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

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen's kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model's values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

Rapid Determination of Cr3+ and Mn2+ in Water Using Laser-Induced Breakdown Spectroscopy Combined with Filter Paper Modified with Gold Nanoclusters.

Excessive emissions of heavy metals not only cause environmental pollution but also pose a direct threat to human health. Therefore, rapid and accurate detection of heavy metals in the environment is of great significance. Herein, we propose a method based on laser-induced breakdown spectroscopy (LIBS) combined with filter paper modified with bovine serum albumin-protected gold nanoclusters (LIBS-FP-AuNCs) for the rapid and sensitive detection of Cr3+ and Mn2+. The filter paper modified with AuNCs was used to selectively enrich Cr3+ and Mn2+. Combined with the multi-element detection capability of LIBS, this method achieved the simultaneous rapid detection of Cr3+ and Mn2+. Both elements showed linear ranges for concentrations of 10-1000 µg L-1, with limits of detection of 7.5 and 9.0 µg L-1 for Cr3+ and Mn2+, respectively. This method was successfully applied to the determination of Cr3+ and Mn2+ in real water samples, with satisfactory recoveries ranging from 94.6% to 105.1%. This method has potential application in the analysis of heavy metal pollution.

Quasi-Non-Destructive Estimation of Concrete Compression Strength Using Laser-Induced Breakdown Spectroscopy and Multivariate Analysis.

Micro- and non-destructive methods of estimating compressive strength are useful for diagnosing the degradation of reinforced structures. The velocity of waves propagating through concrete can be measured using conventional non-destructive methods; however, the propagation path of waves varies depending on the distribution of coarse aggregate, resulting in variations in velocity at different measurement points. To address this issue, a method based on laser-induced breakdown spectroscopy and multivariate analysis was developed in this study for estimating the compressive strength of concrete non-destructively, ensuring the non-influence of the coarse aggregate spatial distribution. The method is based on the correlation between the emission intensity of the spectrum and the hardness of the object to be measured. Principal component analysis and partial least squares regression (PLSR) were used to extract the mortar spectrum, which determines the compressive strength of concrete, from a mixture of aggregate and mortar spectra. The compressive strength estimated based on the proposed method was consistent with the values obtained from the compressive strength test, which indicates the possibility of using multivariable analysis to estimate the compressive strength of concrete. Furthermore, the proposed method enabled on-site measurements through a simple experimental setup and insensitivity to spectral noise offered by PLSR.

Rapid quantitative analysis of coal composition using laser-induced breakdown spectroscopy coupled with random forest algorithm.

Coal is the primary energy source in China, widely used in energy production, industrial processes, and chemical engineering. Due to the complexity and diversity of coal quality, there is an urgent need for new technologies to achieve rapid and accurate detection and analysis of coal, aiming to improve coal resource utilization and reduce pollutant emissions. This study proposes a rapid quantitative analysis of coal using laser-induced breakdown spectroscopy combined with the random forest algorithm. Firstly, a Q-switched Nd: YAG laser at 1064 nm was employed to ablate coal samples, generating plasma, and spectral data were collected using a spectrometer. Secondly, the study explores the impact of different parameters in the preprocessing method (wavelet transform) on the predictive performance of the random forest model. It identifies elements related to coal ash content and calorific value along with their spectral information. Subsequently, to further validate the predictive performance of the model, a comparison is made with models established using support vector machine, artificial neural network, and partial least squares. Finally, under optimal parameters for spectral information preprocessing (wavelet transform with Db4 as the base function and 3 decomposition levels), a model combining wavelet transform with Random Forest is established to predict and analyze the ash content and calorific value of coal. The results demonstrate that the Wavelet Transform-Random Forest model exhibits excellent predictive performance (coal ash content: R2 = 0.9470, RMSECV = 4.8594, RMSEP = 4.8450; coal calorific value: R2 = 0.9485, RMSECV = 1.5996, RMSEP = 1.5949). Therefore, laser-induced breakdown spectroscopy combined with the random forest algorithm is an effective method for rapid and accurate detection and analysis of coal. The predicted coal composition values show high accuracy, providing insights and methods for coal composition monitoring and analysis.

