Terahertz Spectral Investigation of L-Cysteine Hydrochloride and its Monohydrate

An Interview with AI About Its Potential Role in Vibrational and Atomic Spectroscopy

Spectrophotometric Determination of Thiosulfate in Desulfurization Solutions 

Visible-NIR Spectral Characteristics Analysis Using Hyperspectral Data

Advancing Wetland Classification

A team of researchers from the National Oceanography Centre at the University of Southampton and the National Physical Laboratory in Teddington has developed an optimized method for the precise measurement of iodine-129 (

I) in decommissioning wastes using tandem inductively coupled plasma mass spectrometry (ICP-MS/MS). The study, published in the 

Journal of Analytical Atomic Spectrometry

, highlights the advantages of this approach for sensitive and efficient analysis of 

I, providing a cost-effective alternative to other measurement techniques (1).

Plastic bottles at landfill | Image Credit: © aryfahmed - stock.adobe.com

I, a long-lived fission product, is crucial for waste characterization and long-term monitoring of waste facilities and the surrounding environment. The researchers aimed to explore the capabilities of tandem ICP-MS/MS for the rapid and routine measurement of 

I in nuclear wastes. This method is particularly well-suited for high-throughput analysis and offers improved sensitivity compared to other mass spectrometric and decay counting techniques.

The study focused on addressing the multiple interferences encountered during accurate 

I measurement by ICP-MS. The tandem setup demonstrated its effectiveness in removing isobaric, polyatomic, and tailing interferences. Additionally, the researchers investigated the importance of matrix modification and the selection of an appropriate internal standard to enhance sensitivity. By optimizing these parameters, the team achieved remarkable results.

In the context of ICP-MS/MS, isobaric interferences refer to situations where ions of different elements have the same mass-to-charge ratio (

). These interferences can lead to inaccurate measurement of the target analyte as they can overlap with the signal of interest on the same 

 channel. Polyatomic interferences occur when ions containing multiple atoms interfere with the analyte signal, often resulting in distorted measurements. Tailing interferences, on the other hand, are caused by incomplete ionization or incomplete desolvation of sample ions, leading to skewed peaks and potentially affecting the accuracy and precision of the measurements. Effective removal of these interferences is crucial for obtaining accurate and reliable results in ICP-MS/MS analysis.

Applying the optimized setup to various decommissioning waste simulants, the researchers achieved an impressive instrument detection limit of 1.05 × 10

I. This limit is two orders of magnitude below the target out-of-scope limit of 0.01 Bq/g (1.57 ng/g). The measurements obtained using ICP-MS/MS showed good agreement with those obtained through liquid scintillation counting (LSC), further validating the accuracy and reliability of the method.

The study demonstrates the feasibility of rapid, routine, and low-level measurement of 

I using ICP-MS/MS for end-users in decommissioning and environmental monitoring. This optimized method offers several advantages, including improved sensitivity, cost-effectiveness, and high throughput. By accurately characterizing and monitoring 

I in decommissioning wastes, stakeholders can ensure proper waste management and protect the environment effectively.

The development of this optimized method for 

I measurement using ICP-MS/MS marks a significant step forward in the field of nuclear waste analysis. It provides a valuable tool for researchers and professionals involved in waste characterization, decommissioning, and environmental monitoring. The ability to achieve accurate measurements at low levels opens avenues for better waste management practices and contributes to the safe and sustainable disposal of nuclear waste.

(1) Zacharauskas, Z.; Warwick, P.; Russell, B.; Reading, D.; Croudace, I. Development of an optimised method for measurement of iodine-129 in decommissioning wastes using ICP-MS/MS. 

Articles

Scientists have developed an optimized method for the precise measurement of iodine-129 in decommissioning wastes using tandem ICP-MS/MS. Their study demonstrates the effectiveness of this approach in achieving low-level measurements with improved sensitivity for waste characterization and environmental monitoring.

New Method Enables Accurate Measurement of Iodine-129 in Decommissioning Wastes Using ICP-MS/MS

A team of researchers from The University of British Columbia in Vancouver, Canada, and the National Hellenic Research Foundation in Athens, Greece, has developed a novel algorithm for rapid peak fitting and resolution enhancement in the analysis of Raman hyperspectra. The algorithm, described in a study published in the journal 

, offers significant improvements in processing large hyperspectral data sets and extracting valuable information about analytes (1).

