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Advancing Agriculture For Future Generations

An Interview with Felipe Bachion de Santana

Monitoring Soil Quality Using MIR and NIR Spectral Models 

Polymer Characterization of Plastic Litter Using ATR-FT-IR Spectroscopy

Aaron Acevedo is an assistant editor for LCGC and Spectroscopy at MJH Life Sciences.

Scientists from Moscow, Russia are studying the dynamics of lithium plasma under laser-induced breakdown spectroscopy using a custom system made using 3DLINE code, a type of code used to model laser explosions to produce a source of radiation with specified properties (1). Their work was published in 

Spectrochimica Acta Part B: Atomic Spectroscopy

Red laser beam light effect isolated on transparent background. Neon light ray. | Image Credit: © Likanaris - stock.adobe.com

Laser-induced breakdown spectroscopy (LIBS) is a popular technique for material analysis, consisting of a material surface being irradiated by a laser pulse, then becoming ionized so that the produced plasma emits radiation that is registered by a spectrometer.

This technique is used with different types of machines including tokamak reactors, which confine plasma using magnetic fields, allowing plasma to achieve conditions necessary for fusion (2). LIBS is actively tested on existing tokamaks, including those with lithium (Li)-covered surfaces, with different techniques used in analysis. One such technique is the Saha-Boltzmann equation, which can be used to determine both plasma parameters and the material composition. Applying this technique for interpreting LIBS data involves the assumption of local thermodynamic equilibrium (LTE) and plasma transparency for observed spectral lines.

The scientists created a hybrid model using different types of code, such as 3DLINE, to describe Li plasma formation, expansion, and radiation from a plasma plume. According to the scientists, “3DLINE uses more self-consistent model for liquid-plasma transition based on the equation of state produced by the code FEOS” (1). This led to the creation of a model with barely any free parameters, with an exception for laser pulse characteristics. To validate this approach, different LIBS experiments were performed with a large mass-monochromator device.

Calculations showed there can be easy means of violating transparency conditions for Li LIBS plasmas, which usually absorb up to 97% of 671 nm (Li I) of line radiation. As for other easily absorbed lines, such as 548 nm (Li II) and 610 nm (Li I) lines, remained less affected by radiation absorption. It was also noticed that sharper focusing of the laser beam onto the surface led to more transparent plasma. These findings, the scientists believe, can help future LIBS users when characterizing Li-coated surfaces.

(1) Krukovskiy, A. Y.; Novikov, V. G.; Tsygvintsev, I. P. 3DLINE code: numerical simulation of three-dimensional non-stationary radiation gas dynamical problems. 

https://keldysh.ru/papers/2013/prep2013_20.pdf

(2) Marenkov, E.D.; Tsygvintsev, I. P.; Kim, D. A.; Grushin, A. S.; Efimov, N. E.; Sinelnikov, D. N.; Gasparyan, Y. M. Dynamics of lithium plasma in laser-induced breakdown spectroscopy. 

https://doi.org/10.1016/j.sab.2023.106822

https://www.energy.gov/science/doe-explainstokamaks

Articles

Scientists from Moscow, Russia studied the dynamics of lithium plasma under laser-induced breakdown spectroscopy using a custom system made using 3DLINE code.

New System Created to Study Relationship Between Lithium Plasma and LIBS

 on November 20, 2023, researchers from Technische Universität Dresden in Germany, led by corresponding author and faculty member Martin Müller, have delved into the determination of pKa values for protonated mono and polyamines in concentrated solutions, utilizing Fourier transform infrared (FT-IR) titration and classical potentiometric (POT) titration techniques. The article, titled "Determination of the pKa Value of Protonated Mono and Polyamine in Solution Using Fourier Transform Infrared Titration," yields information on the intriguing behavior of propanolamine hydrochloride (PAMH) and poly(allylamine hydrochloride) (PAAMH) under varying pH conditions (1).

The research employed in situ attenuated total reflection (ATR)-FT-IR spectra on PAMH and PAAMH solutions, starting with their fully protonated forms and incrementally adjusting the pH with sodium hydroxide addition. By monitoring the variation of diagnostic infrared (IR) bands, particularly the δ(NH

) band, the dissociation process of the NH

 groups was tracked. The dissociation degree αIR of the ammonium groups was determined from the decrease of the most intense band area A.

The study innovatively plotted pH against αIR, fitting the curve with a modified Henderson–Hasselbalch function pH = pK

 + B log (αIR/1 – αIR), enabling the extraction of crucial parameters—pK

 and cooperativity factor B. The obtained pKa values from FT-IR titration were qualitatively aligned with those from POT titration, providing comprehensive insights into the dissociation processes of these chemical species (1).

