line combination, the mean relative standard deviation (RSD) of U II 417.159 nm in validation samples decreases from 4.92% to 2.27%, and the root mean square error of prediction (RMSEP) and the mean RSD of prediction results decrease from 4.81% to 1.93% and from 1.92% to 1.56%, respectively. Furthermore, the results of 10 validation sets and 216 line combinations show that ETNDC outperforms baseline methods in terms of average performance and robustness.

SIGNIFICANCE

For the first time, ETNDC explicitly addresses the temperature and electron density variations while compensating for the total number density variation, where the inaccurate spectroscopic parameters are avoided by fitting related quantities using concentration information. The method demonstrates effective and robust improvement in signal repeatability and analytical performance in uranium determination, facilitating accurate quantification of the LIBS technique.

Heavy metals high-sensitive detection by laser-induced breakdown spectroscopy based on radial electroosmotic flow-driven enrichment

Heavy metal detection is imperative for human health and environmental sustainability. However, the commonly used liquid sample pretreatment, drying liquid droplet to solid, encounters solute diffusion and nonuniform distribution, thus causing unpromising detection results. Here, we developed a radial electroosmotic flow-driven (REOF) platform to enrich heavy metals in water for high-sensitive detection using laser-induced breakdown spectroscopy (LIBS). Firstly, the electrodes in the substate for REOF were designed and produced by the printed circuit board manufacturer. Different particle deposition patterns were observed by modifying the direction and magnitude of voltage in the evaporated droplets of Cadmium Chloride (CdCl2) on the substrate. Then, the two-dimensional model of the evaporating droplets with REOF was established to verify the experimental phenomenon. The CdCl2 (10-50 mg/L) and Manganese Chloride (MnCl2, 1-8 mg/L) solutions were quantitatively analyzed with the optimized parameter on the substrate by LIBS. The detection limits of Ca and Mn can be reduced by approximately 42 times with REOF substrates by LIBS. Finally, the Mn in the real underground water sample was tested with the REOF substrate by LIBS, and the relative error was 5.5% compared with the results of ICP-MS. The results demonstrated that the REOF can enrich and uniformly distribute the solute on the substrate, and be helpful for the analysis of heavy metals in solution with LIBS.

Automated, real-time material detection during ultrashort pulsed laser machining using laser-induced breakdown spectroscopy, for process tuning, end-pointing, and segmentation

The rapid, high-resolution material processing offered by ultrashort pulsed lasers enables a wide range of micro and nanomachining applications in a variety of disciplines. Complex laser processing jobs conducted on composite samples, require an awareness of the material type that is interacting with laser both for adjustment of the lasering process and for endpointing. This calls for real-time detection of the materials. Several methods such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and energy dispersive X-Ray spectroscopy (EDS) can be used for material characterization. However, these methods often need interruption of the machining process to transfer the sample to another instrument for inspection. Such interruption significantly increases the required time and effort for the machining task, acting as a prohibitive factor for many laser machining applications. Laser induced breakdown spectroscopy (LIBS) is a powerful technique that can be used for material characterization, by analyzing a signal that is generated upon the interaction of laser with matter, and thus, it can be considered as a strong candidate for developing an in-situ characterization method. In this work, we propose a method that uses LIBS in a feedback loop system for real time detection and decision making for adjustment of the lasering process on-the-fly. Further, use of LIBS for automated material segmentation, in the 3D image resulting from consecutive lasering and imaging steps, is showcased.

Interpreting convolutional neural network classifiers applied to laser-induced breakdown optical emission spectra

Laser-induced breakdown spectroscopy (LIBS) is a well-established industrial tool with emerging relevance in high-stakes applications. To achieve its required analytical performance, LIBS is often coupled with advanced pattern-recognition algorithms, including machine learning models. Namely, artificial neural networks (ANNs) have recently become a frequently applied part of LIBS practitioners' toolkit. Nevertheless, ANNs are generally applied in spectroscopy as black-box models, without a real insight into their predictions. Here, we apply various post-hoc interpretation techniques with the aim of understanding the decision-making of convolutional neural networks. Namely, we find synthetic spectra that yield perfect expected classification predictions and denote these spectra class-specific prototype spectra. We investigate the simplest possible convolutional neural network (consisting of a single convolutional and fully connected layers) trained to classify the extended calibration dataset collected for the ChemCam laser-induced breakdown spectroscopy instrument of the Curiosity Mars rover. The trained convolutional neural network predominantly learned meaningful spectroscopic features which correspond to the elements comprising the major oxides found in the calibration targets. In addition, the discrete convolution operation with the learnt filters results in a crude baseline correction.

