Direct spectral analysis of tea samples using 266 nm UV pulsed laser-induced breakdown spectroscopy and cross validation of LIBS results with ICP-MS

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques

Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance. The knowledge of soil unconfined compressive strength (UCS) enables engineers to identify the behaviors of a particular soil and propose effective solutions to given geotechnical problems. However, the experimental techniques involved in the measurements of soil UCS are incredibly expensive and time-consuming. In this work, we develop a pioneering technique to estimate the soil unconfined compressive strength using artificial intelligent methods based on the spectra obtained from the LIBS system. Decision tree regression (DTR) and support vector regression learners were initially employed, and consequently, the adaptive boosting method was applied to improve the performance of the two single learners. The prediction power of the established models was determined using the standard performance evaluation metrics such as the root-mean-square error, CC between the predicted and actual soil UCS values, mean absolute error, and R² score. Our results revealed that the boosted DTR exhibited the highest coefficient of correlation of 99.52% and an R² value of 99.03% during the testing phase. To validate the models, the UCS values of soils stabilized with lime and cement were predicted with an optimum degree of accuracy, confirming the models' suitability and generalization strength for soil UCS investigations.

Quantitative analysis of human hairs and nails

Hair and nails are human biomarkers capable of providing a continuous assessment of the concentrations of elements inside the human body to indicate the nutritional status, metabolic changes, and the pathogenesis of various human diseases. Laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF) spectrometry are robust and multi-element analytical techniques able to analyze biological samples of various kinds for disease diagnosis. The primary objective of this review article is to focus on the major developments and advances in LIBS and XRF for the elemental analysis of hair and nails over the last 10-year period. The developments in the qualitative and quantitative analyses of human hair and nail samples are discussed in detail, with special emphasis on the key aspects of elemental imaging and distribution of essential and non-essential elements within the hair and nail tissue samples. Microchemical imaging applications by LIBS and XRF (including micro-XRF and scanning electron microscopy, SEM) are also presented for healthy as well as diseased tissue hair and nail samples in the context of disease diagnosis. In addition, main challenges, prospects, and complementarities of LIBS and XRF toward analyzing human hair and nails for disease diagnosis are also thoroughly discussed here.

Towards a Standardized Approach for the Geographical Traceability of Plant Foods Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Principal Component Analysis (PCA)

This paper presents a systematic literature review focused on the use of inductively coupled plasma mass spectrometry (ICP-MS) combined with PCA, a multivariate technique, for determining the geographical origin of plant foods. Recent studies selected and applied the ICP-MS analytical method and PCA in plant food geographical traceability. The collected results from many previous studies indicate that ICP-MS with PCA is a useful tool and is widely used for authenticating and certifying the geographic origin of plant food. The review encourages scientists and managers to discuss the possibility of introducing an international standard for plant food traceability using ICP-MS combined with PCA. The use of a standard method will reduce the time and cost of analysis and improve the efficiency of trade and circulation of goods. Furthermore, the main steps needed to establish the standard for this traceability method are reported, including the development of guidelines and quality control measures, which play a pivotal role in providing authentic product information through each stage of production, processing, and distribution for consumers and authority agencies. This might be the basis for establishing the standards for examination and controlling the quality of foods in the markets, ensuring safety for consumers.

Laser-Ablated SiO2:TiO2 Plasma Optical Emission Spectroscopy

Laser ablation of sintered SiO2:TiO2 targets using Nd:YAG lasers at fundamental (1064[Formula: see text]nm) harmonic generation in the air has been studied using optical emission spectroscopy. Exploring the spatial fluctuations in electron temperature ([Formula: see text]) and electron density ([Formula: see text]), there is a discussion of how laser energy affects electron temperature ([Formula: see text]) and electron density ([Formula: see text]). Laser energy has also been shown to affect the intensity and velocity of neutral and ion species. Using existing data and theory, the findings were confirmed following the local thermodynamic equilibrium (LTE) hypothesis. Plasma properties such as Debye length ([Formula: see text]), ([Formula: see text]) and plasma frequency ([Formula: see text]) were also studied in this study. Using a laser, we found that all plasma parameters were affected. In addition, the calculated inverse Bremsstrahlung absorption coefficients ([Formula: see text]) were altered.

