Methodology and applications of elemental mapping by laser induced breakdown spectroscopy

Assessing spatial distribution of bioindicator elements in various cutaneous tumors using correlative imaging with laser-ablation-based analytical methods

Correlative imaging of cutaneous tumors provides additional information to the standard histopathologic examination. However, the joint progress in the establishment of analytical techniques, such as Laser-Induced Breakdown Spectroscopy (LIBS) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) in clinical practice is still limited. Their combination provides complementary information as it is also shown in our study in terms of major biotic (Ca, Mg, and P) and trace (Cu and Zn) elements. To elucidate changes in the elemental composition in tumors, we have compiled a set of malignant tumors (Squamous Cell Carcinoma, Basal Cell Carcinoma, Malignant Melanoma, and Epithelioid Angiosarcoma), one benign tumor (Pigmented Nevus) and one healthy-skin sample. The data processing was based on a methodological pipeline involving binary image registration and affine transformation. Thus, our paper brings a feasibility study of a practical methodological concept that enables us to compare LIBS and LA-ICP-MS results despite the mutual spatial distortion of original elemental images. Moreover, we also show that LIBS could be a sufficient pre-screening method even for a larger number of samples according to the speed and reproducibility of the analyses. Whereas LA-ICP-MS could serve as a ground truth and reference technique for preselected samples.

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.

Enhancing Diagnostic Capabilities for Occupational Lung Diseases Using LIBS Imaging on Biopsy Tissue

Laser-induced breakdown spectroscopy (LIBS) imaging continues to gain strength as an influential bioanalytical technique, showing intriguing potential in the field of clinical analysis. This is because hyperspectral LIBS imaging allows for rapid, comprehensive elemental analysis, covering elements from major to trace levels consistently year after year. In this study, we estimated the potential of a multivariate spectral data treatment approach based on a so-called convex envelope method to detect exotic elements (whether they are minor or in trace amounts) in biopsy tissues of patients with occupational exposure-related diseases. More precisely, we have developed an approach called Interesting Features Finder (IFF), which initially allowed us to identify unexpected elements without any preconceptions, considering only the set of spectra contained in a LIBS hyperspectral data cube. This task is, in fact, almost impossible with conventional chemometric tools, as it entails identifying a few exotic spectra among several hundred thousand others. Once this detection was performed, a second approach based on correlation was used to locate their distribution in the biopsies. Through this unique data analysis pipeline to processing massive LIBS spectroscopic data, it was possible to detect and locate exotic elements such as tin and rhodium in a patient's tissue section, ultimately leading to a possible reclassification of their lung condition as an occupational disease. This review will thus demonstrate the potential of this new diagnostic tool based on LIBS imaging in addressing the shortcomings of approaches developed thus far. The proposed data processing approach naturally transcends this specific framework and can be leveraged across various domains of analytical chemistry, where the detection of rare events is concealed within extensive data sets.

Tracking organic matrix in the seashell by elemental mapping under laser-induced breakdown spectroscopy

As a biogenic calcium carbonate, the seashell plays a crucial role in marine environmental studies. In these studies, it is essential to investigate the composition of the seashell. In this study, we used laser-induced breakdown spectroscopy (LIBS) to analyze the elemental composition of cultured scallop-shell (Patinopecten yessoensis), with a specific focus on examining the organic elements (C, N, O, H) to track the shell organic matrix (SOM). Our findings indicate that the seashell organic layer can be accurately identified by referencing the strong emission of nitrogen or the low signal of calcium. To further confirm the presence of this layer, we employed fluorescence spectroscopy, Raman spectroscopy and FTIR spectroscopy. Correlation analysis revealed a strong connection between LIBS emissions (H, O, CC) and seashell organics, as well as demonstrated the presence of organics in metallic emissions (Si, Ba). However, when we conducted elemental mapping on the shell cross-section, the distribution similarity was observed between the elements N, Ba, and Sr. Based on the correlation of organics and the distribution similarity, it is concluded that barium is an element associated with the SOM. These results highlight the potential of LIBS for organic analysis, which can complement traditional seashell analysis.

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.