Decision-level data fusion based on laser-induced breakdown and Raman spectroscopy: A study of bimodal spectroscopy for diagnosis of lung cancer at different stages.

Lung cancer staging is crucial for personalized treatment and improved prognosis. We propose a novel bimodal diagnostic approach that integrates LIBS and Raman technologies into a single platform, enabling comprehensive tissue elemental and molecular analysis. This strategy identifies critical staging elements and molecular marker signatures of lung tumors. LIBS detects concentration patterns of elemental lines including Mg (I), Mg (II), Ca (I), Ca (II), Fe (I), and Cu (II). Concurrently, Raman spectroscopy identifies changes in molecular content, such as phenylalanine (1033 cm-1), tyrosine (1174 cm-1), tryptophan (1207 cm-1), amide III (1267 cm-1), and proteins (1126 cm-1 and 1447 cm-1), among others. The bimodal information is fused using a decision-level Bayesian fusion model, significantly enhancing the performance of the convolutional neural network architecture in classification algorithms, with an accuracy of 99.17 %, sensitivity of 99.17 %, and specificity of 99.88 %. This study provides a powerful new tool for the accurate staging and diagnosis of lung tumors.

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique.

BACKGROUND: Laser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem. It is clear that a method to enhance the inherent transferability of spectral original models is urgently needed. RESULTS: We proposed an innovative fusion methodology, named laser-induced breakdown spectroscopy fusion laser-induced plasma acoustic spectroscopy (LIBS-LIPAS), to enhance the transferability of support vector machine (SVM) original models across LIBS systems with varying laser beams. The methodology was demonstrated using nickel-based high-temperature alloy samples. Here, the area-full width at half maximum (AFCEI) Composite Evaluation Index was proposed for extracting critical features from LIBS. Further enhancing the transferability of the model, the laser-induced plasma acoustic signal was transformed from the time domain to the frequency domain. Subsequently, the feature-level fusion method was employed to improve the classification accuracy of the transferred LIBS system to 97.8 %. A decision-level fusion approach (amalgamating LIBS, LIPAS, and feature-level fusion models) achieved an exemplary accuracy of 99 %. Finally, the adaptability of the method was demonstrated using titanium alloy samples. SIGNIFICANCE AND NOVELTY: In this work, based on plasma radiation models, we simultaneously captured LIBS and LIPAS, and proposed the fusion of these two distinct yet origin-consistent signals, significantly enhancing the transferability of the LIBS original model. The methodology proposed holds significant potential to advance LIBS technology and broaden its applicability in analytical chemistry research and industrial applications.

Real-time in situ detection of petroleum hydrocarbon pollution in soils via a novel optical methodology.

Petroleum hydrocarbon (PHC) contamination in soils is considered one of the most serious problems currently, of which the detection and identification is a fairly significant but challenging work. Conventional methods to do such work usually need complex sample pretreatment, consume much time and fail to do the in-situ detection. This paper set out to create a novel systematic methodology to realize the goals accurately and efficiently. Based on laser-induced breakdown spectroscopy (LIBS) and self-improved machine learning methods, the innovative methodology only uses extremely simple devices to do the real-time in situ detection and identification work and even realize the quantitative analysis of pollution level accurately. In the study, clean soils mixed with petroleum were served as polluted samples, clean soils to be the blank group for comparison. Based on the elemental information from the spectra obtained by LIBS, machine learning methods were improved and helped optimized the algorithm to identify the PHC polluted soil samples for the first time. Furthermore, a novel model was designed to perform the quantitative analysis of the concentration of PHC pollution in soils, which can be applied to detect the degree of PHC contamination in soils accurately. Finally, the harmful volatile component of the PHC polluted soils was also successfully and identified despite its extremely minimal content in the air. The newly-designed methodology is novel and efficient, which has extensive application prospect in the real-time in situ detection of petroleum hydrocarbon pollution.

Simple and sensitive determination of Cr (III), Cu (II) and Pb (II) in tea infusions using AgNPs-modified resin combined with laser-induced breakdown spectroscopy.