The article systematically explores and discusses quantitative deviations in pK

 values between polyelectrolyte (PEL) and respective monoelectrolyte. The researchers delve into the potential effects of PEL molecular weight, ambient ionic strength, and titration concept (FT-IR and POT), drawing on classical models of weak PEL.

This research not only refines our understanding of protonated mono and polyamines but also contributes valuable data to the broader field of chemical analysis using spectroscopy. The article, available in the latest issue of 

, showcases the detailed work of the research team in unraveling the mysteries of pH behavior in these intriguing chemical systems.

This article was written with the help of artificial intelligence and has been edited to ensure accuracy and clarity. You can read more about our

(1) Müller, M.; Wirth, L.; Urban, B. Determination of the pKa Value of Protonated Mono and Polyamine in Solution Using Fourier Transform Infrared Titration. 

Researchers from Technische Universität Dresden, reveal new insights into protonated mono and polyamines' behavior by determining pKa values using Fourier transform infrared titration, providing valuable data for chemical analysis.

FT-IR Titration Unveils pH Mysteries: New Insights into Protonated Mono and Polyamine Solutions

Scientists in Ireland recently tested the effectiveness of different machine learning (ML) methods for measuring contamination levels on wind turbine blades. Their work was published in 

wind turbines in Oiz eolic park | Image Credit: © mimadeo - stock.adobe.com

Wind energy has become a popular alternative to fossil fuels. In addition to not releasing emissions that can pollute the air or water, wind turbines can reduce electricity generation from fossil fuels, causing a decrease in total air pollution and carbon dioxide emissions (2). However, contamination can have a detrimental effect on wind turbine performance. Contaminants can be organic (insects, birds, plant matter) or inorganic (soil, sand, salt). This has led to a push for cleaning processes that are efficient, cost-effective, and non-hazardous.

In this study, the scientists investigated standoff laser ablation as a means of cleaning wind turbine blades, specifically in combination with standoff laser-induced breakdown spectroscopy (LIBS). This use of LIBS has been used for material analysis in different fields, such as historical architecture and infrastructure testing; meanwhile, laser ablation has been used to investigate archaeological artifacts, biological materials, and more.

The scientists combined these techniques to investigate the surface of wind turbine blade materials and detect contamination pre- and post-cleaning. This involved performing LIBS in the vacuum ultraviolet (VUV) and ultraviolet visible (UV-Vis) spectral ranges. Analyzing the spectra only showed slight variations in constituent materials between clean and contaminated blade samples. To create a more efficient method of distinguishing between clean and contaminated samples, four different machine learning (ML) and statistical methods were evaluated: partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), competitive learning (CL), and convolutional neural networks (CNN). Additionally, the spectral regions where the algorithms were applied were chosen via a volumetric ellipsoid overlap test based on principal component analysis (PCA).

The scientists found that SVM performed the best, followed by PLS-DA, CNN, and finally, CL. The SVM approach showed high accuracy and precision, and when projected via PCA, the LIBS spectra became linearly separable. The other techniques did perform well but were slightly less accurate and longer in run time than SVM, the scientists wrote (1). The researchers intend to further compare and evaluate other ML algorithms and address challenges for incorporating this setup into portable systems for field deployment.

(1) Cummins, S.; Campbell, J. N.; Durkan, S. M.; Somers, J.; Finnegan, W.; Goggins, J.; Hayden, P.; Murray, R.; Burke, D.; Lally, C.; Alli, M. B.; Varvarezos, L.; Costello, J. T. Wind turbine contaminant classification using machine learning techniques.

https://doi.org/10.1016/j.sab.2023.106802

(2) Wind explained: Wind energy and the environment. U.S. Energy Information Administration. 

https://www.eia.gov/energyexplained/wind/wind-energy-and-the-environment.php

Scientists in Ireland recently tested the effectiveness of different machine learning (ML) methods for measuring contamination levels on wind turbine blades.

Machine Learning Algorithms Tested for Wind Turbine Contaminant Detection

 on November 27, 2023, researchers from Beihang University in Beijing, China, have introduced a novel approach to real-time monitoring of surface water contamination. The article titled "Dynamic Multivariate Outlier Detection Algorithm Using Ultraviolet Visible Spectroscopy for Monitoring Surface Water Contamination With Hydrological Fluctuation in Real-Time" presents a dynamic multivariable outlier sampling rate detection (DM-SRD) algorithm, addressing key challenges in the detection of water contaminants.