From sensor fusion to knowledge distillation in collaborative LIBS and hyperspectral imaging for mineral identification

Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques by combining information gathered from distinct sources. In this manuscript, we explore such opportunities by focusing on two well-known spectral imaging techniques, namely laser-induced breakdown spectroscopy, and hyperspectral imaging, and explore the opportunities of collaborative sensing for a case study involving mineral identification. In specific, the work builds upon two distinct approaches: a traditional sensor fusion, where we strive to increase the information gathered by including information from the two modalities; and a knowledge distillation approach, where the Laser Induced Breakdown spectroscopy is used as an autonomous supervisor for hyperspectral imaging. Our results show the potential of both approaches in enhancing the performance over a single modality sensing system, highlighting, in particular, the advantages of the knowledge distillation framework in maximizing the potential benefits of using multiple techniques to build more interpretable models and paving for industrial applications.

Nickel oxide nanoparticles induce cell wall modifications, root anatomical changes, and nitrosative signaling in ecotypes of Ni hyperaccumulator Odontarrhena lesbiaca

The industrial application and environmental release of nickel oxide NPs (NiO NPs) is increasing, but the details of their relationship with plants are largely unknown. In this work, the cellular, tissue, organ, and molecular level responses of three ecotypes of Ni hyperaccumulator Odontarrhena lesbiaca grown in the presence of high doses of NiO NP (250 mg/L and 500 mg/L) were studied. All three ecotypes showed a similar accumulation of Ni in the presence of nano Ni, and in the case of NiO NPs, the root-to-shoot Ni translocation was slighter compared to the bulk Ni. In all three ecotypes, the walls of the root cells effectively prevented internalization of NiO NPs, providing cellular defense against Ni overload. Exposure to NiO NP led to an increase in cortex thickness and the deposition of lignin-suberin and pectin in roots, serving as a tissue-level defense mechanism against excessive Ni. Exposure to NiO NP did not modify or cause a reduction in some biomass parameters of the Ampeliko and Loutra ecotypes, while it increased all parameters in Olympos. The free salt form of Ni exerted more negative effects on biomass production than the nanoform, and the observed effects of NiO NPs can be attributed to the release of Ni ions. Nitric oxide and peroxynitrite levels were modified by NiO NPs in an ecotype-dependent manner. The changes in the abundance and activity of S-nitrosoglutathione reductase protein triggered by NiO NPs suggest that the enzyme is regulated by NiO NPs at the post-translational level. The NiO NPs slightly intensified protein tyrosine nitration, and the slight differences between the ecotypes were correlated with their biomass production in the presence of NiO NPs. Overall, the Odontarrhena lesbiaca ecotypes exhibited tolerance to NiO NPs at the cellular, tissue, organ/organism and molecular levels, demonstrating various defense mechanisms and changes in the metabolism of reactive nitrogen species metabolism and nitrosative protein modification.

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.

Expanding the boundaries of atomic spectroscopy at the single-cell level: critical review of SP-ICP-MS, LIBS and LA-ICP-MS advances for the elemental analysis of tissues and single cells

Metals have a fundamental role in microbiology, and accurate methods are needed for their identification and quantification. The inability to assess cellular heterogeneity is considered an impediment to the successful treatment of different diseases. Unlike bulk approaches, single-cell analysis allows elemental heterogeneity across genetically identical populations to be related to specific biological events and to the effectiveness of drugs. Single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS) can analyse single cells in suspension and measure this heterogeneity. Here we explore advances in instrumental design, compare mass analysers and discuss key parameters requiring optimisation. This review has identified that the effect of pre-treatment of cell suspensions and cell fixation approaches require further study and novel validation methods are needed as using bulk measurements is unsatisfactory. SP-ICP-MS has the advantage that a large number of cells can be analysed; however, it does not provide spatial information. Techniques based on laser ablation (LA) enable elemental mapping at the single-cell level, such as laser-induced breakdown spectroscopy (LIBS) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). The sensitivity of commercial LIBS instruments restricts its use for sub-tissue applications; however, the capacity to analyse endogenous bulk components paired with developments in nano-LIBS technology shows great potential for cellular research. LA-ICP-MS offers high sensitivity for the direct analysis of single cells, but standardisation requires further development. The hyphenation of these trace elemental analysis techniques and their coupling with multi-omic technologies for single-cell analysis have enormous potential in answering fundamental biological questions.