The detection of heavy metals in tea infusions is important because of the potential health risks associated with their consumption. Existing highly sensitive detection methods pose challenges because they are complicated and time-consuming. In this study, we developed an innovative and simple method using Ag nanoparticles-modified resin (AgNPs-MR) for pre-enrichment prior to laser-induced breakdown spectroscopy for the simultaneous analysis of Cr (III), Cu (II), and Pb (II) in tea infusions. Signal enhancement using AgNPs-MR resulted in amplification with limits of detection of 0.22 µg L-1 for Cr (III), 0.33 µg L-1 for Cu (II), and 1.25 µg L-1 for Pb (II). Quantitative analyses of these ions in infusions of black tea from various brands yielded recoveries ranging from 83.3% to 114.5%. This method is effective as a direct and highly sensitive technique for precisely quantifying trace concentrations of heavy metals in tea infusions.

Identification of Relevant Spectral Ranges in Laser-Induced Breakdown Spectroscopy Imaging Using the Fourier Space.

Laser-induced breakdown spectroscopy (LIBS) imaging has now a well-established position in the subject of spectral imaging, leveraging multi-element detection capabilities and fast acquisition rates to support applications both at academic and technological levels. In current applications, the standard processing pipeline to explore LIBS imaging data sets revolves around identifying an element that is suspected to exist within the sample and generating maps based on its characteristic emission lines. Such an approach requires some previous expert knowledge both on the technique and on the sample side, which hinders a wider and more transparent accessibility of the LIBS imaging technique by non-specialists. To address this issue, techniques based on visual analysis or peak finding algorithms are applied on the average or maximum spectrum, and may be employed for automatically identifying relevant spectral regions. Yet, maps containing relevant information may often be discarded due to low signal-to-noise ratios or interference with other elements. In this context, this work presents an agnostic processing pipeline based on a spatial information ratio metric that is computed in the Fourier space for each wavelength and that allows for the identification of relevant spectral ranges in LIBS. The results suggest a more robust and streamlined approach to feature extraction in LIBS imaging compared with traditional inspection of the spectra, which can introduce novel opportunities not only for spectral data analysis but also in the field of data compression.

Real-Time Monitoring of Thermal Phenomena during Femtosecond Ablation of Bone Tissue for Process Control.

Femtosecond (fs) laser technology is currently being considered in innovative fields such as osteotomy and treatment of hard tissue thanks to the achievable high resolution and ability to prevent tissue damage. In a previous study, suitable process parameters were obtained to achieve competitive ablation rates on pork femur processing. Nevertheless, a better control of thermal accumulation in the tissue during laser ablation could further improve the postoperative regeneration of the treated bone compared with conventional procedures and push forward the exploitation of such technology. This study presents methods for real time analyses of bone tissue temperature and composition during fs laser ablation and highlights the importance of implementing an efficient cooling method of bone tissue in order to achieve optimized results. Results show that it is possible to achieve a larger process window for bone tissue ablation where bone tissue temperature remains within the protein denaturation temperature in water-based processing environment. This is a key outcome towards a clinical exploitation of the presented technology, where higher process throughputs are necessary. The effects of process parameters and environments on bone tissue were confirmed by LIBS technique, which proved to be an efficient method by which to record real-time variation of bone tissue composition during laser irradiation.

Effect of Femtosecond Ultraviolet and Infrared Laser Wavelength on Plasma Characteristics of Metals, Ceramics and Glass Samples Using Femtosecond Laser-Induced Breakdown Spectroscopy.

In this work, we present studies on the effect of laser wavelengths on the laser-induced plasma characterization using a femtosecond (fs) ytterbium-doped potassium-gadolinium tungstate (Yb:KGW) laser. Plasma plumes of copper, steel, ceramics, and glass samples were induced using a multiple shot of 200 fs laser pulses with 1030 nm and 343 nm wavelengths at fixed laser fluence (10.5J/cm2) and analyzed using the laser-induced breakdown spectroscopy (LIBS) technique. Time-resolved fs-LIBS measurements were performed on the same set of samples and under the same experimental conditions. For the calculation of plasma parameters, the set of spectral lines of Cu(I) (for copper sample), Fe(I) (for steel sample), and Ca(I), K(I) (for glass and ceramics samples) were observed. The plasma electron temperature and density were evaluated from the Boltzmann plots and Stark-broadening profiles of the plasma spectral lines, assuming the local thermodynamic equilibrium condition. Time-resolved plasma temperature and electron density for fs-LIBS using ultraviolet (UV) and infrared (IR) laser wavelengths were analyzed and no significant dependence on fs laser wavelength was observed for any of the samples. However, for all samples the signal-to-noise ratio (SNR) significantly increased using UV laser radiation: copper (∼100%), steel (∼300%), glass (∼400%), and ceramics (∼40%). Therefore, by using a fs UV laser wavelength for laser-induced breakdown spectroscopy experiments, for certain materials the SNR and at the same time the limit of detection can be significantly enhanced.

