Search Results (295)

Against the background of globalization, the circulation range of traditional Chinese medicinal materials is constantly expanding, and the phenomena of mixed origins and counterfeiting are becoming increasingly serious. Tracing the origin of traditional Chinese medicinal materials is of great significance for ensuring their quality, safety, and effectiveness. Laser-induced breakdown spectroscopy (LIBS), as a rapid and non-destructive element analysis technique, can be used for the origin tracing of traditional Chinese medicinal materials. Deep learning can not only handle non-linear relationships but also automatically extract features from high-dimensional data. In this paper, LIBS is combined with deep learning, and a Double-Multi Convolutional Neural Network LIBS Analysis System (DMC-LIBSAS) is proposed for the origin tracing of the traditional Chinese medicinal material Angelica dahurica. The system consists of a LIBS signal generation module, a spectral preprocessing module, and an algorithm analysis module—Double-Multi Convolutional Neural Network (DMCNN)—achieving a direct mapping from input data to output results. And the ability of DMCNN to extract characteristic peaks is demonstrated by the 1D Gradient-weighted Class Activation Mapping (1D-Grad-CAM) method. The tracing accuracy of DMC-LIBSAS for Angelica dahurica reaches 95.25%. To further verify the effectiveness of the system, it is compared with six classic methods including LeNet, AlexNet, Resnet18, K-nearest neighbors (KNN), Random Forest (RF), and Decision Tree (DT) (with accuracies of 68%, 75%, 72.5%, 79.7%, 86.7%, and 75.5%, respectively), and the tracing effects are all much lower than that of DMC-LIBSAS. The results show that DMC-LIBSAS can effectively and accurately trace the origin of Angelica dahurica, providing a new technical support for the quality supervision of traditional Chinese medicinal materials. Full article

Laser-induced breakdown spectroscopy (LIBS), a rapid and versatile analytical technique, is becoming increasingly widespread within the geoscience community. Suitable for fieldwork analyses using handheld analyzers, the elemental composition of a sample is revealed by generating plasma using a high-energy laser, providing a practical solution to numerous geological challenges, including identifying and discriminating between different mineral phases. This data paper presents over 12,000 reference mineral spectra acquired using a handheld LIBS analyzer (© SciAps), including those of silicates (e.g., beryl, quartz, micas, spodumene, vesuvianite, etc.), carbonates (e.g., dolomite, magnesite, aragonite), phosphates (e.g., amblygonite, apatite, topaz), oxides (e.g., hematite, magnetite, rutile, chromite, wolframite), sulfates (e.g., baryte, gypsum), sulfides (e.g., chalcopyrite, pyrite, pyrrhotite), halides (e.g., fluorite), and native elements (e.g., sulfur and copper). The datasets were collected from 170 pure mineral samples in the form of crystals, powders, and rock specimens, during three research projects: NEXT, Labex Ressources 21, and ARTeMIS. The extensive spectral range covered by the analyzer spectrometers (190–950 nm) allowed for the detection of both major (>1 wt.%) and trace (<1 wt.%) elements, recording a unique spectral signature for each mineral. Mineral spectra can serve as reference data to (i) identify relevant emission lines and spectral ranges for specific minerals, (ii) be compared to unknown LIBS spectra for mineral identification, or (iii) constitute input data for machine learning algorithms. Full article

In the present work, laser-induced breakdown spectroscopy (LIBS) and fluorescence spectroscopy are used and assessed for the detection of EVOOs’ adulteration with some non-EVOO edible oils (i.e., pomace, corn, sunflower, and soybean) and the discrimination of EVOOs based on geographical origin. For the direct comparison of the performance of the two techniques, the same set of EVOO samples was studied. The acquired spectroscopic data were analyzed by several machine learning algorithms, and the constructed predictive models are evaluated thoroughly for their reliability and robustness. In all cases, the high classification accuracies obtained support the potential and efficiency of both LIBS and fluorescence spectroscopy for the rapid, online, and in situ study of EVOOs’ authentication issues, with LIBS being more advantageous as it operates much faster. Full article

We developed a composite recognition method combining hybrid sensor recognition and artificial intelligence image recognition techniques and optimized quality monitoring of the ceramic production process. By integrating deep learning models and image preprocessing techniques, appearance defects (such as cracks and color differences) were detected in ceramic products, and key parameters in the sintering process using temperature, pressure, and gas-sensitive sensors were monitored in real-time. The developed composite recognition method significantly improved the quality control level of ceramic production, reduced production energy consumption and wastes, and improved production efficiency. Full article

Laser-induced breakdown spectroscopy (LIBS) is widely used for online quantitative analysis in industries due to its rapid analysis and minimal damage. However, challenges like signal instability, matrix effects, and self-absorption hinder the measurement accuracy. Recent approaches, including the internal standard method and crater limitation method, aim to improve the stability but suffer from high computational demands or complexity. This study proposes a method to enhance LIBS stability by utilizing craters formed from laser ablation without external cavity assistance. It first improves the plasma temperature calculation reliability using multiple elemental spectral lines, after which electron density calculations are performed. By fitting plasma parameter curves based on laser pulse counts and using a laser confocal microscope for crater analysis, stable plasma conditions were found within crater areas of 0.400 mm² to 0.443 mm² and depths of 0.357 mm to 0.412 mm. Testing with elemental spectral lines of Ti II, K II, Ca I, and Fe I showed a significant reduction in the relative standard deviation (RSD) of the LIBS spectral line intensity, demonstrating an improved signal stability within specified crater dimensions. Full article