Signal enhancement in spark-assisted laser-induced breakdown spectroscopy for discrimination of glioblastoma and oligodendroglioma lesions

Here, the discrimination of two types of lethal brain cancers, i.e., glioblastoma multiforme (GBM) and oligodendroglioma (OG) are investigated under the laser-induced breakdown spectroscopy (LIBS) and the electrical spark-assisted laser-induced breakdown spectroscopy (SA-LIBS) in order to discriminate the human brain glioma lesions against the infiltrated tissues. It is shown there are notable differences between the plasma emissions over the brain gliomas against those of infiltrated tissues. In fact, a notable enhancement appears in the characteristic emissions in favor of SA-LIBS against those of conventional LIB spectra. Moreover, the plasma properties such as temperature, electron density, and degree of ionization are probed through the data processing of the plasma emissions. The corresponding parameters, taken from SA-LIBS data, attest to be lucidly larger than those of LIBS up to one order of magnitude. In addition, the ionic species such as Mg II characteristic line at 279 nm and caII emission at 393 nm are notably enhanced in favor of SA-LIBS. In general, the experimental evidence verifies that SA-LIBS is beneficial in the discrimination and grading of GBM/OG neoplasia against healthy (infiltrate) tissues in the early stages.

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.

Laser adaptive processing technology for multilayer dissimilar materials

We report a laser adaptive processing technology (LAPT) for the selective removal of Cu/Al multilayer dissimilar materials. Using the wavelength range and intensity distribution of the characteristic spectrum, the properties and content of multilayer dissimilar materials can be analyzed online based on laser-induced breakdown spectroscopy. The traditional low-speed spectral detection mode was transformed into a high-speed photoelectric detection method by using a scheme consisting of a bandpass filter with an avalanche photodetector (APD), and the in situ online detection of a 30 ns, 40 kHz high-frequency pulse signal during laser scanning was realized. Combined with a field programmable gate array (FPGA) digital control unit, online feedback and closed-loop control were achieved at the kHz level, and the adaptive intelligent control of material interfaces and laser processing parameters was achieved. This excellently demonstrated the feasibility and flexibility of LAPT for processing arbitrary multilayer dissimilar materials.

Enhancing Dental Cement Bond Strength with Autofocus-Laser-Cutter-Generated Grooves on Polyetheretherketone Surfaces

Polyetheretherketone (PEEK) is widely used in dentistry owing to its exceptional properties, including its natural appearance; however, existing surface treatment methods for bonding PEEK have limitations. Autofocus laser cutters, known for their precise engraving and cutting capabilities, offer potential for surface treatment of PEEK; thus, the objective of this study was to investigate the creation of laser groove structures on PEEK to enhance its bonding capability with dental resin cement. A dental computer-aided design and manufacturing system was used to fabricate PEEK samples, and three groove patterns (circle, line, and grid) were generated on PEEK surfaces, with air-abrasion used as the control group. The surface characteristics, cell viability, and bond strength were evaluated, and the data were statistically analyzed using one-way analysis of variance and post hoc Tukey's tests (α = 0.05). Laser-treated PEEK exhibited a uniform texture with a groove depth of approximately 39.4 µm, hydrophobic properties with a contact angle exceeding 90°, a surface roughness of 7.3-12.4 µm, consistent topography, and comparable cell viability compared with untreated PEEK. Despite a decrease in bond strength after thermal cycling, no significant intergroup differences were observed, except for the line-shaped laser pattern. These findings indicate that the autofocus laser cutter effectively enhances the surface characteristics of PEEK by creating a uniform texture and grooves, showing promise in improving bonding properties, even considering the impact of thermal cycling effects.

Selenate triggers diverse oxidative responses in Astragalus species with diverse selenium tolerance and hyperaccumulation capacity

Selenium (Se) hyperaccumulators are capable of uptake and tolerate high Se dosages. Excess Se-induced oxidative responses were compared in Astragalus bisulcatus and Astragalus cicer. Plants were grown on media supplemented with 0, 25 or 75 μM selenate for 14 days. Both A. bisulcatus and A. cicer accumulated >2000 μg/g dry weight Se to the shoot but the translocation factors of A. cicer were below 1 suggesting its non hyperaccumulator nature. A. cicer showed Se sensitivity indicated by reduced seedling fresh weight, root growth and root apical meristem viability, altered element homeostasis in the presence of Se. In Se-exposed A. bisulcatus, less toxic organic Se forms (mainly MetSeCys, γ-Glu-MetSeCys, and a selenosugar) dominated, while these were absent from A. cicer suggesting that the majority of the accumulated Se may be present as inorganic forms. The glutathione-dependent processes were more affected, while ascorbate levels were not notably influenced by Se in either species. Exogenous Se triggered more intense accumulation of malondialdehyde in the sensitive A. cicer compared with the tolerant A. bisulcatus. The extent of protein carbonylation in the roots of the 75 μM Se-exposed A. cicer exceeded that of A. bisulcatus indicating a correlation between selenate sensitivity and the degree of protein carbonylation. Overall, our results reveal connection between oxidative processes and Se sensitivity/tolerance/hyperaccumulation and contribute to the understanding of the molecular responses to excess Se.