Research on a new multiple-screening method for laser-induced plasma spectroscopy utilizing Lorentz.

In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566 % and 0.1024 %. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

Sensitivity and stability improvement on slippery surface-aggregated substrate for trace heavy metals detection using NELIBS.

The sensitive and stable detection of trace heavy metals in liquid is crucial given its profound impact on various aspects of human life. Currently, nanoparticle-enhanced laser-induced breakdown spectroscopy (NELIBS) with dried droplet method (DDM) is widely applied for heavy metals detection. Nevertheless, the coffee ring effect (CRE) in DDM affects the stability, accuracy, and sensitivity of NELIBS. Here, we developed a slippery surface-aggregated substrate (SS substrate) to suppress the CRE and enrich analytes, and form a plasmonic platform for NELIBS detection. The SS substrate was prepared by infiltrating perfluorinated lubricant into the pores of PTFE membrane. The droplet, with targeted elements and gold nanoparticles, was dried on the SS substate to form the plasmonic platform for NELIBS analysis. Then, trace heavy metal elements copper (Cu) and manganese (Mn) were analyzed by NELIBS. The results of Cu (RSD = 5.60%, LoD = 3.72 µg/L) and Mn (RSD = 7.42%, LoD = 6.37 µg/L), illustrated the CRE suppression and analytes enrichment by the SS substrate. The results verified the realization of stable, accurate and sensitive NELIBS detection. And the LoDs succeeded to reach the standard limit of China (GB/T 14848-2017). Furthermore, the results for groundwater detection (relative error: 5.92% (Cu) and 4.74% (Mn)), comparing NELIBS and inductively coupled plasma mass spectrometry (ICP-MS), validated the feasibility of the SS substrate in practical applications. In summary, the SS substrate exhibits immense potential for practical application such as water quality detection and supervision.

Comparison of single and double pulse laser-induced breakdown spectroscopy for the detection of biomolecules tagged with photon-upconversion nanoparticles.

BACKGROUND: Laser-induced breakdown spectroscopy (LIBS) is a well-recognized analytical technique used for elemental analysis. This method is gaining considerable attention also in biological applications thanks to its ability for spatial mapping and elemental imaging. The implementation of LIBS in the biomedical field is based on the detection of metals or other elements that either naturally occur in the samples or are present artificially. The artificial implementation of nanoparticle labels (Tag-LIBS) enables the use of LIBS as a readout technique for immunochemical assays. However, one of the biggest challenges for LIBS to meet immunoassay readout standards is its sensitivity. RESULTS: This paper focuses on the improvement of LIBS sensitivity for the readout of nanoparticle-based immunoassays. First, the LIBS setup was optimized on photon-upconversion nanoparticle (UCNP) droplets deposited on the microtiter plate wells. Two collection optics systems were compared, with single pulse (SP) and collinear double pulse (DP) LIBS arrangements. By deploying the second laser pulse, the sensitivity was improved up to 30 times. The optimized SP and DP setups were then employed for the indirect detection of human serum albumin based on immunoassay with UCNP-based labels. Compared to our previous LIBS study, the detection limit was enhanced by two orders of magnitude, from 10 ng mL-1 to 0.29 ng mL-1. In addition, two other immunochemical methods were used for reference, based on the readout of upconversion luminescence of UCNPs and absorbance measurement with enzyme labels. Finally, the selectivity of the assay was tested and the practical potential of Tag-LIBS was demonstrated by the successful analysis of urine samples. SIGNIFICANCE AND NOVELTY: In this work, we improved the sensitivity of the Tag-LIBS method by combining new labels based on UCNPs with the improved collection optics and collinear DP configuration. In the instrumental setup optimization, the DP LIBS showed better sensitivity and signal-to-noise ratio than SP. The optimizations allowed the LIBS readout to surpass the sensitivity of enzyme immunoassay, approaching the qualities of upconversion luminescence readout, which is nowadays a state-of-the-art readout technique.