Surface water contamination poses a significant threat to ecosystems and human health. Traditionally, ultraviolet-visible (UV-vis) spectroscopy has been a reliable method for water quality assessment. However, the ever-changing nature of surface water, influenced by factors such as rainfall and alterations in flow, introduces complexities in spectral characteristics over time. This dynamic environment often results in misinterpretation between hydrological fluctuation spectra and contaminated water spectra, leading to higher false alarm rates and missed detections.

The DM-SRD algorithm, proposed by the authors, offers a dynamic solution to these challenges. By incorporating a dynamic updating strategy, the algorithm enhances its adaptability to hydrological fluctuations, significantly reducing false alarms. Moreover, the integration of multiple outlier variables as outlying degree indicators improves the overall accuracy of contamination detection.

The efficacy of the DM-SRD method was rigorously tested through experiments utilizing spectra collected from real surface water sites with simulated hydrological fluctuations. Comparative analyses with static SRD methods and spectral matching techniques showcased the superiority of the DM-SRD algorithm. The results revealed an impressive accuracy rate of 97.8%, outperforming alternative detection methods while simultaneously minimizing false alarm rates and eliminating the risk of missing alarms (1).

One of the notable strengths of the DM-SRD algorithm is its exceptional adaptability and robustness. The research findings indicate that whether the database contains prior information on hydrological fluctuation or not, the DM-SRD method consistently maintained high detection accuracy. This adaptability underscores its potential for real-world applications, making it a game-changer in the field of water contamination monitoring.

As water quality continues to be a global concern, the DM-SRD algorithm's innovative approach promises to reshape the landscape of real-time surface water contamination detection, providing unparalleled accuracy and reliability. The research, available in the latest issue of 

, marks a significant leap forward in the ongoing efforts to safeguard water resources worldwide.

(1) Li, Q.; Shao, X.; Cui, H.; Wei, Y.; Shang, Y. Dynamic Multivariate Outlier Detection Algorithm Using Ultraviolet Visible Spectroscopy for Monitoring Surface Water Contamination With Hydrological Fluctuation in Real-Time. 

Researchers from Beihang University in Beijing, China, introduce a dynamic multivariate outlier detection algorithm, DM-SRD, in the journal Applied Spectroscopy, significantly improving the accuracy of real-time monitoring for surface water contamination by addressing challenges posed by hydrological fluctuations.

Dynamic Multivariate Outlier Detection Revolutionizes Real-Time Monitoring of Surface Water Contamination

A close up of a Talavera cup with a blue background.

X-ray fluorescence (XRF) has emerged as a common technique for non-invasive analysis of the elemental composition of cultural artifacts. In a new study, published in the journal 

Spectrochimica Acta Part B: Atomic Spectroscopy 

a group of researchers from the Università degli Studi di Milano-Bicocca in Milan, Italy used this technique to analyze Puebla ceramic decorations (1). The team analyzed 

Puebla Policromo, which is a style of glazed ceramic that originated in Puebla, Mexico.

XRF is a common technique in the elemental analysis of art and other artifacts. Portable devices make it easy for scientists to quickly and non-invasively analyze works of art in museums or archaeological sites. The analysis is completed by irradiating a sample with high energy X-rays (2). The technique is commonly used in industries including oil and gas, automotive, aerospace, mining, and construction (2).

For successful XRF analysis samples must be flat and homogenous for the best results, which can be a challenge with many artifacts, such as ceramics, which often have many layers and pigments. The research team used angle resolved XRF (AR-XRF), which is an analytical technique where a sample is irradiated via different angles.

“Historical, and archeological artifacts are, most of the time, inhomogeneous samples, not only because they can be made of different materials (like ceramics or pigments), but also because they can exhibit layers that are considered ‘intermediate’ thickness for the investigated x-ray energies,” the scientists wrote in the study. “These layers may originate from the manufacturing of the sample, such as glazes covering a ceramic object, or the gilding of a metal; but also, be the result of changes in surface composition caused by aging and other alteration processes (1).”

The decorations on the sample were classified into three groups: black spots, blue decoration, and black stripes, on top of a homogenous thick white glaze. The scientists were able to determine the composition of the white glaze, which was made of mostly lead (Pb) and tin (Sn). They also found the blue decorations had a similar composition with the addition of cobalt and iron. The black decorations showed intense signals of iron and low signals of lead, they wrote.