Computer algorithm productivity efficiency, cyber security concepts | Image Credit: © Pablo Lagarto - stock.adobe.com

Spectroscopic peak parameters play a crucial role in understanding the characteristics of analytes. Peak fitting, which involves identifying and resolving overlapped peaks, is a complex task due to computational challenges and the often unknown nature of the analyte. This poses obstacles when processing vast hyperspectral datasets, especially for applications like manufacturing process control.

The research team developed a new two-part algorithm to address these challenges. In the first part, they utilized a combination of techniques to estimate the total number of bands and their parameters based on a representative spectrum from the dataset. Leveraging vector operations and exploiting intrinsic features of the Gaussian distribution, the algorithm rapidly fitted all the spectra in an iterative manner, ultimately producing the best fits for each spectrum.

By reducing the bandwidths and simultaneously increasing the amplitudes of the obtained bands, the algorithm constructed high-resolution spectra that significantly enhanced correlation-based analyses. To validate its effectiveness, the algorithm was tested on synthetic spectra, successfully recovering the ground truth correlations between highly overlapped peaks. Furthermore, the researchers applied the algorithm to low-resolution spectra of glucose, comparing the results to those obtained from high-resolution spectra. The algorithm showcased its ability to enhance peak resolution and capture important aspects of the data's intrinsic correlation structure.

To demonstrate its practical utility, the team processed a larger spectral dataset obtained from mammalian cells, both fixed with methanol and air drying. The algorithm successfully enhanced resolution in complex spectra and showcased its impact on two-dimensional correlation spectroscopy and principal component analyses.

The development of this new algorithm is a significant step forward in the field of Raman spectroscopy. Its ability to rapidly perform peak fitting and resolution enhancement on large hyperspectral datasets opens doors for improved analysis in various domains, including manufacturing, biotechnology, and materials science. Researchers and analysts can now obtain high-resolution spectra more efficiently, enabling deeper insights into the nature of analytes and their correlation structures.

As technology continues to advance, algorithms like these are essential tools that accelerate scientific progress and drive innovation in a wide range of industries.

(1) Schulze, H. G.; Rangan, S.; Vardaki, M. Z.; Blades, M. W.; Turner, R. F. B.; Piret, J. M. Rapid Vector-Based Peak Fitting and Resolution Enhancement for Correlation Analyses of Raman Hyperspectra. 

A team of researchers has developed a novel algorithm for rapid peak fitting and resolution enhancement in Raman hyperspectra analysis. The algorithm offers significant advancements in processing large datasets, improving peak resolution, and extracting valuable information about analytes.

Breakthrough Algorithm Enables Rapid Peak Fitting and Resolution Enhancement for Raman Hyperspectra Analysis

Heilongjiang University researchers have introduced a breakthrough in predicting the germination of sugarbeet seeds using the innovative combination of hyperspectral imaging (HSI) and information fusion techniques. The study, published in 

, demonstrates the effectiveness of this nondestructive approach in accurately forecasting seed germination rates, paving the way for improved seed selection and cultivation practices in the sugarbeet industry (1).

Harvested sugar beet crop root pile | Image Credit: © Bits and Splits - stock.adobe.com

Germination rate plays a crucial role in determining seed quality and successful planting outcomes. Traditional methods of assessing germination often require destructive sampling or lengthy observation periods. To overcome these limitations, the research team at Heilongjiang University proposed a novel nondestructive prediction method using HSI technology.

The study involved the application of hyperspectral imaging techniques, combined with germination tests, for feature association analysis and prediction of sugarbeet seed germination performance. Through the process of binarization, morphology, and contour extraction, hyperspectral imaging effectively achieved accurate segmentation of individual sugarbeet seeds without causing any damage.

To extract relevant information from the hyperspectral data, the researchers conducted a comparative analysis of nine spectral pretreatment methods. The standard normal variate-first derivative (SNV+1D) method was employed to process the average spectrum of sugarbeet seeds, resulting in the identification of fourteen characteristic wavelengths using the Kullback–Leibler (KL) divergence technique. The validity of these characteristic wavelengths was verified through principal component analysis (PCA) and material properties analysis.