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.

Accuracy improvement of laser-induced breakdown spectroscopy coal analysis by hybrid transfer learning

Laser-induced breakdown spectroscopy (LIBS) has been applied in coal analysis for advantages such as real-time online analysis. Fine-tuning is a transfer learning method that has been utilized in LIBS to improve accuracy in the target domain with a limited training set by introducing a model trained on a different but related source domain. This research proposed a hybrid transfer learning method (HTr-LIBS) to further enhance the performance of LIBS coal analysis by combining fine-tuning with sample reweighting. A neural network was pre-trained on the source domain and target domain training set. The sample weights of the source domain were iteratively adjusted according to the prediction errors. The pre-trained neural network with optimal sample weights was then fine-tuned using the target domain training set. The proposed method significantly improved the analytical accuracy compared to direct modeling using small training sets. When the training set size increased to 19, the R2P of direct modeling for ash content and volatile matter content were 0.8105 and 0.9440, respectively. HTr-LIBS increased the R2P for ash content and volatile matter content to 0.9029 and 0.9627, respectively. The improvements were more significant and stable than fine-tuning of the source domain model without sample reweighting. The introduction of target domain data during pre-training and the iterative adjustment of sample weights both contributed to the improvements.

Ultrafast Laser Excitation Improves LIBS Performance for the Analysis of Optically Trapped Single Nanoparticles Owing to Characteristic Interaction Mechanisms

Owing to the exceedingly small mass involved, complete elemental characterization of single nanoparticles demands a highly precise control of signal background and noise sources. LIBS has demonstrated remarkable merits for this task, providing a unique tool for the multielemental analysis of particles on the attogram-picogram mass scale. Despite this outstanding sensitivity, the air plasma acting as a heat source for particle dissociation and excitation is a meddling agent, often limiting the acquisition of an accurate sample signature. Although thermal effects associated with ultrashort laser pulses are known to be reduced when compared to the widely used nanosecond pulse duration regime, attempts to improve nanoinspection performance using ultrafast excitation have remained largely unexplored. Herein, picosecond laser pulses are used as a plasma excitation source for the elemental characterization of single nanoparticles isolated within optical traps in air at atmospheric pressure. Results for picosecond excitation of copper particles lead to a mass detection limit of 27 attogram, equivalent to single particles 18 nm in diameter. Temporally and wavelength-resolved plasma imaging reveals unique traits in the mechanism of atomic excitation in the picosecond regime, leading to a deeper understanding of the interactions occurring in single nanoparticle spectroscopy.

In-situ and fast classification of origins of Baishao (Radix Paeoniae Alba) slices based on auto-focus laser-induced breakdown spectroscopy

In this Letter, a rapid origin classification device and method for Baishao (Radix Paeoniae Alba) slices based on auto-focus laser-induced breakdown spectroscopy (LIBS) is proposed. The enhancement of spectral signal intensity and stability through auto-focus was investigated, as were different preprocessing methods, with area normalization (AN) achieving the best results-increasing by 7.74%-but unable to replace the improved spectral signal quality provided by auto-focus. A residual neural network (ResNet) was used as both a classifier and feature extractor, achieving higher classification accuracy than traditional machine learning methods. The effectiveness of auto-focus was elucidated by extracting LIBS features from the last pooling layer output using uniform manifold approximation and projection (UMAP). Our approach demonstrated that auto-focus could efficiently optimize the LIBS signal, providing broad prospects for rapid origin classification of traditional Chinese medicines.

