Analysis and classification of heterogeneous kidney stones using laser-induced breakdown spectroscopy (LIBS)

Conventional versus AI-based spectral data processing and classification approaches to enhance LIBS's analytical performance

Laser-Induced Breakdown Spectroscopy (LIBS) combined with Artificial Intelligence (AI) offers a powerful method for analyzing and comparing spectral data. This study presents a comparative analysis of conventional and AI-developed methods for processing and interpreting LIBS data, especially in forensic applications, focusing on toner sample discrimination. We propose a novel AI-developed approach that combines normalization, interpolation, and peak detection techniques to simplify LIBS spectral analysis without user preprocessing and easily identify unique spectral features. This method was compared with conventional principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), which are commonly used for LIBS data analysis. The AI-developed method demonstrated superior performance in discriminating between toner samples from various brands and models of printers and photocopiers. The quantitative evaluation of the performance of the AI-developed approach was performed using statistical analysis, including accuracy difference percentage, component-wise variance analysis, paired t-test, and cross-validation test. The results confirmed a significant improvement in accuracy with the AI-developed method compared to conventional approaches. This proposed work highlights the potential of AI in enhancing spectroscopic analysis for forensic applications, offering increased efficiency and accuracy in sample discrimination and classification. Additionally, it accelerates the analysis of LIBS data with no need for user preprocessing.

Analysis of Lithium Aging Using Machine Learning-Enhanced Spectroscopy Techniques

Lithium compounds such as lithium hydride (LiH) and lithium hydroxide (LiOH) have a wide range of industrial applications, but are highly reactive in environments with H2O and CO2. These reactions lead to the ingrowth of secondary lithium compounds, which can alter the homogeneity and affect the application of particular lithium chemicals. This study performed an exploratory analysis of different lithium compounds using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Machine learning models are trained on the recorded spectral data to discriminate emission features that differ between LiH, LiOH, and Li2CO3 to perform high-fidelity classification. Support vector machine classifiers yield perfect prediction accuracy between the three compounds with optimal training time. Multivariate methods are then used to produce regression models quantifying the ingrowth of LiOH in LiH. Performing a mid-level data fusion of selected LIBS and Raman features with partial least-squares regression produces the superlative model with a root mean square error of 2.5 wt% and a detection limit of 6.3 wt% .

Spectral characterization of renal calculi collected from population in downtown Madrid (Spain)

This paper reports on a comprehensive approach to characterize a set of kidney stones through various analytical techniques including ESEM-EDS, XRD, Raman, and CL spectroscopy, linked to an assessment of the patient's lifestyle and dietary habits. The use of these techniques can provide valuable insights into the underlying causes of stone formation and guide strategies for prevention and treatment. ESEM-EDS and XRD are commonly used techniques for kidney stone characterization due to their complementary nature, enabling the identification of a wide range of renal calculi. However, these techniques may not be sensitive enough to determine the detailed composition of the samples. In such cases, Raman and CL techniques can be used to provide more precise information about the chemical and structural composition of the stones. Raman spectroscopy, for example, can identify molecular phases observed under an optical microscope characterizing chemical compositions through vibrational modes associated with specific bonds. The CL spectral emission within the 250-850 nm range can also yield valuable information about the mineral phases, including the identification of structural crystallinity, hydrated molecules, Ca-OH bonds, and oxygen defects. By correlating spectral analyses with patient habits, this study identifies potential exogenous factors contributing to stone formation, including excess protein consumption, urinary bacterial infections, and oxalate-rich diets. This comprehensive approach provides a more complete understanding of the composition of kidney stones helping to personalized prevention and treatment strategies.