Quantitative elemental mapping of biological tissues by laser-induced breakdown spectroscopy using matrix recognition

The present study demonstrates the importance of converting signal intensity maps of organic tissues collected by laser-induced breakdown spectroscopy (LIBS) to elemental concentration maps and also proposes a methodology based on machine learning for its execution. The proposed methodology employs matrix-matched external calibration supported by a pixel-by-pixel automatic matrix (tissue type) recognition performed by linear discriminant analysis of the spatially resolved LIBS hyperspectral data set. On a swine (porcine) brain sample, we successfully performed this matrix recognition with an accuracy of 98% for the grey and white matter and we converted a LIBS intensity map of a tissue sample to a correct concentration map for the elements Na, K and Mg. Found concentrations in the grey and white matter agreed the element concentrations published in the literature and our reference measurements. Our results revealed that the actual concentration distribution in tissues can be quite different from what is suggested by the LIBS signal intensity map, therefore this conversion is always suggested to be performed if an accurate concentration distribution is to be assessed.

Milk's inorganic content analysis via laser induced breakdown spectroscopy

In the present work, the inorganic content of different milk samples is investigated by Laser Induced Breakdown Spectroscopy (LIBS) technique. Milk samples of different animal origin, in liquid, lyophilized powder, and ashed forms were studied using both infrared (1064 nm) and visible (532 nm) laser excitation conditions and the optimum experimental conditions for the measurement of the inorganic elements present in low concentration, were determined. Spectral features of major (Ca, Na, Mg and K) and minor minerals (P, Zn, Cu and Si) were detected and identified. The LIBS results for the different milk samples were found to correlate perfectly with the results obtained from atomic absorption measurements, demonstrating the potential of LIBS technique for the fast and in-situ qualitative characterization of the inorganic content of different animal origin milk samples.

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.

Laser-induced breakdown spectroscopy-guided synergistic phototherapy: A theranostic platform mediated by a single laser wavelength

Currently, cancer theranostic studies have only focused on integrating existing medical imaging techniques with therapeutic modalities. Obviously, this strategy is not a real theranostic method, as diagnosis and therapy are based on different principles and require independent operation. Here, a cancer theranostic method was established by laser-induced breakdown spectroscopy (LIBS)-mediated synergistic photothermal/photodynamic therapy, which was activated by a single 1064-nm light for simultaneous tumor localization and treatment. PEGylated cobalt phosphate (CoP@PEG) nanoparticles (NPs) with strong near-infrared (NIR)-II absorbance, high photothermal conversion efficiency and a reactive oxygen species generation effect were fabricated, and they produced excellent antitumor outcomes under 1064-nm excitation, as evidenced by the substantial increase in HepG2 cell death in vitro and complete tumor elimination in vivo. Meanwhile, the diagnostic method of the LIBS imaging system used in the present study also uses 1064-nm light. The LIBS imaging system can provide fast, real-time analysis and imaging of elements and facilitate the localization of the tumor site by monitoring the distribution of CoP@PEG NPs for precise tumor treatment. We postulate that this theranostic platform will promote the development of further theranostic research.

Analysis of Laser-Induced Breakdown Spectroscopy Data Acquired from Boundary of Two Matrices

Laser-induced breakdown spectroscopy (LIBS) data obtained from the elemental imaging of heterogenous samples were processed with various chemometric algorithms. The intention was to cluster obtained characteristic spectra and to provide additional information about the sample surface composition and distribution of individual matrices. However, there is a gray zone on the boundary of two matrices and the consequent clustering of the spectra obtained on this boundary is ambiguous. This paper focuses on the transition between two well-defined matrices in a simplified case for a better transparency in data visualization. Steel and aluminum samples that are represented by characteristic spectra with significantly distinct structures (e.g., different number of spectral lines). Using a carefully designed experiment, several Fe:Al ratios were ablated and analyzed by principal component analysis (PCA), self-organizing maps (SOM), and standard data metrics. This paper shows the strategy for the discrimination of unrecognized spectra and possibilities in their clustering.