Application of Laser-Induced Breakdown Spectroscopy and Chemometrics for the Quality Evaluation of Foods with Medicinal Properties: A Review

Laser-induced Breakdown Spectroscopy (LIBS) is becoming an increasingly popular analytical technique for characterizing and identifying various products; its multi-element analysis, fast response, remote sensing, and sample preparation is minimal or nonexistent, and low running costs can significantly accelerate the analysis of foods with medicinal properties (FMPs). A comprehensive overview of recent advances in LIBS is presented, along with its future trends, viewpoints, and challenges. Besides reviewing its applications in both FMPs, it is intended to provide a concise description of the use of LIBS and chemometrics for the detection of FMPs, rather than a detailed description of the fundamentals of the technique, which others have already discussed. Finally, LIBS, like conventional approaches, has some limitations. However, it is a promising technique that may be employed as a routine analysis technique for FMPs when utilized effectively.

Rapid quantitative characterization of tea seedlings under lead-containing aerosol particles stress using Vis-NIR spectra

The problem of excessive lead content in tea has become more and more serious with the development of society and industry. This paper investigated the ability of visible and near-infrared (Vis-NIR) spectroscopy to evaluate foliar lead uptake by tea plants through simulating real air pollution. Lead content of tea leaves in different treatment groups during stress time was measured by inductively coupled plasma mass spectrometry (ICP-MS). It was determined that stomata can be a channel for lead particles in the air and most of the lead entering through the stomata accumulates in the leaves. The spectral variation of treated samples was measured, and it was found that a combination of partial least squares-discriminant analysis (PLS-DA) and spectral responses can perfectly classify the tea samples under different lead concentrations stress with an overall accuracy of 0.979. Then the Vis-NIR spectra were used for fast monitoring physiological and biochemical indicators in tea leaves under atmospheric deposition. Relevant spectra pretreatment methods and characteristic wavelength selection approaches were evaluated for quantitative analysis and then optimal prediction models to instantly detect quality indicators in tea samples were built. Among predictive models, PLS had the best results (RMSE = 0.139 mg/g, 0.663 mmol/g, and 1.494 μmol/g) for the prediction of chlorophyll a (Chl-a), ascorbic acid (ASA), and glutathione (GSH), respectively. Also, principal component regression (PCR) gave the best results (RMSE = 0.053 mg/g, 0.024 mg/g, and 0.011%) for prediction of chlorophyll b (Chl-b), carotenoid (Car) and moisture content (MC), respectively. Results of this study can be applied for developing an effective and reliable approach for monitoring atmospheric deposition in plants.

Optimizing the quantitative analysis of solid biomass fuel properties using laser induced breakdown spectroscopy (LIBS) coupled with a kernel partial least squares (KPLS) model

The rapid analysis of fuel properties is important for the utilization of solid biomass due to its great variation in feedstock. Laser-induced breakdown spectroscopy (LIBS) technology combined with quantitative analysis models can be used for this analysis. Most existing prediction models used in LIBS for fuel property analysis are linear methods, such as the partial least squares (PLS) model, which fail to reflect the non-linear relationships between the LIBS spectrum and the fuel property index being predicted. In the present work, LIBS data combined with a kernel partial least squares (KPLS) method are used to analyze the gross calorific value, and the volatile matter, ash and fixed carbon content of the solid biomass fuel. Quantitative analysis performance of the KPLS model was compared to that of the widely used PLS method, with the results showing some improvements. The KPLS model was further improved using three data normalization methods (i.e., C internal standardization, total intensity standardization and standard normal variate). The best quantitative analysis results of the volatile matter and ash content were obtained when the KPLS model was combined with C internal standardization, with root mean square errors of prediction (RMSEP) of 1.365% and 0.290%, and average standard deviations (ASD) of 0.277% and 0.080%, respectively. The best quantitative analysis results of the gross calorific value and fixed carbon content were obtained when using KPLS without normalization. The RMSEP and ASD of the gross calorific value and fixed carbon content were 0.198 MJ kg^-1 and 0.746%, and 0.070 MJ kg^-1 and 0.111% respectively.