However, using the AR-XRF to analyze Puebla ceramic decorations proved challenging, the scientists noted. The information they were able to glean from the technique largely depended on the parameters and thickness of the layers.

“In the case of the blue glaze, AR-XRF cannot discriminate the presence of two different layers,” they wrote. “Indeed, in this case, the colored glaze is very thick, and the composition is similar to that of the white glaze underneath, besides, a diffusion of the blue glaze inside the white glaze can also be observed in the fractures. For this reason, the blue-colored regions are described as a unique layer of infinite thickness (1).”

While AR-XRF can be employed to analyze the structure of a layered sample, it won’t be able to give complete information on the composition and thickness of different layers, they wrote. Other techniques, including Rutherford backscattering spectroscopy (RBS) coupled with particle induced X-ray emission (PIXE) may be better suited for the analysis of layered samples.

Orsilli, J.; Martini, M.; Galli, A. Angle Resolved-XRF Analysis of Puebla Ceramic Decorations. 

Spectrochim. Acta Part B: Atomic Spectrosc.

What Is XRF (X-Ray Fluorescence) and How Does It Work? https://www.thermofisher.com/blog/ask-a-scientist/what-is-xrf-x-ray-fluorescence-and-how-does-it-work/ (accessed 2023-11-27)

Scientists faced challenges when using XRF on artifacts that contain many layers. 

Analyzing Puebla Ceramics Using X-Ray Fluorescence 

A team of researchers has successfully developed 3D printed multi-hole collimators for X-ray imaging systems, offering a cost-effective alternative to traditional pinhole apertures. This development has the potential to enhance the sensitivity and position resolution of X-ray fluorescence (XRF) spectrometers, paving the way for more accurate and detailed analyses.

, explored the performance of multi-hole collimators with varying geometries and material compositions in a full-field XRF spectrometer based on the 2D-THCOBRA gas detector (a special micropattern gaseous detector [MPGD] design). The researchers evaluated the relative sensitivity and position resolution of different setups, comparing them to pinhole-based configurations (1).

Their findings revealed that the use of smaller pinholes, while limiting system sensitivity, provides superior position resolution. Conversely, collimators with large holes and small septa yielded higher sensitivity but compromised position resolution. The best position resolution, 1.10 ± 0.02 mm full width at half maximum (FWHM), was achieved with a 0.3 mm diameter pinhole aperture, which also exhibited the lowest relative sensitivity. The lead (Pb) collimator with 2 mm hexagonal holes presented the worst position resolution value, 3.25 ± 0.09 mm FWHM (1).

In terms of sensitivity, the low attenuation coefficient and small depth of the copper (Cu) collimator with 1 mm circular holes resulted in the highest sensitivity–50 times greater than the value achieved with the smaller pinhole aperture.

These findings underscore the critical trade-off between sensitivity and position resolution in XRF imaging, emphasizing the importance of tailoring the optical component to the specific application. While pinhole apertures allow magnification for enhanced detail, they may compromise sensitivity. Multi-pinhole collimators, as used in SPECT imaging of small animals, offer a balance between sensitivity and position resolution.

The researchers highlight the potential of 3D printed collimators as a cost-effective and versatile solution for energy dispersive X-ray fluorescence (EDXRF) system applications. 3D printing enables the exploration of diverse geometries and designs, paving the way for further advancements in XRF imaging technology.

This research holds significant implications for various fields, including environmental monitoring, material analysis, and biomedical imaging. The development of 3D printed multi-hole collimators promises to enable more accurate, sensitive, and versatile analyses.

This article was written with the help of artificial intelligence and has been edited to ensure accuracy and clarity. You can read more about our 

Carvalho, P. M. S.; Leite, F. D.; Tavares, G.; Correia, P. M. M.; Oliveira, J. M. M.; Veloso, J. F. C. A.; Silva, A. L. M. 3D Printed Imaging Apertures for MicroPattern Gas Detector Based Full Field of View X-Ray Fluorescence Spectrometers. 

, 106806. DOI: 10.1016/j.sab.2023.106806.

In a new study, a group of researchers explored the performance of multi-hole collimators with varying geometries and material compositions in a full-field XRF spectrometer based on the 2D-THCOBRA gas detector.

