Jewelry rock discrimination as interpretable data using laser-induced breakdown spectroscopy and a convolutional LSTM deep learning algorithm.

In this study, the deep learning algorithm of Convolutional Neural Network long short-term memory (CNN-LSTM) is used to classify various jewelry rocks such as agate, turquoise, calcites, and azure from various historical periods and styles related to Shahr-e Sokhteh. Here, the CNN-LSTM architecture includes utilizing CNN layers for the extraction of features from input data mixed with LSTMs for supporting sequence forecasting. It should be mentioned that interpretable deep learning-assisted laser induced breakdown spectroscopy helped achieve excellent performance. For the first time, this paper interprets the Convolutional LSTM effectiveness layer by layer in self-adaptively obtaining LIBS features and the quantitative data of major chemical elements in jewelry rocks. Moreover, Lasso method is applied on data as a factor for investigation of interoperability. The results demonstrated that LIBS can be essentially combined with a deep learning algorithm for the classification of different jewelry songs. The proposed methodology yielded high accuracy, confirming the effectiveness and suitability of the approach in the discrimination process.

Enhancing biomarker detection sensitivity through tag-laser induced breakdown spectroscopy with NELIBS.

Nanoparticle-enhanced laser-induced breakdown spectroscopy and Tag-LIBS are two approaches that have been shown to significantly enhance LIBS sensitivity and specificity. In an effort to combine both of these approaches, we have initiated a study on the effect of the presence of Silver nanoparticle concentrations on Europium (Eu) and Ytterbium (Yb) LIBS signals. These elements are part of metal-loaded polymers conjugated to antibodies. We observe a signal enhancement of the emission lines of about 10 and 12 times for the Europium and Ytterbium lines. This study shows that Europium and Ytterbium are enhanced differently; Europium shows enhancement for both neutral and ionized species while the Ytterbium shows enhancement only for ionized species. Additionally, we found that NPs at 0.1 mg/mL and 0.05 mg/mL achieved maximum enhancement for Eu and Yb, respectively. Based on our findings, the temperature and electron density of Eu and Yb are not significantly different for NPs concentrations, but the total signal intensity is significantly higher for optimum NP concentrations for both Eu and Yb.

Blood metal analysis of plasmas from donors with and without SARS-CoV-2 using laser-induced breakdown spectroscopy and logistic regression.

Research on the correlation between metal levels in blood and Covid-19 infection has been conducted primarily by assessing how each individual blood metal is linked to different aspects of the disease using samples from donors with various levels of severity to Covid-19 infection. Using logistics regression on LIBS spectra of plasma samples collected pre- and post- Covid-19 pandemic from donors known to have developed various levels of antibodies to the SARS-Cov-2 virus, we show that relying on the levels of Na, K, and Mg together is more efficient at differentiating the two types of plasma samples than any single blood alone.

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.

Longitudinal analysis of antibody decay in convalescent COVID-19 patients.

Determining the sustainability of antibodies targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for predicting immune response against the Coronavirus disease 2019 (COVID-19). To quantify the antibody decay rates among the varying levels of anti-nucleocapsid (anti-N) Immunoglobulin G (IgG) in convalescent COVID-19 patients and estimate the length of time they maintained SARS-CoV-2 specific antibodies, we have collected longitudinal blood samples from 943 patients over the course of seven months after their initial detection of SARS-CoV-2 virus by RT-PCR. Anti-N IgG levels were then quantified in these blood samples. The primary study outcome was the comparison of antibody decay rates from convalescent patients with high or low initial levels of antibodies using a mixed linear model. Additional measures include the length of time that patients maintain sustainable levels of anti-N IgG. Antibody quantification of blood samples donated by the same subject multiple times shows a gradual decrease of IgG levels to the cutoff index level of 1.4 signal/cut-off (S/C) on the Abbott Architect SARS-CoV-2 IgG test. In addition, this study shows that antibody reduction rate is dependent on initial IgG levels, and patients with initial IgG levels above 3 S/C show a significant 1.68-fold faster reduction rate compared to those with initial IgG levels below 3 S/C. For a majority of the donors naturally occurring anti-N antibodies were detected above the threshold for only four months after infection with SARS-CoV-2. This study is clinically important for the prediction of immune response capacity in COVID-19 patients.

Effects of Laser Beam Focusing Characteristics on Laser-Induced Breakdown Spectra.