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.

Long-term repeatability improvement using beam intensity distribution for laser-induced breakdown spectroscopy

The relatively low measurement repeatability has long been considered as a major obstacle to the widespread use and commercialization of laser-induced breakdown spectroscopy (LIBS). Although many efforts have been made to improve the signal repeatability in the short term, how to improve the long-term signal repeatability is critical in practical applications and has rarely been studied. Moreover, the mechanisms behind the degradation of long-term repeatability are not fully revealed. This study proposes a new method to improve the long-term repeatability of LIBS measurement, which modifies the spectral intensity based on laser beam intensity distribution. It first pre-processes the beam intensity distribution profiles and spectral intensity. Then the relationship between the relative deviations of beam and spectral intensities is modelled using Partial Least Squares Regression (PLSR). The proposed method was tested on copper and silicon samples, and the spectra and laser beam intensity distribution were recorded for more than thirty days. Day-to-day variations in beam intensity distribution were observed. Such variations can lead to changes in spectral intensity, resulting in degraded signal repeatability. By modifying the spectral intensity, the long-term signal repeatability was improved. Specifically, in terms of day-mean spectral intensity, the valid correction rates were above 70% for both of copper silicon sample in most cases. Long-term RSD decreased from ∼13.5% to ∼4% for copper and decreased from ∼10.7% to 6.5% for silicon sample. These results indicate that the proposed method provides a viable method for improving the long-term repeatability of LIBS measurement.

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.

The Development of Super-Saturated Rebamipide Eye Drops for Enhanced Solubility, Stability, Patient Compliance, and Bioavailability

The present study aimed to develop clear aqueous rebamipide (REB) eye drops to enhance solubility, stability, patient compliance, and bioavailability. For the preparation of a super-saturated 1.5% REB solution, the pH-modification method using NaOH and a hydrophilic polymer was employed. Low-viscosity hydroxypropyl methylcellulose (HPMC 4.5cp) was selected and worked efficiently to suppress REB precipitation at 40 °C for 16 days. The additionally optimized eye drops formulation (F18 and F19) using aminocaproic acid and D-sorbitol as a buffering agent and an osmotic agent, respectively, demonstrated long-term physicochemical stability at 25 °C and 40 °C for 6 months. The hypotonicity (<230 mOsm) for F18 and F19 noticeably extended the stable period, since the pressure causing the REB precipitation was relieved compared to the isotonic. In the rat study, the optimized REB eye drops showed significantly long-lasting pharmacokinetic results, suggesting the possibility of reducing daily administration times and increasing patient compliance (0.50- and 0.83-times lower Cmax and 2.60- and 3.64-times higher exposure in the cornea and aqueous humor). In conclusion, the formulations suggested in the present study are promising candidates and offer enhanced solubility, stability, patient compliance, and bioavailability.

A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis

A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.

Detection of heavy metal ions using laser-induced breakdown spectroscopy combined with filter paper modified with PtAg bimetallic nanoparticles

The rapid and sensitive detection of heavy metal ions is important for environment and human health. Hence, the rapid and sensitive detection of multiple heavy metals simultaneously has become a critical issue. Here, we propose a method based on laser-induced breakdown spectroscopy (LIBS) combined with filter paper modified with PtAg bimetallic nanoparticles (BNPs) (LIBS-FP-PtAgBNPs) for the ultrasensitive detection of Hg²⁺, Cr³⁺, and Pb²⁺. The PtAgBNPs-modified filter paper was used to efficiently and specifically adsorb Hg, Cr, and Pb, and LIBS was used to detect the Hg, Cr, and Pb simultaneously. The limits of detection for Hg, Cr, and Pb were 0.5 μg/L (2.5 nM), 8 μg/L (0.15 μM), and 2 μg/L (9 nM), respectively. Furthermore, this method was successfully applied to determine the concentrations of Hg, Cr, and Pb in real spiked water samples. Compared with other methods based on nanoparticle sensing, LIBS-FP-PtAgBNPs is simpler to use and can achieve highly efficient enrichment, rapid separation, and sensitive detection of heavy metal ions. The optimal detections of Hg, Cr, and Pb were achieved in the pH range of 1-6. The developed method provides a new avenue to realize the rapid and sensitive detection of trace heavy metals in the environment.