Nowadays, the recovery/recycling/reuse of mining and mineral processing wastes is considered the best approach to support the circular economy and sustainability of mining and metal extraction industries. Mine wastes can be used to restore surface and subsurface land damaged by mining operations, generate fuel for power plants, further extract their component minerals, and as building materials additives. The aim of this perspective paper is to briefly highlight and focus on the most recent analytical potential and performance achieved by handheld laser-induced breakdown spectroscopy (hLIBS) instrumentation in the perspective of its future application in the mine waste sector to quickly identify on-site the presence of useful chemical elements for their possible sustainable recovery. Full article

Strategic metals are indispensable for meeting the needs of modern society. It is then necessary to reassess the potential of such metals in Europe. For the exploration of strategic metals, portable XRF (X-Ray Fluorescence) and LIBS (Laser Induced Breakdown Spectroscopy) are powerful techniques allowing their multi-elementary analysis. This paper presents a database providing more than 2000 pXRF data and more than 4000 pLIBS spectra acquired on minerals from the Mineralogy and Petrology Museum of National and Kapodistrian University of Athens (NKUA), selected based on their potential in bearing strategic metals. The combination of these two portable techniques, along with expanding dataset on strategic metal-rich minerals, provides valuable insights into strategic metal affinities and demonstrates the effectiveness of portable tools for exploring strategic raw materials. Indeed, such database allows to strengthen the knowledge on strategic metals by producing statistic and chemometric analyses (e.g., boxplot, PCA, PLS) on their distribution. Full article

Laser-induced breakdown spectroscopy (LIBS) is a rapid, non-contact analytical technique that is widely applied in various fields. However, the high dimensionality and information redundancy of LIBS spectral data present challenges for effective model development. This study aims to assess the effectiveness of the minimum redundancy and maximum relevance (mRMR) method for feature selection in LIBS spectral data and to explore its adaptability across different predictive modeling approaches. Using the ChemCam LIBS dataset, we constructed predictive models with four quantitative methods: random forest (RF), support vector regression (SVR), back propagation neural network (BPNN), and partial least squares regression (PLSR). We compared the performance of mRMR-based feature selection with that of full-spectrum data and three other feature selection methods: competitive adaptive re-weighted sampling (CARS), Regressional ReliefF (RReliefF), and neighborhood component analysis (NCA). Our results demonstrate that the mRMR method significantly reduces the number of selected features while improving model performance. This study validates the effectiveness of the mRMR algorithm for LIBS feature extraction and highlights the potential of feature selection techniques to enhance predictive accuracy. The findings provide a valuable strategy for feature selection in LIBS data analysis and offer significant implications for the practical application of LIBS in predicting elemental content in geological samples. Full article

To promote sustainability and reduce the ecological footprint of recycling processes, this study develops an analytical tool for fast and accurate identification of components in photovoltaic panels (PVs) and Li-Ion battery waste, optimizing material recovery and minimizing resource wastage. The laser-induced breakdown spectroscopy (LIBS) technique was selected and employed to identify fluoropolymers in photovoltaic back sheets and to determine the thickness of layers containing fluorine. LIBS was also used for Li-Ion batteries to reveal the elemental composition of anode, cathode, and separator materials. The analysis not only revealed all the elements contained in the electrodes but also, in the case of cathode materials, allowed distinguishing a single-component cathode (cathode A containing LiCoO₂) from multi-component materials (cathode B containing a mixture of LiMn₂O₄ and LiNi_0.5Mn_1.5O₄). The results of LIBS analysis were verified using SEM-EDS analysis and XRD examination. Additionally, an indirect method for identifying fluoropolymers (polytetrafluoroethylene (PTFE) or poly(vinylidene fluoride) (PVDF)) employed to prepare dispersions of cathode materials was proposed according to the differences in wettability of both polymers. By enabling efficient material identification and separation, this study advances sustainable recycling practices, supporting circular economy goals in the renewable energy sector. Full article

The fast detection of Extra Virgin Olive Oil (EVOO) adulteration with poorer quality and lower price vegetable oils is important for the protection of consumers and the market of olive oil from fraudulent activities, the latter exhibiting an increasing trend worldwide during the last few years. In this work, two optical spectroscopic techniques, namely, Laser-Induced Breakdown Spectroscopy (LIBS) and UV-Vis-NIR absorption spectroscopy, are employed and are assessed for EVOO adulteration detection, using the same set of olive oil samples. In total, 184 samples were studied, including 40 EVOOs and 144 binary mixtures with pomace, soybean, corn, and sunflower oils, at various concentrations (ranging from 10 to 90% w/w). The emission data from LIBS, related to the elemental composition of the samples, and the UV-Vis-NIR absorption spectra, related to the organic ingredients content, are analyzed, both separately and combined (i.e., fused), by Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs), and Logistic Regression (LR). In all cases, very highly predictive accuracies were achieved, attaining, in some cases, 100%. The present results demonstrate the potential of both techniques for efficient and accurate olive oil authentication issues, with the LIBS technique being better suited as it can operate much faster. Full article