Environmental Conditions as Determinants of Kidney Stone Formation

Urolithiasis is a disease characterized by the presence of stones in the urinary tract, whether in the kidneys, ureters, or bladder. Its origin is multiple, and causes can be cited as hereditary, environmental, dietary, anatomical, metabolic, or infectious factors. A kidney stone is a biomaterial that originates inside the urinary tract, following the principles of crystalline growth, and in most cases, it cannot be eliminated naturally. In this work, 40 calculi from the Don Benito, Badajoz University Hospital are studied and compared with those collected in Madrid to establish differences between both populations with the same pathology and located in very different geographical areas. Analysis by cathodoluminescence offers information on the low crystallinity of the phases and their hydration states, as well as the importance of the bonds with the Ca cation in all of the structures, which, in turn, is related to environmental and social factors of different population groups such as a high intake of proteins, medications, bacterial factors, or possible contamination with greenhouse gases, among other factors.

LIBS and Raman spectroscopy in tandem with machine learning for interrogating weatherization of lithium hydride

Lithium compounds such as lithium hydride (L i H) and anhydrous lithium hydroxide (L i O H) have various applications in industry but are highly reactive when exposed to moisture and C O ₂. These reactions create new molecular compounds that degrade applications. Environmental conditions such as temperature and moisture are examples of environmental conditions that are of interest for these reactions. To interrogate the effects of such weatherization, experiments were conducted in an environmental chamber (Plas-Labs 890-THC glove box) employing a pulsed laser and an echelle spectrograph in a novel single setup to conduct both Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) in tandem. These measurements in conjunction with data fusion and machine learning techniques are used to develop training and testing of environmental conditioning of Li compounds. Modeling of environmental characterizations involving lithium-based compounds enabled by the presented measurements and analytical techniques has significant implications on industrial technologies, such as batteries, and other nuclear applications.

Two-Step Partial Least Squares-Discriminant Analysis Modeling for Accurate Classification of Edible Sea Salt Products Using Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has been widely applied to material classification in various fields, and partial least squares-discriminant analysis (PLS-DA) is one of the frequently used classical multivariate statistics to construct classification models based on the LIBS spectra. However, classification accuracy of the PLS-DA model is sensitive to the number of classes and their similarities. Considering this characteristic of PLS-DA, we suggest a two-step PLS-DA modeling approach to improve the classification accuracy. This strategy was demonstrated for a six-class problem in which six commercial edible sea salts produced in Japan, South Korea, and France are classified using their LIBS spectra. At the first step, test spectra were sorted into four classes and one extended class, composed of the two other most confusing classes, and then the test spectra in the extended class were further classified into each of the two constituent classes which were modeled separately from the other four classes. This two-step classification has been found to remarkably improve the PLS-DA classification accuracy by maximizing the difference between the confusing classes in the second-step modeling.

Detection of the mineral constituents in human renal calculi by vibrational spectroscopic analysis combined with allied techniques Powder XRD, TGA, SEM, IR imaging and TXRF

Detection of the mineral constituents in a batch of 310 samples of human urinary calculi (kidney stones-235 and bladder stones-75) combined with a semi-quantitative analysis has been presented on the basis of Fourier Transform based IR and Raman spectral measurements. Some of the observed characteristic IR and Raman bands have been proposed as 'Marker Bands' for the most reliable identification of the constituents. A detailed vibrational spectral analysis combined with a DFT level calculation for the functional groups in Calcium Oxalate Monohydrate (COM), Magnesium Ammonium Phosphate Hexahydrate (MAPH), Calcium Hydrogen Phosphate Dihydrate (CHPD), Penta-Calcium Hydroxy-Triphosphate (PCHT) and Uric Acid (UA) has been proposed. It has been shown that the identified mineral constituents as major or minor components can be deduced from the application of Lambert-Beer law of radiation absorption and results are in agreement with quantitative Spectral Data base. This simple method has the potential to be integrated into the management of Urolithiasis, a process of forming renal calculi in the kidney, bladder and/or urethra. Employment of powder XRD, TGA, SEM, TXRF and IR Imaging techniques has provided additional support for the proposed foolproof identification of the mineral constituents. Among the mineral constituents, Calcium Oxalate Monohydrate, Calcium Oxalate Dihydrate or their mixture account for 85% of the total number of samples; the remaining 15% and 5% samples contain Phosphate and Uric acid stones respectively.