Al and Si quantitative analysis in aqueous solutions by LIBS method

In the paper results of Laser-Induced Breakdown Spectroscopy (LIBS) measurements of Al and Si content in aqueous solutions are presented. Calibration curves exhibit good linearity and satisfying R² coefficients of determination (over 0.99). The calibration procedures were next used for the determination of Al and Si concentration in biological material transferred into water solutions of unknown Al and Si content. Obtained data were verified by the AAS method (atomic absorption spectrometry). Measurement results showed good conformity. The LIBS spectra were also processed using Factorial Analysis (FA). It was found for Si samples that instead of a classic approach based on the selection of a particular spectral line for the calibration the FA makes possible the use of entire spectra to obtain quantitative information on the investigated material.

Elemental analysis of fish feed by laser-induced breakdown spectroscopy

In this study, the potential of laser-induced breakdown spectroscopy (LIBS) as an efficient multi-elemental quantification tool for fish feed is determined. A particular focus of this paper is total chromium, an essential element that has the potential to be toxic and carcinogenic. In total six elements, four macro-elements (Ca, Fe, K and Mg) and two micro-elements (Cr and Rb), were modelled using LIBS spectra of aquafeed samples. Reference analysis was conducted via inductively coupled plasma mass spectrometry (ICP-MS) and showed good agreement with LIBS predictions. These results provide evidence that LIBS has the potential to be utilized in the field as a real-time screening tool for establishing the elemental composition of a range of fish feeds.

Detection of nutritional and toxic elements in Pakistani pepper powders using laser induced breakdown spectroscopy

In the current study, we applied laser induced breakdown spectroscopy (LIBS) to determine the elemental distribution of nutritional and trace heavy metals in pepper powders available in Pakistan using the standard calibration curve of laser induced breakdown spectroscopic (CC-LIBS) technique. The samples were found to contain elements such as K, Ca, Mg, Na, Fe, Zn, Al, Pb, Cu, and Cr. In addition, we also identified the atmospheric H-alpha line of hydrogen in the spectra, which was used to estimate the electron number density. To achieve a highly sensitive LIBS system to determine the trace amounts of nutritional and toxic metals in the pepper powders and to achieve the best limit of detection, the LIBS system was optimized by studying the dependency of the LIBS signal intensity on laser irradiance, variation in spacing between the focusing lens and target, as well as on the gate width. To justify the results of the LIBS study, we also determined the concentration of similar (duplicate) peppers by digesting the samples in appropriate solvents using a standard method such as inductively coupled plasma-optical emission spectrometry (ICP-OES). The minimum detection limit was obtained for trace heavy metals from the calibration curves. The results of LIBS displayed noteworthy conformity with those acquired from the ICP-OES analysis. The results of both the techniques clearly revealed that K was the most abundant element in all the peppers, followed by Ca, Mg, Na, Fe, Al, Pb, Zn, Cu, and Cr. The relative accuracy of our LIBS system for different species as compared with the ICP technique was in the range of 0.08-0.3 at 2.5% error confidence. Conclusively, the present work demonstrated the suitability of the LIBS technique due to its rapid, non-destructive, and eco-friendly approach for food security.

Laser-induced breakdown spectroscopy (LIBS): a novel technology for identifying microbes causing infectious diseases

With the advent of improved experimental techniques and enhanced precision, laser-induced breakdown spectroscopy (LIBS) offers a robust tool for probing the chemical constituents of samples of interest in biological sciences. As the interest continues to grow rapidly, the domain of study encompasses a variety of applications vis-à-vis biological species and microbes. LIBS is basically an atomic emission spectroscopy of plasma produced by the high-power pulsed laser which is tightly focused on the surface of any kinds of target materials in any phase. Due to its experimental simplicity, and versatility, LIBS has achieved its high degree of interest particularly in the fields of agricultural science, environmental science, medical science, forensic sciences, and biology. It has become a strong and sensitive elemental analysis tool as compared to the traditional gold standard techniques. As such, it offers a handy, rapid, and flexible elemental measurement of the sample compositions, together with the added benefits of less cumbersome sample preparation requirements. This technique has extensively been used to detect various microorganisms, extending the horizon from bacteria, molds, to yeasts, and spores on surfaces, while also being successful in sensing disease-causing viruses. LIBS-based probe has also enabled successful detection of bacteria in agriculture as well. In order for good quality processing of food, LIBS is also being used to detect and identify bacteria such as Salmonella enteric serovar typhimurium that causes food contamination. Differences in soil bacteria isolated from different mining sites are a very good indicator of relative environmental soil quality. In this connection, LIBS has effectively been employed to discriminate both the inter- and intra-site differences of the soil quality across varying mining sites. Therefore, this article summarizes the basic theory and use of LIBS for identifying microbes causing serious agricultural and environmental infectious diseases.