Diagnosis of Gulf War Illness Using Laser-Induced Spectra Acquired from Blood Samples

Gulf War illness (GWI) is a chronic illness with no known validated biomarkers that affects the lives of hundreds of thousands of people. As a result, there is an urgent need for the development of an untargeted and unbiased method to distinguish GWI patients from non-GWI patients. We report on the application of laser-induced breakdown spectroscopy (LIBS) to distinguish blood plasma samples from a group of subjects with GWI and from subjects with chronic low back pain as controls. We initially obtained LIBS data from blood plasma samples of four GWI patients and four non-GWI patients. We used an analytical method based on taking the difference between a mean LIBS spectrum obtained with those of GWI patients from the mean LIBS spectrum of those of the control group, to generate a "difference" spectrum for our classification model. This model was cross-validated using different numbers of differential LIBS emission peaks. A subset of 17 of the 82 atomic and ionic transitions that provided 70% of correct diagnosis was selected test in a blinded fashion using 10 additional samples and was found to yield 90% classification accuracy, 100% sensitivity, and 83.3% specificity. Of the 17 atomic and ionic transitions, eight could be assigned unambiguously to species of Na, K, and Fe.

Determination of Fluorine Distribution in Shark Teeth by Laser Induced Breakdown Spectroscopy

Quantifying the chemical composition of fast-growing hard tissues in the environment can shed valuable information in terms of understanding ecosystems both prehistoric and current. Changes in chemical composition can be correlated with environmental conditions and can provide information about the organism's life. Sharks can lose 0.1 to 1.1 teeth/day, depending on species, which offers a unique opportunity to record environmental changes over a short duration of time. Shark teeth contain a biomineral phase that is made up of fluorapatite [Ca₅(PO₄)₃F], and the F distribution within the tooth can be correlated to tooth hardness. Typically, this is determined by bulk acid digestion, energy-dispersive X-ray spectroscopy (EDS), or wavelength-dispersive spectroscopy. Here we present laser-induced breakdown spectroscopy (LIBS) as an alternative and faster approach for determining F distribution within shark teeth. Using a two-volume laser ablation chamber (TwoVol3) with innovative embedded collection optics for LIBS, shark teeth were investigated from sand tiger (Carcharias Taurus), tiger (Galeocerdo Cuvier), and hammerhead sharks (Sphyrnidae). Fluorine distribution was mapped using the CaF 603 nm band (CaF, Β ²Σ⁺ → X ²Σ⁺) and quantified using apatite reference materials. In addition, F measurements were cross referenced with EDS analyses to validate the findings. Distributions of F (603 nm), Na (589 nm), and H (656 nm) within the tooth correlate well with the expected biomineral composition and expected tooth hardness. This rapid methodology could transform the current means of determining F distribution, particularly when large sample specimens (350 mm², presented here) and large quantities of specimens are of interest.

The Development of Dentin Microstructure Is Controlled by the Type of Adjacent Epithelium

Considerable amount of research has been focused on dentin mineralization, odontoblast differentiation, and their application in dental tissue engineering. However, very little is known about the differential role of functionally and spatially distinct types of dental epithelium during odontoblast development. Here we show morphological and functional differences in dentin located in the crown and roots of mouse molar and analogous parts of continuously growing incisors. Using a reporter (DSPP-cerulean/DMP1-cherry) mouse strain and mice with ectopic enamel (Spry2^+/- ;Spry4^-/- ), we show that the different microstructure of dentin is initiated in the very beginning of dentin matrix production and is maintained throughout the whole duration of dentin growth. This phenomenon is regulated by the different inductive role of the adjacent epithelium. Thus, based on the type of interacting epithelium, we introduce more generalized terms for two distinct types of dentins: cementum versus enamel-facing dentin. In the odontoblasts, which produce enamel-facing dentin, we identified uniquely expressed genes (Dkk1, Wisp1, and Sall1) that were either absent or downregulated in odontoblasts, which form cementum-facing dentin. This suggests the potential role of Wnt signalling on the dentin structure patterning. Finally, we show the distribution of calcium and magnesium composition in the two developmentally different types of dentins by utilizing spatial element composition analysis (LIBS). Therefore, variations in dentin inner structure and element composition are the outcome of different developmental history initiated from the very beginning of tooth development. Taken together, our results elucidate the different effects of dental epithelium, during crown and root formation on adjacent odontoblasts and the possible role of Wnt signalling which together results in formation of dentin of different quality. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Rapid detection of Pseudomonas aeruginosa based on lab-on-a-chip platform using immunomagnetic separation, light scattering, and machine learning