The impact of altering laser focusing conditions on laser-induced breakdown spectroscopy experiments is investigated under ambient Earth laboratory and simulated Martian atmospheres. Experiments were performed in which the focal spot size was varied on a sample by altering the lens to sample distance with respect to targets of interest. Samples investigated include aluminum, copper, and steel. Specific neutral and ionic transitions of each sample were monitored. Atomic and ionic emissions show different intensity peak distributions along the varying lens to sample distance. Ionic species have peak emissions when laser plasma is initiated with a focused spot within the sample in ambient Earth laboratory air, while atomic emissions have peak intensities several millimeters deeper into a sample. In simulated Martian atmospheres, atomic emissions are observed to peak when the laser is focused within the sample, while ionic emissions have peak intensities when the laser is focused near the surface of a sample.

Diagnosis of Alzheimer's disease using laser-induced breakdown spectroscopy and machine learning.

Alzheimer's disease (AD) is a progressive incurable neurodegenerative disease and a major health problem in aging population. We show that the combined use of Laser-Induced Breakdown Spectroscopy (LIBS) and machine learning applied for the analysis of micro-drops of plasma samples of AD and healthy controls (HC) yields robust classification. Following the acquisition of LIBS spectra of 67 plasma samples from a cohort of 31 AD patients and 36 healthy controls (HC), we successfully diagnose late-onset AD (> 65 years old), with a total classification accuracy of 80%, a specificity of 75% and a sensitivity of 85%.

Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier.

Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of "dark signal" and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian.

Uncertainty of Integrated Intensity Following Line Profile Fitting of Multiline Spectra.

A novel method of determining the total uncertainty in the integrated intensity of fitted emission lines in multipeaked emission spectra is presented. The proposed method does not require an assumption of the type of line profile to be specified. The absolute difference between a fit and measured spectrum defines the uncertainty of the integrated signal intensity and is subsequently decomposed to determine the uncertainty of each peak in multiline fits. Decomposition relies on tabulating a weighting factor, which describes how each peak contributes to the total integral uncertainty. Applications of this method to quantitative approaches in laser-induced breakdown spectroscopy analysis are described.

Femtosecond Laser Ablation Synthesis of Aryl Functional Group Substituted Gold Nanoparticles.

Femtosecond laser ablation synthesis of gold-aryl nanoparticles in solution was explored. Laser irradiation of the yellow solution of diazonium tetrachloroaurate(III) salt [C8F17-4-C6H4N≡N]AuCl4 in acetonitrile formed ruby red color of gold-aryl nanoparticles without the need for external chemical reducing agent. X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), X-ray powder diffraction (XRD), and nanodrop UV-Vis spectroscopy were used in the nanoparticles characterization. XPS showed the presence of the core­shell and the formation of gold(0) oxidation state only. The nanoparticles size distribution estimated by TEM is dependent on the duration of laser irradiation. Longer irradiation time resulted in decreasing the nanoparticles size. UV-Vis studies in acetonitrile showed that the absorption of gold(III) at 310 nm vanished with a concomitant formation of a plasmon absorbance at 532 nm due to the formation of "embryonic" gold-aryl nanoparticles. The novelty of this work is the in situ conjugation of core­shell structure without the need for adjusting the conjugate/gold ratio, chemicals-free synthesis from reducing agents and surfactants, synthesis of nanoparticles using gold salts unlike the common ablation of flat metal surfaces, and the use of reactive [AuCl4]− counter-ion that permits the co-deposition of gold and conjugates. Released solvated electrons and hydrogen radicals are believed to induce the reduction reaction of the gold salts. Isolation of pure nanoparticles is important for further biomedical applications including cellular uptake.

Tag-femtosecond laser-induced breakdown spectroscopy for the sensitive detection of cancer antigen 125 in blood plasma.

Successful treatment of cancers requires detecting early signs of the disease. One promising way to approach this is to develop minimally invasive tests for the sensitive and specific detection of biomarkers in blood. Irrespective of the detection approach one uses, this remains a challenging task because biomarkers are typically present in low concentrations and there are signals that interfere strongly with prevailing compounds of human fluids. In this paper, we show that elemental encoded particle assay coupled with femtosecond laser-induced breakdown spectroscopy for simultaneous multi-elemental analysis can significantly improve biomarker detectability. An estimated near single molecule per particle efficiency of this method leads to sensitive detection of ovarian cancer biomarker CA125 in human blood plasma. This work opens new ways for earlier detection of cancers and for multiplex assay developments in various analytical applications from proteomics, genomics, and neurology fields.