A Versatile Method for Quantitative Analysis of Total Iron Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy

Focus in quality assessment of iron ore is the content of total iron (TFe). Laser-induced breakdown spectroscopy (LIBS) technology possesses the merits of rapid, in situ, real-time multielement analysis for iron ore, but its application to quantitative TFe content is subject to interference of the iron matrix effect and the lack of suitable data mining tools. Here, a new method of LIBS-based variable importance back propagation artificial neural network (VI-BP-ANN) for quantitative TFe content in iron ore was first proposed. After the LIBS spectra of 80 representative iron samples were obtained, random forest (RF) was optimized by out-of-bag (OOB) error and then used to measure and rank variable importance. The variable importance thresholds and the number of neurons were optimized with five-fold cross-validation (CV) with correlation coefficient (R²) and root mean square error (RMSE). With using only 1.40% of full spectral variables to construct BP-ANN model, the resulted R², the root mean squared error of prediction (RMSEP) and the modeling time of the final VI-BP-ANN model was 0.9450, 0.3174 wt%, and 24 s, respectively. Compared with full spectrum-based model, for example, BP-ANN, RF, support vector machine (SVM), and PLS and VI-RF model, the VI-BP-ANN model reduced overfitting and obtained the highest R² and the lowest RMSE both for calibration and prediction. Meanwhile, the characteristics of variables selected by VI were analyzed. In addition to the elemental emission lines of Ca, Al, Na, K, Mn, Si, Mg, Ti, Zr, and Li, partial spectral baselines of 540-610 nm and 820-970 nm were also selected as characteristic variables, which indicated that VI can take into full consideration the elemental interactions and the spectral baselines. Our approach shows that LIBS combined with VI-BP-ANN is able to quantify TFe content rapidly and accurately in iron ore.

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.

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.

Model transfer method based on piecewise direct standardization in laser-induced-breakdown spectroscopy

A large number of certified samples are usually required to build models in the quantitative analysis of complicated matrices in laser-induced-breakdown spectroscopy (LIBS). Because of differences among instruments, including excitation and collection efficiencies, a quantitative model made on one instrument is difficult to apply directly to other instruments. Each instrument requires a large number of samples to model, which is very labor intensive and will hinder the rapid application of the LIBS technique. To eliminate the differences in spectral data from different instruments and reduce the cost of building new models, a piecewise direct standardization method combined with partial least squares (PLS_PDS) is studied in this work. Two portable LIBS instruments with the same configuration are used to obtain spectral data, one of which is called a master instrument because its calibration model is directly built on a large number of labeled samples, and the other of which is called a slave instrument because its model is obtained from the master instrument. The PLS_PDS method is used to build a transfer function of spectra between the master instrument and slave instrument to reduce the spectral difference between two instruments, and thus one calibration model can adapt to different instruments. Results show that for multiple elemental analyses of aluminium alloy samples, the number of samples required for slave modeling was reduced from 51 to 14 after model transferring by PLS_PDS, and the quantitative performance of the slave instrument was close to that of the master instrument. Therefore, the model transfer method can obviously reduce the sample number of building models for slave instruments, and it will be beneficial to advance the application of LIBS.

Electrolyte Analysis in Blood Serum by Laser-Induced Breakdown Spectroscopy Using a Portable Laser

The fast and reliable analysis of electrolytes such as K, Na, Ca in human blood serum has become an indispensable tool for diagnosing and preventing diseases. Laser-induced breakdown spectroscopy (LIBS) has been demonstrated as a powerful analytical technique on elements. To apply LIBS to the quantitative analysis of electrolyte elements in real time, a self-developed portable laser was used to measure blood serum samples supported by glass slides and filter paper in this work. The partial least squares regression (PLSR) method was employed for predicting the concentrations of K, Na, Ca from serum LIBS spectra. Great prediction accuracies with excellent linearity were obtained for the serum samples, both on glass slides and filter paper. For blood serum on glass slides, the prediction accuracies for K, Na, Ca were 1.45%, 0.61% and 3.80%. Moreover, for blood serum on filter paper, the corresponding prediction accuracies were 7.47%, 1.56% and 0.52%. The results show that LIBS using a portable laser with the assistance of PLSR can be used for accurate quantitative analysis of elements in blood serum in real time. This work reveals that the handheld LIBS instruments will be an excellent tool for real-time clinical practice.