The rapid detection of the pathogenic bacteria in patient samples is crucial to expedient patient care. The proposed approach reports the development of a novel lab-on-a-chip device for the rapid detection of P. aeruginosa based on immunomagnetic separation, optical scattering, and machine learning. The immunomagnetic particles with a diameter of 5 μm were synthesized for isolating P. aeruginosa from the test sample. A microfluidic chip was fabricated, and three optical fibers were embedded for connecting a laser light and two photodetectors. The laser light was pointed towards the channel to pass light through the sample. A pair of photodetectors via optical fibers were arranged symmetrically at 45° to the channel. The photodetectors acquired scattered light from the flowing sample and converted the light to an electrical signal. The sample containing immunomagnetic beads linked with bacteria was injected into the microfluidic chip. The optimized conditions for performing the experiments were characterized for real-time detection of P. aeruginosa. The data acquisition system recorded the real-time light scattering from the test sample. After removing noise from the output waveform, five different time-domain statistical features were extracted from each waveform: standard mean, standard variance, skewness, kurtosis, and coefficient of variation. The pathogens classification was performed by training the discrimination model using extracted features based on machine learning algorithms. The support vector machines (SVM) with a sigmoid function kernel showed superior classification performance with 97.9% accuracy among other classifiers, including k-nearest neighbors (KNN), logistic regression (LR), and naïve Bayes (NB). The method can detect P. aeruginosa specifically and quantitatively with a limit of detection of 10² CFU/mL. The device can classify P. aeruginosa within 10 min with a total assay time of 25 min. The device was used to test its ability to detect pathogen from the serum and sputum specimens spiked with 10⁵ CFU/mL concentration of P. aeruginosa. The results indicate that light scattering combined with machine learning can be used to detect P. aeruginosa. The proposed technique is anticipated to be helpful as a rapid device for diagnosing P. aeruginosa related infections.

Interpretable deep learning-assisted laser-induced breakdown spectroscopy for brand classification of iron ores

Brand classification of iron ores using laser-induced breakdown spectroscopy (LIBS) combined with artificial neural networks can quickly realize the compliance verification and guarantee the interests of both trading partners. However, its practical application is impeded by complex pretreatments and unexplained feature learning problems. According to the LIBS data characteristics of iron ores, a convolutional neural network (CNN) is designed to predict 16 types of brand iron ores from Australia, Brazil, and South Africa. The accuracies of the calibration set and the prediction set with five-fold cross-validation (5-CV) were 99.86% and 99.88%, and the value of loss function was 0.0356. Meanwhile, the established CNN method was also compared with common machine learning methods using raw spectra as input variables, and it outperformed other methods. For the first time, this work interprets the CNN's effectiveness layer by layer in self-adaptively extracting LIBS features through t-distributed stochastic neighbor embedding (t-SNE) and the quantitative data of major chemical components in iron ores. Our approach shows that deep learning assisted LIBS is able to significantly reduce manual factors in preprocessing and feature selection and has broad application prospects in the brand classification of iron ores.

Methodology for the Implementation of Internal Standard to Laser-Induced Breakdown Spectroscopy Analysis of Soft Tissues

The improving performance of the laser-induced breakdown spectroscopy (LIBS) triggered its utilization in the challenging topic of soft tissue analysis. Alterations of elemental content within soft tissues are commonly assessed and provide further insights in biological research. However, the laser ablation of soft tissues is a complex issue and demands a priori optimization, which is not straightforward in respect to a typical LIBS experiment. Here, we focus on implementing an internal standard into the LIBS elemental analysis of soft tissue samples. We achieve this by extending routine methodology for optimization of soft tissues analysis with a standard spiking method. This step enables a robust optimization procedure of LIBS experimental settings. Considering the implementation of LIBS analysis to the histological routine, we avoid further alterations of the tissue structure. Therefore, we propose a unique methodology of sample preparation, analysis, and subsequent data treatment, which enables the comparison of signal response from heterogenous matrix for different LIBS parameters. Additionally, a brief step-by-step process of optimization to achieve the highest signal-to-noise ratio (SNR) is described. The quality of laser-tissue interaction is investigated on the basis of the zinc signal response, while selected experimental parameters (e.g., defocus, gate delay, laser energy, and ambient atmosphere) are systematically modified.