In addition to spectral features, the study also considered image features extracted from the hyperspectral images using the gray-level co-occurrence matrix (GLCM). These features, along with the spectral features, were fused together to establish prediction models, including partial least squares discriminant analysis (PLS-DA), CatBoost, and support vector machine radial-basis function (SVM-RBF) models.

CatBoost is a gradient boosting algorithm that is designed to handle categorical features in machine learning tasks. It uses an innovative approach called ordered boosting, which incorporates the natural order of categorical variables to improve model performance. CatBoost also employs advanced techniques such as gradient-based sampling and symmetric trees to handle high-dimensional data and mitigate overfitting, making it a powerful algorithm for a wide range of classification and regression problems.

The results demonstrated that the fusion of spectral and image features provided superior prediction accuracy compared to using spectral or image features alone. Among the models examined, the CatBoost model yielded outstanding performance with a prediction accuracy of up to 93.52%.

By leveraging the capabilities of HSI and information fusion, the nondestructive prediction of sugarbeet seed germination becomes more precise and efficient. This advancement holds significant promise for the agricultural industry, enabling farmers and seed producers to make informed decisions regarding seed selection and optimizing planting strategies, ultimately leading to improved crop yields and quality in sugarbeet cultivation.

(1) Wang, J.; Sun, L.; Xing, W.; Feng, G.; Yang, J.; Li, J.; Li, W. Sugarbeet Seed Germination Prediction Using Hyperspectral Imaging Information Fusion. 

A recent study presents an innovative approach for predicting sugarbeet seed germination using the fusion of hyperspectral imaging and information analysis techniques. 

Advancing Sugarbeet Seed Germination Prediction: Harnessing Hyperspectral Imaging and Information Fusion

Confocal Raman microscopy has emerged as a valuable tool for analyzing the chemical and structural properties of transparent three-dimensional (3D) objects. However, accurately interpreting depth profiles obtained from Raman measurements can be challenging due to various factors such as sample size and surrounding environment. In a recent study conducted by researchers from Technische Universität Braunschweig in Germany, a comprehensive investigation of optical effects at the interface between polymer spheres and different substrates was performed using ray- and wave-optical simulations.

Molecules, atoms background. Medical background for banner or flyer. Molecular structure at the atomic level. 3D illustration | Image Credit: © rost9 - stock.adobe.com

Ray- and wave-optical simulations play a crucial role in understanding and interpreting Raman spectroscopy data. By using these simulations, researchers can investigate the interaction of light with a sample at the microscale level. Ray optics approximations, such as the geometrical optics model, allow for the prediction of the light's propagation path and intensity distribution within the sample. On the other hand, wave optics simulations, such as finite-difference time-domain (FDTD) or finite element method (FEM), consider the wave nature of light and provide more detailed information about the electromagnetic field distribution. By combining these two simulation approaches, researchers can gain insights into the complex phenomena occurring during Raman spectroscopy measurements, such as light scattering, diffraction, and interference effects, enabling a more accurate interpretation of experimental results and enhancing the overall understanding of the sample's optical behavior.

The results of this research, published in 

, shed new information on the observed optical phenomena and their impact on depth profiling in confocal Raman microscopy (1). To address the challenges associated with interpreting Raman depth profiles, the researchers derived a correction factor that enhances the accuracy of determining the nominal dimensions of scanned objects. This correction factor, depending on the instrumental configuration, enables more precise measurements and quantitative analysis of 3D objects.

The findings highlight the importance of considering and accounting for optical effects when employing confocal Raman microscopy for nondestructive tomography of complex samples. By providing a deeper understanding of the factors influencing depth profiling, this study paves the way for improved characterization of polymeric microsphere layers and other transparent 3D structures. The ability to accurately determine the chemical composition, structural properties, and size of such objects opens up new possibilities in materials science, biomedical research, and various other fields.

With the derived correction factor and the insights gained from optical simulations, researchers and practitioners can enhance the reliability and accuracy of depth profiling in confocal Raman microscopy. This advancement contributes to the development of more robust and quantitative techniques for noninvasive analysis and characterization of intricate 3D samples, ultimately enabling a better understanding of their properties and behaviors.