Lithium Isotope Measurement Using Laser-Induced Breakdown Spectroscopy and Chemometrics

Laser-induced breakdown spectroscopy (LIBS) is a technique capable of portable, quantitative elemental analysis; however, quantitative isotopic determination of samples in situ has not yet been demonstrated. This research demonstrates the ability of LIBS to quantitatively determine concentrations of ⁶Li in solid samples of lithium hydroxide monohydrate in a nominally 40 mTorr argon environment using chemometrics. Three chemometric analysis techniques (principal component regression, partial least squares regression, and neural networks analysis) are applied to spectra collected using a spectrometer with modest resolving power (λ/Δλ ≈ 27 000). This analysis suggests that bulk lithium isotopic assay can be determined using LIBS to within a 95% confidence interval in minutes to an hour for enrichment levels ranging from 3% to 85%. This has direct applications for the nuclear safeguards and geological exploration communities and others that desire a portable, stable isotope analytical technique. Additionally, isotope-specific self-absorption of atomic emission in a laser-produced plasma is observed for the first time.

Laser induced breakdown spectroscopy with machine learning reveals lithium-induced electrolyte imbalance in the kidneys

Lithium is a major psychiatric medication, especially as long-term maintenance medication for Bipolar Disorder. Despite its effectiveness, lithium has side-effects, such as on renal function. In this study, lithium was administered to adult rats. This animal model of renal function was validated by measuring blood lithium, urea nitrogen (BUN), and thyroxine (T₄) using inductively-coupled plasma mass spectrometry and enzyme-linked immunosorbent assay. The kidneys were analyzed by laser induced breakdown spectroscopy (LIBS) with 1064 nm ablation and 300-900 nm detection. Principal components analysis (PCA), radial visualization, and random forest classification were performed on the LIBS spectra for multi-element prediction and classification. Lithium at 0.34 mmol/L was detected in the blood of lithium treated subjects only. BUN was increased (6.6 vs. 5.3 mmol/L) and T₄ decreased (58.12 vs. 51.4 mmol/L) in the blood of lithium subjects compared with controls, indicating renal abnormalities. LIBS detected lithium at 2.3 mmol/kg in the kidneys of lithium subjects only. Calcium was also observed to be reduced in lithium subjects, compared with controls. Subsequent PCA observed a change in the balance of sodium and potassium in the kidneys. These are key electrolytes in the body. Importantly, partial least squares regression showed that standard clinical measurements, such as the blood tests, can be used to predict kidney electrolyte measurements, which typically cannot be performed in humans. Overall, lithium accumulates in the kidneys and adversely affects renal function. The effects are likely related to electrolyte imbalance. LIBS with machine learning analysis has potential to improve clinical management of renal side-effects in patients on lithium medication.

Analysis of stones formed in the human gall bladder and kidney using advanced spectroscopic techniques

Stone diseases (gallstones and kidney stones) are extremely painful and often cause death. The prime aim of biomedical research in this area has been determination of factors resulting in stone formation inside the gallbladder and urinary tract. Many theories have been put forward to explain the mechanism of stone formation and their growth; however, their complete cycle of pathogenesis is still under debate. Several factors are responsible for stone formation; however, much emphasis is placed on the determination of elemental and molecular composition of the stones. In the present review article, we describe different kinds of spectroscopic techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray fluorescence (XRF) spectroscopy, time-of-flight secondary ion mass spectrometry (TOF-SIMS), and laser-induced breakdown spectroscopy (LIBS) and highlight their use in the analysis of stone diseases. We have summarized work done on gallstones and kidney stones using these advanced techniques particularly over the last 10 years. We have also briefly elaborated the basics of stone formations inside the human body and their complications for a better understanding of the subject.