Optimized laser-induced breakdown spectroscopy for the determination of high toxic lead in edible colors

An optimized laser-induced breakdown spectroscopy (LIBS) system was utilized as a robust tool for the detection of high toxic lead (Pb) in often used edible colors available in local markets. Edible colors are extensively used in different food stuffs and sweet dish items. To attain a highly sensitive LIBS system for the determination of trace amounts of toxic Pb in edible colors and to achieve the best detection limit, various parametric dependence studies were performed. The quantitative determinations were accomplished under the condition of local thermodynamic equilibrium in terms of optically thin plasma. Besides toxic Pb, other elements like Ca, Mg, Fe, and Na were also detected in edible colors. To estimate the concentration of toxic lead in edible colors, calibration curves were plotted by preparing standard samples of different lead concentrations in an edible colors matrix. Results of the LIBS technique were compared with the outcomes of the same samples studied using a standard analytical technique of inductively coupled plasma-optical emission spectroscopy to confirm the contemporary LIBS setup. The color samples were found above the safe permissible level due to the mass of toxic lead. Limit of detection of the LIBS setup was found at 0.86+/-0.03  ppm for toxic lead present in edible color samples.

Development of hybrid extreme learning machine based chemo-metrics for precise quantitative analysis of LIBS spectra using internal reference pre-processing method

Laser induced breakdown spectroscopy (LIBS) is a versatile spectroscopic technique that requires little or no sample preparation and capable of simultaneous elemental sample analysis. Quantitative analysis of its spectra has been a major challenge due to self-absorption of the emitted radiation during plasma cooling and inadequate description of non-linear complex interactions taking place in the laser induced plasma. This work presents a novel chemo-metric tool, extreme learning machine (ELM) and its hybrid HHELM (homogenously hybridized ELM), for the first time in modeling the complex interactions of laser induced plasma and quantification of LIBS spectra. Internal reference preprocessing (IRP) method is also proposed as a novel method of enhancing the performance of ELM based chemo-metrics. Since the proposed chemo-metrics (ELM and HHELM) determine their input weights as well as their hidden biases in a random manner, ELM and HHELM are respectively hybridized with gravitational search algorithm (GSA) for optimization of the number of hidden neurons. Effect of IRP, obtained by normalizing the emission spectra intensities with the emission intensity that has highest upper level excitation energy and lowest transition probability, on the performance of the proposed GSA-ELM and GSA-HHELM chemo-metrics is investigated. The proposed models are implemented using spectra of seven bronze standard samples. Chemo-metrics with IRP (GSA-ELM-IRP and GSA-HHELM-IRP) show better generalization performance than those without IRP (GSA-ELM-WIRP and GSA-HHELM-WIRP) while GSA-HHELM based chemo-metrics perform better than their counterparts. The outstanding performance demonstrated by the proposed chemo-metrics and their self-absorption correction ability would definitely widen the applicability of LIBS and improve its precision for the quantitative analysis.

Comparative Study of the Detection of Chromium Content in Rice Leaves by 532 nm and 1064 nm Laser-Induced Breakdown Spectroscopy

Fast detection of toxic metals in crops is important for monitoring pollution and ensuring food safety. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the chromium content in rice leaves. We investigated the influence of laser wavelength (532 nm and 1064 nm excitation), along with the variations of delay time, pulse energy, and lens-to-sample distance (LTSD), on the signal (sensitivity and stability) and plasma features (temperature and electron density). With the optimized experimental parameters, univariate analysis was used for quantifying the chromium content, and several preprocessing methods (including background normalization, area normalization, multiplicative scatter correction (MSC) transformation and standardized normal variate (SNV) transformation were used to further improve the analytical performance. The results indicated that 532 nm excitation showed better sensitivity than 1064 nm excitation, with a detection limit around two times lower. However, the prediction accuracy for both excitation wavelengths was similar. The best result, with a correlation coefficient of 0.9849, root-mean-square error of 3.89 mg/kg and detection limit of 2.72 mg/kg, was obtained using the SNV transformed signal (Cr I 425.43 nm) induced by 532 nm excitation. The results indicate the inspiring capability of LIBS for toxic metals detection in plant materials.