Rapid quantitative analysis of potassium in soil based on direct-focused laser ablation-laser induced breakdown spectroscopy

A fast quantitative analysis method of soil potassium based on direct-focused laser ablation-laser induced breakdown spectroscopy (direct-focused LA-LIBS) was proposed and tested. A high single-pulse energy laser (200 mJ/pulse) beam was focused on the aerosols near the focus of the 10 kHz fiber laser to generate plasma spectra, and the analytical capability of the direct-focused LA-LIBS system was compared with traditional LIBS system using a high single-pulse energy laser (SP-LIBS). The result showed that for moist soil samples the data stability of the direct-focused LA-LIBS method was significantly improved and the R2 factor of the calibration curve improved from 0.64 to 0.93, the limit of detection improved from 159.2 μg/g to 140.9 μg/g. Three random soil samples from different areas of Beijing suburbs were analyzed by the direct-focused LA-LIBS method, and the results were consistent with AAS. The direct-focused LA-LIBS method proposed is different from the traditional double-pulse technology and laser ablation-assisted technology because it not only does not need carrier gas, but also can overcome the matrix differences better, especially the influence of moisture, which provides a new idea for the rapid detection of nutrient elements in field soils.

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.

Study on Microdamage Quantitative Analysis of Cd and Pb in Leaves by Laser Induced Breakdown Spectroscopy

Recent years, research on the detection of heavy metals in Traditional Chinese Medicine (TCM) by laser induced breakdown spectroscopy (LIBS) have gradually increased. Current main methods of establishing calibration curve are based on grounding and pelleting of the tested samples. Although compared to digested samples, grounding and pelleting of the sample is already quite simple, it cannot fully reflect the advantages of LIBS: rapid analysis, and, also, the uneven distribution of heavy metals in the TCM is ignored. In order to avoid grinding and pelleting sample to be tested, and to achieve microdamage quantitative analysis by LIBS, this article presents a new method for establishing calibration curve. The experiment in this paper based on a study with Cd and Pb in leaves of laurel. The preparation of calibration samples and the establishment of calibration methods for microdamage quantitative analysis were presented, which proved the feasibility of microdamage quantitative analysis by LIBS. The square of the linear relationship coefficient R of Pb was higher than 0.82. This method provides a guiding method for the rapid quantitative analysis of heavy metals in TCM by LIBS.

Estimation of Grain Size in Randomly Packed Granular Material Using Laser-Induced Breakdown Spectroscopy

Grain size is one of the most important physical parameters for randomly packed granular (RPG) materials. Its estimation, especially in situ, plays a key role in many natural and industrial processes. Here, the application of laser-induced breakdown spectroscopy (LIBS) was investigated experimentally to estimate the grain size in RPG materials. The experiment was performed by taking sieved copper microspheres with discrete median diameters ranging from 53 to 357 μm as examples and by measuring the plasma emissions induced by 1064 nm laser pulses with a duration of 7 ns in an air environment. It was found that the plasma emission measurements were successful in estimating the grain median diameter via monitoring the variations in plasma temperature (electron density) at the range of median diameter below (above) a critical value. In addition, it was demonstrated that, when plasma temperature serves as an indicator of grain size, the intensity ratio between two spectral lines from different upper energy levels of the same emitting species can be used as an alternative indicator with higher sensitivity. The results show the potential of using LIBS for in situ estimation of grain size in RPG materials for the first time.