Laser-induced breakdown spectroscopy-Raman: An effective complementary approach to analyze renal-calculi

Presence of renal-calculi (kidney stones) in human urethra is being increasingly diagnosed over the last decade and is considered as one of the most painful urological disorders. Accurate analysis of such stones plays a vital role in the evaluation of urolithiasis patients and in turn helps the clinicians toward exact etiologies. Two highly complementary laser-based analytical techniques; laser-induced breakdown spectroscopy (LIBS) and micro-Raman spectroscopy have been used to identify the chemical composition of different types of renal-calculi. LIBS explores elemental characteristics while Raman spectroscopy provides molecular details of the sample. This complete information on the sample composition might help clinicians to identify the key aspects of the formation of kidney stones, hence assist in therapeutic management and to prevent recurrence. The complementarity of both techniques has been emphasized and discussed. LIBS spectra of different types of stones suggest the probable composition of it by virtue of the major, minor and trace elements detected from the sample. However, it failed to differentiate the crystalline form of different hydrates of calcium oxalate stone. This lacuna was overcome by the use of Raman spectroscopy and these results are compared with conventional chemical analysis.

Laser-induced breakdown spectroscopy is a reliable method for urinary stone analysis

OBJECTIVE

We compared laser-induced breakdown spectroscopy (LIBS) with the traditionally used and recommended X-ray diffraction technique (XRD) for urinary stone analysis.

MATERIAL AND METHODS

In total, 65 patients with urinary calculi were enrolled in this prospective study. Stones were obtained after surgical or extracorporeal shockwave lithotripsy procedures. All stones were divided into two equal pieces. One sample was analyzed by XRD and the other by LIBS. The results were compared by the kappa (κ) and Spearman's correlation coefficient (rho) tests.

RESULTS

Using LIBS, 95 components were identified from 65 stones, while XRD identified 88 components. LIBS identified 40 stones with a single pure component, 20 stones with two different components, and 5 stones with three components. XRD demonstrated 42 stones with a single component, 22 stones with two different components, and only 1 stone with three different components. There was a strong relationship in the detection of stone types between LIBS and XRD for stones components (Spearman rho, 0.866; p<0.001). There was excellent agreement between the two techniques among 38 patients with pure stones (κ index, 0.910; Spearman rho, 0.916; p<0.001).

CONCLUSION

Our study indicates that LIBS is a valid and reliable technique for determining urinary stone composition. Moreover, it is a simple, low-cost, and nondestructive technique. LIBS can be safely used in routine daily practice if our results are supported by studies with larger numbers of patients.

Rapid elemental analysis and provenance study of Blumea balsamifera DC using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was applied to perform a rapid elemental analysis and provenance study of Blumea balsamifera DC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were implemented to exploit the multivariate nature of the LIBS data. Scores and loadings of computed principal components visually illustrated the differing spectral data. The PLS-DA algorithm showed good classification performance. The PLS-DA model using complete spectra as input variables had similar discrimination performance to using selected spectral lines as input variables. The down-selection of spectral lines was specifically focused on the major elements of B. balsamifera samples. Results indicated that LIBS could be used to rapidly analyze elements and to perform provenance study of B. balsamifera.

Kidney stone analysis techniques and the role of major and trace elements on their pathogenesis: a review

Kidney stone disease is a polygenic and multifactorial disorder with a worldwide distribution, and its incidence and prevalence are increasing. Although significant progress has been made in recent years towards identifying the specific factors that contribute to the formation of kidney stone, many questions on the pathogenesis of kidney stones remain partially or completely unanswered. However, none of the proposed mechanisms specifically consider the role(s) of the trace elements and, consequently, the contribution of trace constituents to the pathogenesis of kidney stones remains unclear and under debate. The findings of some studies seem to support a role for some major and trace elements in the initiation of stone crystallization, including as a nucleus or nidus for the formation of the stone or simply as a contaminant of the stone structure. Thus, the analysis of kidney stones is an important component of investigations on nephrolithiasis in order to understand the role of trace constituents in the formation of kidney stones and to formulate future strategies for the treatment and prevention of stone formation and its recurrence. The aim of this review is to compare and evaluate the methods/procedures commonly used in the analysis of urinary calculi. We also highlight the role of major and trace elements in the pathogenesis of kidney stones.