In situ rapid classification of rock lithology is crucial in various fields, including geological exploration and petroleum logging. Laser-induced breakdown spectroscopy (LIBS) is particularly well-suited for in situ online analysis due to its rapid response time and minimal sample preparation requirements. To facilitate in situ raw rock discrimination analysis, a portable LIBS device was developed specifically for outdoor use. This device built upon a previous multi-directional optimization scheme and integrated machine learning to classify seven types of original rock samples: mudstone, basalt, dolomite, sandstone, conglomerate, gypsolyte, and shale from oil logging sites. Initially, spectral data were collected from random areas of each rock sample, and a series of pre-processing steps and data dimensionality reduction were performed to enhance the accuracy and efficiency of the LIBS device. Subsequently, four classification algorithms—linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost)—were employed for classification discrimination. The results were evaluated using a confusion matrix. The final average classification accuracies achieved were 95.71%, 93.57%, 92.14%, and 98.57%, respectively. This work not only demonstrates the effectiveness of the portable LIBS device in classifying various original rock types, but it also highlights the potential of the XGBoost algorithm in improving LIBS analytical performance in field scenarios and geological applications, such as oil logging sites. Full article

Ca and Mg are key constituents in surface water that are essential nutrients and vital indicators of water hardness. Rapid on-site measurement of Ca and Mg concentrations in surface water is important. However, traditional laboratory detection methods are complex and time-consuming, and on-site detection is difficult. In this study, a portable surface water detection method was developed using laser-induced breakdown spectroscopy with a miniaturized spectrometer LIBS and a liquid jet device for sample introduction. The device enables the rapid online in situ measurement of elemental concentrations in the water. The limits of detection for the rapid on-site detection of Ca and Mg in surface water were 11.58 and 2.57 mg/L, respectively. We applied this method to assess the concentrations of Ca and Mg in authentic water samples collected from rivers and ponds. The recovery rates for Ca and Mg were 90.83–101.74% and 93.43–108.74%, respectively. This method is suitable for rapid, on-site, and highly sensitive monitoring of Ca and Mg concentrations in the environment. Full article

Wood modification with graphene oxide can give it unique features characteristic of other materials. However, the durability of the newly acquired features is of great importance. To better understand them, it is worth conducting an in-depth analysis of the structural changes that occur in wood under the influence of modification with graphene oxide. As part of the research, wood was impregnated with aqueous graphene oxide dispersion. Wood was impregnated using two methods: single vacuum and pressureless with ultrasound. Laser-assisted ionization spectroscopy (LIBS) was used to determine elements, mainly carbon, and to characterize differences in the elemental composition between the surface layers of wood impregnated with graphene oxide and native wood. Changes in the structure of polymers building wood tissue were analyzed using LIBS and FTIR spectrometry. The wood surface was also imaged using three microscopic techniques (stereomicroscope, confocal laser scanning microscope, and scanning electron microscopy). LIBS showed that graphene oxide was deposited on the surface of impregnated wood, and the intensity of carbon signals in wood impregnated with graphene oxide using vacuum and ultrasound differed. The content of carbon, magnesium, and oxygen elements in the surface layers of wood impregnated with graphene oxide using ultrasound was lower than in vacuum-impregnated wood. Analysis of FTIR spectra showed effective incorporation of graphene oxide into the surface layer of wood. Full article

Rationale: Orthodontic archwires undergo chemical and structural changes in the complex intraoral environment. The present work aims to investigate the safe duration for intraoral use (related to the nickel release hypothesis) of different types of nickel-containing wires. By analyzing how the nickel content (NC) varies over time, we aim to provide practical recommendations for the optimal use of said archwires. Materials and Methods: Our analysis focuses on the following nickel-containing archwires: stainless steel, Ni-Ti superelastic, heat-activated NiTi and CuNiTi, and multi-force archwires. The studied archwires of each type were divided into three groups: group 1, as received; group 2, retrieved after intraoral exposure for less than 6 weeks; group 3, used for more than 8 weeks. To assess NC, measurements using scanning electron microscopy (SEM), energy-dispersive X-ray (EDX), and laser-induced breakdown spectroscopy (LIBS) were performed in multiple regions of each wire. Statistical analysis of the measured values using one-way ANOVA and multiple group comparisons showed significant differences in nickel content between groups. The dynamic behavior of the statistical results for NC was then modeled using logistic regression and fitted with cubic splines. Conclusions: The proposed behavior model, with further refinement, could enable orthodontists to make informed, patient-specific decisions regarding the safe and effective use of orthodontic floss. The overall conclusion of the study is that due to stability, SS-CrNi, HA-Ni-Ti with Cu, and TriTanium^TM are suitable for long-term use, and due to higher nickel release, Ni-Ti-Superelastic, HA-Ni-Ti without Cu, and Bio-Active^TM are better for short- to medium-term use. Full article