Automatic classification of laser-induced breakdown spectroscopy (LIBS) data of protein biomarker solutions.

We perform multi-class classification of laser-induced breakdown spectroscopy data of four commercial samples of proteins diluted in phosphate-buffered saline solution at different concentrations: bovine serum albumin, osteopontin, leptin, and insulin-like growth factor II. We achieve this by using principal component analysis as a method for dimensionality reduction. In addition, we apply several different classification algorithms (K-nearest neighbor, classification and regression trees, neural networks, support vector machines, adaptive local hyperplane, and linear discriminant classifiers) to perform multi-class classification. We achieve classification accuracies above 98% by using the linear classifier with 21-31 principal components. We obtain the best detection performance for neural networks, support vector machines, and adaptive local hyperplanes for a range of the number of principal components with no significant differences in performance except for that of the linear classifier. With the optimal number of principal components, a simplistic K-nearest classifier still provided acceptable results. Our proposed approach demonstrates that highly accurate automatic classification of complex protein samples from laser-induced breakdown spectroscopy data can be successfully achieved using principal component analysis with a sufficiently large number of extracted features, followed by a wrapper technique to determine the optimal number of principal components.

Photothermal lens spectrometry measurements in highly turbid media.

We measured the photothermal lens signal in samples exhibiting high turbidity using a pump-probe scheme. We show that the photothermal lens signal properties remain nearly unchanged up to values of turbidity of 6 cm(-1) despite the signal reduction due to the decrease of excitation power associated to turbidity losses. The signal starts decreasing abruptly for values of turbidity larger than 6 cm(-1). Multiple light scattering yields a reduction of the temperature gradients, which results in a decrease of the effective signal. However, the signal-to-noise ratio remains above 50 for turbidity values of 9 cm(-1), which corresponds to a reduction of light transmission by more than four orders of magnitude. We report on the detection of the photothermal lens signal through a 2 mm layer of organic tissue with a signal-to-noise ratio of about 500. This technique appears promising for imaging applications in organic samples, which usually exhibit high turbidity for visible and near-infrared light.

Helium detection in gas mixtures by laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) has been evaluated as a tool for monitoring trace levels of helium in gas mixtures consisting mostly of hydrogen. Calibration data for helium in hydrogen was investigated at different helium concentration levels. At high concentrations of helium (>7.25%), the LIBS signal is quenched due to Penning ionization. The hydrogen alpha line (656.28 nm) was observed to broaden as the concentration of helium impurities in the hydrogen gas mixture increased. The helium line at 587.56 nm was selected as the analyte line for helium impurity detection. The effects of laser energy, the delay time between the laser pulse and data acquisition, and the gas pressure on the LIBS signal of helium were investigated to determine the optimum conditions for helium detection. The LIBS signal from the helium line at 587.56 nm shows good linear correlation with helium concentration for He concentrations below 1%. Thus, LIBS can be reliably used to detect the low levels of helium. The limit of detection for helium was found to be 78 ppm.

Optomechanical shape analysis using group theory.

We describe an optomechanical technique using a knife-edge, which is scanned spatially across a beam of light to identify shape-based irradiance. Symmetry groups are identified through linear and rotational scanning signatures of illuminated shapes. The scanning signature is used to classify the shape into a symmetry group. To demonstrate the shape analysis technique, we have classified basic geometric shapes, which belong to the orthogonal and dihedral symmetry groups O(2), D(2), D(3), and D(6).

Elemental analysis of laser induced breakdown spectroscopy aided by an empirical spectral database.

Laser induced breakdown spectroscopy (LIBS) is commonly used to identify elemental compositions of various samples. To facilitate this task, we propose the use of an elemental spectral library for single-pulsed, nanosecond LIBS in the spectral range 198-968 nm. This spectroscopic library is generated by measuring optical emissions from plasmas of 40 pure elements. To demonstrate the usefulness of the proposed database, we measure and analyze the LIBS spectra of pure iron and of ethanol and show that we identify these samples with a high degree of certainty.

Optomechanical integration method for finite integrals.

We present, for the first time to our knowledge, an optomechanical integration method for finite functions. This technique allows for the integration of any finite function by combining optical and mechanical principals. The integrated function can then be determined using curve fitting methods. Furthermore, the original function can be reproduced through numerical or analytical integration.