Composition analysis of ceramic raw materials using laser-induced breakdown spectroscopy and autoencoder neural network

In the ceramic production process, the content of Si, Al, Mg, Fe, Ti and other elements in the ceramic raw materials has an important impact on the quality of the ceramic products. Exploring a method that can quickly and accurately analyze the content of key elements in ceramic raw materials is of great significance to improve the quality of ceramic products. In this work, laser-induced breakdown spectroscopy (LIBS) is used for rapid analysis of ceramic raw materials. The chemical element composition and content of ceramic raw materials are quite different, which leads to serious matrix effects. Building an artificial neural network model is an effective way to solve the complex matrix effects, but model training can easily lead to overfitting due to the high number of spectral features and the limited number of samples. In order to solve this problem, we propose a feature extraction method that combines the linear regression (LR) and the sparse and under-complete autoencoder (SUAC) neural network. This LR + SUAC method performs nonlinear feature extraction and dimension reduction on high-dimensional spectral data. The spectral data dimension is reduced from 8188 to 100 through the LR layer, and further reduced to 32 through the SUAC encoding layer. Further, a quantitative analysis model for the elemental composition of ceramic raw materials is established by the combination of LR + SUAC and Back Propagation Neural Network (BPNN). Since the input data dimension and redundant information are greatly reduced by LR + SUAC, the overfitting problem of BPNN is greatly reduced. Experiment results showed that the LR + SUAC + BPNN method obtained the best quantitative analysis performance compared with several other methods in the cross-validation process.

Classification of steel using laser-induced breakdown spectroscopy combined with deep belief network

The identification of steels is a crucial step in the process of recycling and reusing steel waste. Laser-induced breakdown spectroscopy (LIBS) coupled with machine learning is a convenient method to classify the types of materials. LIBS can generate characteristic spectra of various samples as input variable for steel classification in real time. However, the performance of classification model is limited to the complex input due to similar chemical composition in samples and nonlinearity problems between spectral intensities and elemental concentrations. In this study, we developed a method of LIBS coupled with deep belief network (DBN), which is suitable to deal with a nonlinear problem, to classify 13 brands of special steels. The performance of the training and validation sets were used as the standard to optimize the structure of DBN. For different input, such as the intensities of full-spectra signals and characteristic spectra lines, the accuracies of the optimized DBN model in the training, validation, and test set are all over 98%. Moreover, compared with the self-organizing maps, linear discriminant analysis (LDA), k-nearest neighbor (KNN) and back-propagation artificial neural networks (BPANN), the result of the test set showed that the optimized DBN model performed second best (98.46%) in all methods using characteristic spectra lines as input. The test accuracy of the DBN model could reach 100% and the maximum accuracy of other methods ranged from 62.31% to 96.16% using full-spectra signals as input. This study demonstrates that DBN can extract representative feature information from high-dimensional input, and that LIBS coupled with DBN has great potential for steel classification.

Beam-crossing configuration to control plasma position, improve spatial resolution, and enhance emissions in single-pulse, laser-induced breakdown spectroscopy in gases

We report a relatively simple configuration of laser-induced breakdown spectroscopy (LIBS) that is suitable for gas flow diagnostics with increased spatial resolution, signal intensity, and stability. In this optical configuration, two laser beams are generated by splitting a single laser beam, and then they are focused and crossed orthogonally at the detection volume from two different optical paths. Different from dual-pulse LIBS, this LIBS configuration uses only one laser source, and thus is of relatively low cost. Several advantages were found for this simple beam-crossing LIBS when it was demonstrated in air in the present work, particularly on signal enhancement and stabilization, confining plasma volume, and controlling plasma position. Both of the latter two advantages are relevant to spatial resolution improvement of LIBS in gases, which has rarely been discussed in previous reports. An enhancement factor of 2 was found for atomic hydrogen, nitrogen, and oxygen emissions with respect to conventional LIBS. Another advantage is that the position of breakdown can be precisely controlled through adjustment of the propagation of the two beams, also resulting in smaller plasma volume and stable emission intensity. Furthermore, the technique is moderately tolerant to dust particles neutrally present in the environment, avoiding the spark occurring at a position out of the detection volume. Beyond LIBS, the new configuration has other potential applications, e.g., laser-induced ignition, which is also briefly discussed.