Detection of heavy metal Cd in polluted fresh leafy vegetables by laser-induced breakdown spectroscopy

In seeking a novel method with the ability of green analysis in monitoring toxic heavy metals residue in fresh leafy vegetables, laser-induced breakdown spectroscopy (LIBS) was applied to prove its capability in performing this work. The spectra of fresh vegetable samples polluted in the lab were collected by optimized LIBS experimental setup, and the reference concentrations of cadmium (Cd) from samples were obtained by conventional atomic absorption spectroscopy after wet digestion. The direct calibration employing intensity of single Cd line and Cd concentration exposed the weakness of this calibration method. Furthermore, the accuracy of linear calibration can be improved a little by triple Cd lines as characteristic variables, especially after the spectra were pretreated. However, it is not enough in predicting Cd in samples. Therefore, partial least-squares regression (PLSR) was utilized to enhance the robustness of quantitative analysis. The results of the PLSR model showed that the prediction accuracy of the Cd target can meet the requirement of determination in food safety. This investigation presented that LIBS is a promising and emerging method in analyzing toxic compositions in agricultural products, especially combined with suitable chemometrics.

Quantification of copper content with laser induced breakdown spectroscopy as a potential indicator of offal adulteration in beef

Laser induced breakdown spectroscopy (LIBS) is an emerging technique in the field of food analysis which provides various advantages such as minimal sample preparation, chemical free, rapid detection, provision of spatial information and portability. In this study, LIBS was employed for quantitative analysis of copper content in minced beef samples spiked with beef liver over three independent batches. Copper content was determined with graphite furnace atomic absorption spectroscopy (GFAAS) in order to obtain reference values for modelling. Partial least square regression (PLSR) was performed to build a calibration and validation model. A calibration model with a high R_cv² of 0.85 and a RMSECV of 43.5ppm was obtained, confirming a good fit for the model. The validation model showed a good prediction accuracy with a high R_p² of 0.85 and RMSEP of 36.8ppm. Moreover, on a further study to evaluate the spatial capabilities, LIBS was able to successfully map copper content within a pellet, indicating the suitability of LIBS to provide spatial information and therefore potential use on heterogeneous samples. Overall, it can be concluded that LIBS combined with chemometrics demonstrates potential as a quality monitoring tool for the meat processing industry.

Melted Paraffin Wax as an Innovative Liquid and Solid Extractant for Elemental Analysis by Laser-Induced Breakdown Spectroscopy

This work proposes a new development in the use of melted paraffin wax as a new extractant in a procedure designed to aggregate the advantages of liquid phase extraction (extract homogeneity, fast, and efficient transfer, low cost and simplicity) to solid phase extraction. As proof of concept, copper(II) in aqueous samples was converted into a hydrophobic complex of copper(II) diethyldithiocarbamate and subsequently extracted into paraffin wax. Parameters which affect the complexation and extraction (pH, DDTC, and Triton X-100 concentration, vortex agitation time and complexation time) were optimized in a univariate way. The combination of the extraction proposed procedure with laser-induced breakdown spectroscopy allowed the precise copper determination (coefficient of variation = 3.1%, n = 10) and enhanced detectability because of the concentration factor of 18 times. A calibration curve was obtained with a linear range of 0.50-10.00 mg L^-1 (R² = 0.9990, n = 7), LOD = 0.12 mg L^-1, and LOQ = 0.38 mg L^-1 under optimized conditions. An extraction procedure efficiency of 94% was obtained. The accuracy of the method was confirmed through the analysis of a reference material of human blood serum, by the spike and recovery trials with seawater, tap water, mineral water, and alcoholic beverages and by comparing with those results obtained by graphite furnace atomic absorption spectrometry.