(1) Syring, A.; Wang, Z. Wundrack, S.; Stosch, R.; Voss, T. Optical Tomography of Polymeric Microsphere Layers Using Confocal Raman Microscopy. 

A new study demonstrates the improved accuracy of depth profiling in confocal Raman microscopy for analyzing the structural and chemical composition of polymeric microsphere layers. 

Enhancing Depth Profiling Accuracy in Confocal Raman Microscopy for Optical Tomography of Polymeric Microsphere Layers

Researchers from the Universities of Shandong and Qingdao University of Science and Technology in China have made a breakthrough in the field of biosensing with the development of a near-infrared (NIR) fluorescent probe. The probe offers a highly sensitive and selective method for monitoring the activity of butyrylcholinesterase (BChE), a critical esterase synthesized by the liver. Additionally, the probe demonstrates remarkable capabilities in detecting pesticide residue in food samples. The study was published in the journal 

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Farmer spraying pesticide in the rice field | Image Credit: © comzeal - stock.adobe.com

A ratiometric near-infrared (NIR) fluorescent probe refers to a type of molecular sensor that utilizes two different emission wavelengths within the NIR or visible range. It is designed to undergo a fluorescence change at these specific wavelengths in response to a target molecule or analyte. By measuring the ratio of the emitted fluorescence at these two wavelengths, the probe provides a more reliable and accurate detection method, minimizing the effects of background noise and variations in experimental conditions. This ratiometric approach enhances the sensitivity and selectivity of the probe, making it a valuable tool for various applications, including bioimaging, disease diagnostics, and environmental monitoring.

Monitoring BChE activity is essential for evaluating overall health, but it poses challenges in complex biological systems. To address this, the researchers designed a novel NIR fluorescent probe that utilizes a cyanine backbone. By harnessing intrinsic NIR fluorescence and circumventing interference from bioluminescence, the probe enables precise and reliable detection of BChE activity. The researchers observed a fascinating structural transformation during the sensing event, which caused a striking fluorescence change from the NIR region, measured at 816 nm, to the red region, measured at 637 nm. This ratiometric assay provides an innovative approach for monitoring BChE activity with high accuracy and sensitivity.

The researchers conducted comprehensive investigations to assess the probe's performance in various bio-areas, including the cellular level and slice platform. The results demonstrated that the developed receptor exhibited a ratiometric pattern and could be successfully applied in diverse biological settings. Notably, the probe's versatility extends beyond BChE activity monitoring. It was found to possess the ability to detect pesticide dichlorvos (DDVP) residue in food samples with exceptional sensitivity and accuracy. This discovery suggests that the NIR fluorescent probe has the potential to serve as a viable candidate for monitoring environmental pollution caused by pesticide residues.

The implications of this development are far-reaching. The accurate monitoring of BChE activity provides valuable insights into numerous diseases, potentially facilitating convenient and reliable diagnoses. Moreover, the probe's capability to detect pesticide residue in food samples addresses growing concerns about food safety and environmental pollution. By offering a highly sensitive and selective tool, this research opens new avenues for biosensing applications and paves the way for improved health evaluation and environmental monitoring.

Further research and development will focus on optimizing the probe's performance and exploring its potential applications in clinical diagnosis and environmental surveillance. The work of these Chinese researchers contributes to the advancement of molecular spectroscopy and showcases the power of near-infrared ratiometric fluorescent strategies in the detection of BChE activity and pesticide residue in various samples.

(1) Yuan, W.; Wan, C.; Zhang, J.; Li, Q.; Zhang, P.; Zheng, K.; Zhang, Q.; Ding, C. Near-infrared ratiometric fluorescent strategy for butyrylcholinesterase activity and its application in the detection of pesticide residue in food samples and biological imaging. 

Spectrochim. Acta A Mol. Biomol. Spectrosc. 

https://doi.org/10.1016/j.saa.2023.122719

Researchers have developed a near-infrared fluorescent probe that allows for highly sensitive detection of butyrylcholinesterase activity and pesticide residue in food samples. The probe offers a ratiometric pattern and shows promise for applications in health evaluation, disease diagnosis, and environmental monitoring.