Improvement of sample discrimination using laser-induced breakdown spectroscopy with multiple-setting spectra

Laser-induced breakdown spectroscopy (LIBS) is a promising multi-elemental analysis technique and has the advantages of rapidness and minimal sample preparation. In traditional LIBS measurement, sample spectra are generally collected based on a single set of fixed experimental parameters, such as laser energy and delay time. When samples have the same main components and similar component concentrations, the difference in their spectral intensities becomes less obvious. This can lower the sensitivity of LIBS measurement and pose a threat to the accuracy and robustness of LIBS qualitative analysis. In this work, we propose a new method to increase the spectral difference between similar samples, namely multiple-setting spectra. For each sample, it adopts different sets of experimental parameters and obtains a group of spectra to increase the fingerprint spectral information. The effectiveness of the proposed method is theoretically verified and then tested on 11 similar coal samples. Specifically, the sample spectra were collected with different laser energy and delay time, and processed by principal component analysis (PCA) and Davies-Bouldin index (DBI). The results show that the use of multiple-settings spectra can significantly improve the sample discrimination accuracy from 81.8% to 96.4%. In addition, the proposed method can maintain the efficiency and cost of LIBS measurement.

Accuracy improvement in plastics classification by laser-induced breakdown spectroscopy based on a residual network

The whole ecosystem is suffering from serious plastic pollution. Automatic and accurate classification is an essential process in plastic effective recycle. In this work, we proposed an accurate approach for plastics classification using a residual network based on laser-induced breakdown spectroscopy (LIBS). To increasing efficiency, the LIBS spectral data were compressed by peak searching algorithm based on continuous wavelet, then were transformed to characteristic images for training and validation of the residual network. Acrylonitrile butadiene styrene (ABS), polyamide (PA), polymethyl methacrylate (PMMA), and polyvinyl chloride (PVC) from 13 manufacturers were used. The accuracy of the proposed method in few-shot learning was evaluated. The results show that when the number of training image data was 1, the verification accuracy of classification by plastic type under residual network still kept 100%, which was much higher than conventional classification algorithms (BP, kNN and SVM). Furthermore, the training and testing data were separated from different manufacturers to evaluate the anti-interference properties of the proposed method from various additives in plastics, where 73.34% accuracy was obtained. To demonstrate the superiority of classification accuracy in the proposed method, all the evaluations were also implemented by using conventional classification algorithm (kNN, BP, SVM algorithm). The results confirmed that the residual network has a significantly higher accuracy in plastics classification and shows great potential in plastic recycle industries for pollution mitigation.

Evaluation of femtosecond laser-induced breakdown spectroscopy system as an offline coal analyzer

Developments in femtosecond laser induced breakdown spectroscopy (fs-LIBS) applications during the last two decades have further centered on innovative métier tie-in to the advantageous properties of femtosecond laser ablation (fs-LA) introduced into LIBS. Yet, for industrially-oriented application like coal analysis, no research has exposed to view the analytical capabilities of fs-LA in enhancing the physical processes of coal ablation and the impact into quantitative correlation of spectra and data modeling. In a huge coal market, fast and accurate analysis of coal property is eminently important for coal pricing, combustion optimization, and pollution reduction. Moreover, there is a thirst need of precision standardization for coal analyzers in use. In this letter, the analytical performance of a one-box femtosecond laser system is evaluated relative to an industrially applied coal analyzer based on five objectives/measures: spectral correlation, relative sensitivity factors, craters topology, plasma parameters, and repeatability. Despite high-threshold operation parameters of the fs system, competitive results are achieved compared to the optimized analytical conditions of the ns-coal analyzer. Studies targeting the in-field optimization of fs-LIBS systems for coal analysis can potentially provide insights into fs-plasma hydrodynamics under harsh conditions, instrumental customization, and pave the way for a competitive next-generation of coal analyzers.