Self-Calibrated Laser-Induced Breakdown Spectroscopy for the Quantitative Elemental Analysis of Suspended Volcanic Ash.

Real-time analysis of fine ash in volcanic plumes, which represent magma fragments expelled from the crater during explosive eruptions, is a valuable tool for volcano monitoring and hazard assessment. To obtain the chemical characterization of the juvenile pyroclastic material emitted in volcanic plumes, many analytical techniques can be used. Among them, laser-induced breakdown spectroscopy (LIBS) is the one that can most easily be adapted to advanced applications in extreme environments. In this paper, LIBS experiments based on self-calibrated approaches are used to determine the elemental composition of suspended volcanic ash. To simulate the conditions of dispersed volcanic ash in the atmosphere, different sizes of volcanic ash samples are suspended in the air by laser-induced shockwaves in a dedicated chamber, and a parametric study is carried out to establish the optimal experimental conditions for recording usable plasma emission spectra for each ash size. The quantitative analysis is performed using a self-calibrated analytical method, including calibration-free LIBS, which is based on the calculation of the spectral radiance of a uniform plasma in local thermodynamic equilibrium. The method accounts intrinsically for self-absorption since it modifies the intensity of spectral lines and thus leads to an underestimation of the elemental fraction. An intensity calibration of the spectra based on the measurements of Fe lines intensities was also used in this work to deduce the apparatus response from the spectrum itself and avoid the use of standard calibration lamps. Results demonstrate the potential of real-time measurements of elemental fractions in volcanic ash with good agreement with the literature composition.

Assessment of the Electrolyte Heterogeneity of Tissues in Mandibular Bone-Infiltrating Head and Neck Cancer Using Laser-Induced Breakdown Spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was recently introduced as a rapid bone analysis technique in bone-infiltrating head and neck cancers. Research efforts on laser surgery systems with controlled tissue feedback are currently limited to animal specimens and the use of nontumorous tissues. Accordingly, this study aimed to characterize the electrolyte composition of tissues in human mandibular bone-infiltrating head and neck cancer. Mandible cross-sections from 12 patients with bone-invasive head and neck cancers were natively investigated with LIBS. Representative LIBS spectra (n = 3049) of the inferior alveolar nerve, fibrosis, tumor stroma, and cell-rich tumor areas were acquired and histologically validated. Tissue-specific differences in the LIBS spectra were determined by receiver operating characteristics analysis and visualized by principal component analysis. The electrolyte emission values of calcium (Ca) and potassium (K) significantly (p < 0.0001) differed in fibrosis, nerve tissue, tumor stroma, and cell-rich tumor areas. Based on the intracellular detection of Ca and K, LIBS ensures the discrimination between the inferior alveolar nerve and cell-rich tumor tissue with a sensitivity of ≥95.2% and a specificity of ≥87.2%. The heterogeneity of electrolyte emission values within tumorous and nontumorous tissue areas enables LIBS-based tissue recognition in mandibular bone-infiltrating head and neck cancer.

Rapid and ultra-sensitive trace metals detection of water by partial Leidenfrost superhydrophobic array surface enhanced laser-induced breakdown spectroscopy.