Testing 3D Printed Multi-Hole Collimators for X-Ray Imaging Systems

The use of laser-induced breakdown spectroscopy (LIBS) has helped spearhead new developments in the field of fuel thermal conversion monitoring. Meirong Dong, lecturer at South China University of Technology and the Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, led a team comprised of researchers to document and study the role of atomic emission spectroscopy (AES) in understanding the intricacies of fuel thermal conversion monitoring. Their findings were published in the journal 

The review published in the journal examined the application of LIBS across various stages of the thermal conversion process (1). From scrutinizing raw energy materials such as coal and biomass before combustion to dissecting the combustion process itself and analyzing post-combustion residue like fly ash, the study highlights the versatility of LIBS in fuel analysis.

An important finding highlighted in the review article covered how LIBS has been used to analyze raw energy materials (1). LIBS can assess materials in different forms like rock, pellets, and particle flow. The researchers also developed a suite of online instruments to study the properties of energy materials more closely, which have provided them with new insights into their composition and behavior (1).

The study also explores the integral role LIBS plays in the combustion process. This sophisticated approach outshines conventional detection technologies, offering a multifaceted understanding of combustion states in real-time (1).

The study also sheds light on LIBS' prowess in detecting unburned carbon in fly ash, contributing significantly to understanding post-combustion residues and their environmental impact (1).

The authors conclude their study by emphasizing the importance of optical measurement techniques in enabling in situ, continuous monitoring of multiple parameters during fuel utilization (1). Optical measurement technologies are non-contact and overcome the limitations of traditional conventional detection methods (1). The result is that they can conduct more efficient and cleaner fuel utilization as a result (1).

The findings underscore a paradigm shift in fuel thermal conversion monitoring, paving the way for enhanced efficiency, reduced pollution, and a deeper comprehension of energy conversion processes. The insights that this study provides offers a pathway to the further evolution of technology in the realm of fuel utilization and energy conversion.

(1) Dong, M.; Cai, J.; Liu, H.; Xiong, J.; Rao, G.; Yao, S.; Lu, J. A review of laser-induced breakdown spectroscopy and spontaneous emission techniques in monitoring thermal conversion of fuels. 

New research on fuel thermal conversion shows the potential of laser technology in real-time monitoring, revolutionizing efficiency, and understanding energy conversion processes.

Monitoring Fuel Thermal Conversion Using LIBS and Spontaneous Emission Techniques

A recent study led by Sasa-Alexandra Yehia-Alexe of the National Institute for Lasers, Plasma and Radiation Physics and the University of Bucharest explored the specifics of laser induced ablation (LIA) and laser induced desorption (LID) techniques using a 1053-nm laser source (1). The study was published in 

The study focused on 1-μm aluminum layers created through using a high-power impulse magnetron sputtering technique on substrates with varying surface properties. The crucial aspect was the software-controlled manipulation of laser pulse energy, enabling a seamless transition from layer ablation to layer desorption (1).

The research team evaluated the quantity of the released deuterium after using quadrupole mass spectrometry (QMS) at the end of the laser-induced processes. They compared it to the results received with thermal desorption spectroscopy, which revealed an approximate total of 2.6 × 10²¹ D at/m² of deuterium in the analyzed sample (1). The mass spectra data disclosed that 85% (2.2 × 10²¹ D at/m²) and 9% (0.2 × 10²¹ D at/m²) of the total deuterium atoms were released via LIA and LID, respectively (1).

The research team also were able to determine the boundaries between the ablation and desorption processes by conducting mathematical modeling of the data. The analysis of the aluminum layer surfaces in conjunction with the substrate surface quality provided critical insights into the mechanisms governing the release of deuterium atoms via these laser-induced processes (1).

However, the biggest, and most important, conclusion was that the research team was able to confirm their findings. By using optical emission spectroscopy (OES), the research team validated that the substrate-layer interface had been reached during LIA-QMS analysis.

From advancing our understanding of materials science to potentially revolutionizing energy applications, these newly unveiled laser techniques hold promise for manipulating atomic structures within materials. This opens avenues for further research, driving innovations in energy production and material engineering.This study showcases the potential of laser technology in manipulating atomic behavior within materials.

(1) Yehia-Alexe, S.-A.; Groza, A.; Serbanescu, M.; et al. Considerations on hydrogen isotopes release from thin films by laser induced ablation and laser induced desorption techniques. 

Spectrochimica Acta Part B: At. Spectrosc.

In a recent study, quadrupole mass spectrometry (QMS) was used to test the amount of deuterium atoms from aluminum layers.

New Laser Techniques Unlock Deuterium Release from Aluminum Layers

Patrick Lavery is an Editor for the MJH Life Sciences brands LCGC and Spectroscopy, and their respective websites, 

. He previously spent nearly a decade as a news anchor, reporter, and producer at New Jersey 101.5 FM.