The rapid and ultra-sensitive detection of trace elements in liquid is a primary concern for researchers. In this study, a partial Leidenfrost effect superhydrophobic (PLSHB) array surface was used for rapid in situ evaporation enrichment of sample droplets. Within 4 min, a 50 µL droplet sample was completely evaporated, resulting in all solutes in it being concentrated within a circular range measuring approximately 350 µm in diameter, without the formation of a coffee ring structure. The limits of detection for six metals (Pb, Ba, Be, Mn, Cr, Cu) in water were determined to be as follows: 0.82 µgL-1, 0.27 µgL-1, 0.033 µgL-1, 0.136 µgL-1, 0.241 µgL-1, and 0.083 µgL-1. Furthermore, laser-induced breakdown spectroscopy (LIBS) was employed to detect the enriched solutes from ten liquid samples with identical concentrations on the PLSHB array surface; these measurements exhibited a relative standard deviation (RSD) of only 3.7%. Spike experiments involving the addition of the aforementioned six metals into drinking water demonstrated recovery rates ranging from 85.7% to 117.7%. Therefore, the application potential of PLSHB array surface enhanced LIBS for rapid, stable, and ultra-sensitive detection and analysis of trace metal elements across various fields such as industry, environmental science, and biomedicine might be highly promising.

Multi-phenotype response and cadmium detection of rice stem under toxic cadmium exposure.

Rice stem is the sole conduit for cadmium translocation from underground to aboveground. The presence of cadmium can trigger responses of rice stem multi-phenotype, affecting metabolism, reducing yield, and altering composition, which is related to crop growth, food safety, and new energy utilization. Exploring the adversity response of plant phenotypes can provide a reliable assessment of growth status. However, the phytotoxicity and mechanism of cadmium stress on rice stem remain unclear. Here, we systematically revealed the response mechanisms of cadmium accumulation, adversity physiology, and morphological characteristic in rice stem under cadmium stress for the first time with concentration gradients of CK, 5, 25, 50, and 100 µM, and duration gradients of Day 5, Day 10, Day 15, and Day 20. The results indicated that cadmium stress led to a significant increase in cadmium accumulation, accompanied by the adversity response in stem phenotypes. Specifically, cadmium can cause fluctuations in soluble protein and disturbance of malondialdehyde (MDA), which reflects lipid peroxidation induced by cadmium accumulation. Lipid peroxidation inhibited rice growth by causing (1) a reduction in stem length, diameter, and weight, (2) suppression of air cavity, vascular bundle, parenchyma, and epidermal hair, and (3) disruption of cell structure. Furthermore, rapid detection of cadmium was realized based on the combination of laser-induced breakdown spectroscopy (LIBS) and machine learning, which took less than 3 min. The established qualitative model realized the precise discrimination of cadmium stress degrees with a prediction accuracy exceeding 92 %, and the quantitative model achieved the outstanding prediction effect of cadmium, with Rp of 0.9944. This work systematically revealed the phytotoxicity of cadmium on rice stem multi-phenotype from a novel perspective of lipid peroxidation and realized the rapid detection of cadmium in rice stem, which provided the technical tool and theoretical foundation for accurate prevention and efficient control of heavy metal risks in crops.

Machine Learning-Assisted Determination of C6H14 Mole Fraction From Molecular Emissions of Laser-Induced Hexane-Air Plasmas.

Laser-induced plasmas of materials containing hydrocarbons present strong carbon molecular emission features. Using these emissions to build models relating changes in spectral features to a physical parameter of the system, such as hydrocarbon content, can be difficult because of the dynamic complexity of the spectral features and temperature disequilibrium between molecular species. This study presents machine learning models trained to quantify the mole fraction of hexane in hexane-air plasmas from CN Violet and C2 Swan spectral features. Ensemble regression methods provide the most accurate predictions with root mean squared error on the order 10-2. Artificial neural network regressions produce predictions with superlative sensitivity, exhibiting detection limits as low as 0.008. These foundational models can be further refined with more advanced data to quantify the presence of carbon species in complex plasma environments, such as high-speed reacting flows.

Simultaneous Application of Raman and Laser-Induced Breakdown Spectroscopy in the Gas Phase with a Single Laser and Detector.

Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) are powerful tools for molecular and elemental analysis, respectively. Their combined application, however, is challenging due to the differences in the signal generation and detection characteristics. This note proposes three experimental schemes for the simultaneous application of Raman and LIBS for gas-phase diagnostics. Ring-cavity optical pulse stretchers facilitate shaping suitable pulse pairs from a Q-switched laser that enables the quasi-simultaneous detection of the Raman and LIBS signals on a single detector.