Spectroscopy is predicted to drive 15% growth in the global proteomics market by 2030, pushing the market’s value to $107.66 billion US dollars, 

 by Vantage Market Research. Along with chromatography and electrophoresis, the growth in value of proteomics-related techniques is forecast to enhance a market that was worth an estimated $35.2 billion in 2022.

An illustration of a network of molecules, forming a web-like structure, representing the dynamics of molecular interactions Generative AI | Image Credit: © Denis Yevtekhov - stock.adobe.com

 elucidated the relationship between spectroscopy and proteomics through the years, pointing to nuclear magnetic resonance (NMR) spectroscopy as a complement to or substitute for X-ray crystallography in the process of obtaining information about protein crystal structures (1). Furthermore, the high-resolution magnetic angle spinning (HRMAS) NMR technique can be applied to nonconventional solvents, aligning it with the practices of green analytical chemistry.

The demand for proteomics has increased over the last several years, according to the Vantage report, because of its role in disease diagnosis, particularly the early detection of cancer. The research firm said that the biomarkers identified by proteomics help to discover therapeutic targets in biospecimens that drive market demand. But proteomics-based solutions, the same report said, reach farther than just cancer detection and treatment. As the frequency of other chronic illnesses such as diabetes and heart disease increase worldwide, the need for customizable medicines that detect protein biomarkers is expected to intensify.

As of 2022, Vantage said, the proteomics market was dominated by North America, with the continent’s 42.3% share of revenue attributed to generally advanced healthcare infrastructure. But trend-watchers say governments primarily in the Asia/Pacific region, like those of China and India, but also in other corners of the world such as Brazil, are now investing heavily in the research and development of new potential drugs as proteomics research and technology continues to transform the pharmaceutical industry. Those are the regions which are forecast to experience the maximum market growth by 2030.

(1) Mielko, K. A.; Pudełko-Malik, N.; Tarczewska, A.; Młynarz, P. NMR Spectroscopy as a “Green Analytical Method” in Metabolomics and Proteomics Studies. 

High-resolution magnetic angle spinning nuclear magnetic resonance (HRMAS NMR) spectroscopy in particular can be applied to nonconventional solvents, helping to obtain information about protein crystal structures while also adhering to green analytical chemistry tenets.

Spectroscopy Expected to Drive Growth in $107B Proteomics Market

When using laser-induced breakdown spectroscopy (LIBS) in spectral analysis, scientists can encounter various hurdles. The most common challenges scientist face while conducting elemental analysis are optimizing the interactions between the laser and the samples, making note of the variations in laser energy, and the converging of the environmental noise that helps to create diverse backgrounds within acquired spectra. All these hurdles can significantly impact the analysis.

, a research team from Jiangnan University introduced a novel LIBS method aimed at automating the estimation and removal of varied spectral backgrounds (1). Led by Hao Chen, who works in the School of Mechanical Engineering at Jiangnan University, the researchers proposed an approach that leverages window functions, differentiation concepts, and a piecewise cubic Hermite interpolating polynomial (Pchip) (1).

In this experiment, Chen and his team conducted a series of simulated experiments to evaluate the background correction methodologies. They discovered that their proposed method performed better than existing techniques such as asymmetric least squares (ALS) and model-free background correction (1). By leveraging window functions, Pchip, and differentiation concepts, the new method improved on removing white noise and baseline distortions, achieving a better signal-to-background ratio (SBR) than the previous methods (1). The research team also discovered that their method improved on the handling of background baseline jumps.

The researchers applied their method to seven different aluminum alloys and observed a correlation between spectral intensity and the concentration of magnesium (Mg) (1).

Notably, in experiments measuring Mg concentration in aluminum alloys, the correlation coefficients between the predicted and actual concentrations underwent a dramatic improvement post-correction (1). While ALS and Model-free methods yielded coefficients of 0.9913 and 0.9926, respectively, this new method yielded a coefficient of 0.9943 from an initial 0.9154 (1).

The findings not only validate the efficacy of this automated methodology, but they also open avenues for the advancement of spectral analysis accuracy in LIBS for future research.

(1) Chen, H.; Shi, X.; He, Y.; Zhang, W. Automatic background correction method for laser-induced breakdown spectroscopy. 

Spectrochimica Acta Part B: At. Spectrosc. 

A novel automated method for spectral background estimation in laser spectroscopy promises accuracy and minimal human intervention for quantitative analysis.

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