A Near-Infrared Fluorescent Probe for Monitoring Butyrylcholinesterase Activity and Detecting Pesticide Residue is Developed

Handheld Fourier transform infrared (FT-IR) spectrometers have emerged as highly promising tools for applications requiring real-time and accurate material detection and quantification. However, their compact size, limited warm-up time, and susceptibility to environmental changes can introduce short-term noise and long-term instabilities, which can impact their overall performance. In a recent study published in the journal 

, researchers investigated the impact of long-term multiplicative instabilities on the signal-to-noise ratio (S/N) of these spectrometers, employing the Allan variance technique to identify and quantify different types of noises (1). Their findings shed light on the performance characteristics of these compact FT-IR spectrometers and offer insights for improving their accuracy and stability.

Illustration power of atom release nuclear energy in nucleus molecule with radiation light infrared, nano physics science model concept. | Image Credit: © Quality Stock Arts - stock.adobe.com

The Allan variance technique is a statistical analysis method used to assess the stability and noise characteristics of a measurement system over time. It involves dividing the data into overlapping segments of varying lengths and calculating the variance between consecutive segments. By plotting the Allan variance against the averaging time, it allows for the identification and quantification of different types of noises, including short-term noise, flicker noise, and random walk noise. This technique provides valuable insights into the noise sources and instabilities present in a measurement system, helping researchers and engineers to optimize the system's performance, improve accuracy, and mitigate the effects of noise for various applications in fields such as spectroscopy, metrology, and signal processing.

The compact size and portability of handheld FT-IR spectrometers make them highly attractive for various applications, such as on-site material analysis, environmental monitoring, and biomedical diagnostics. However, the inherent constraints of their design pose challenges in maintaining stable and reliable performance. To address this, the research team focused on investigating the impact of long-term multiplicative instabilities, which can degrade the quality of spectral data and compromise the accuracy of material identification and quantification.

The study employed the 100% line-method to measure the signal-to-noise ratio (S/N) and deduced an expression for the variance in the presence of long-term multiplicative instabilities. By utilizing the Allan variance technique, the researchers were able to effectively analyze the different types of noise affecting the spectrometers. They applied their methodology to a commercial scanner module, providing a practical and real-world context for their investigation.

The results of the study revealed important insights into the performance characteristics of compact FT-IR spectrometers. By quantifying the various sources of noise and instabilities, the researchers were able to better understand the limitations and challenges associated with these portable instruments. This understanding paves the way for further advancements in instrument design, data processing algorithms, and calibration procedures, with the aim of enhancing the accuracy, stability, and reliability of handheld FT-IR spectrometers.

The findings of this research hold implications for a wide range of applications. Improved performance of compact FT-IR spectrometers will facilitate more accurate and reliable material analysis in the field, enabling rapid and informed decision-making in various industries. This study contributes to the growing body of knowledge surrounding portable spectroscopic techniques and highlights the importance of addressing performance limitations to unlock the full potential of handheld FT-IR spectrometers.

As further research and development efforts focus on refining the design and performance of these instruments, the impact of this study is expected to extend beyond the laboratory, enabling broader adoption and utilization of compact FT-IR spectrometers in real-world scenarios that demand precise and timely material analysis.

(1) Adib, G. A.; Sabry, Y. M.; Khalil, D. Allan Variance Characterization of Compact Fourier Transform Infrared Spectrometers. 

https://doi.org/10.1177/00037028231174248

A research team has utilized the Allan variance technique to analyze the performance characteristics of compact Fourier transform infrared (FT-IR) spectrometers. The study provides insights into the noise sources and instabilities of these handheld instruments, offering guidance for improving their accuracy and stability in real-time material detection and quantification applications.

Analysis Reveals Performance Characteristics of Compact Fourier Transform Infrared Spectrometers

The assessment and monitoring of the growth and response of bacteria to antibiotics is of crucial importance in research laboratories, as well as in food, the environment, medical, and pharmaceutical applications. James Chapman of RMIT University in Melbourne, Australia and his colleagues have been actively exploring the ability of near-infrared (NIR) and UV‐visible (UV-Vis) spectrophotometry, combined with machine learning tools (such as partial least squares discriminant analysis) to examine, measure, and monitor bacterial growth phases when treated with antibiotics. Chapman spoke to 

In a recent published paper utilizing near-infrared spectroscopy (1), you state that the measurement of the growth phases in bacteria using optical density at 600 nm (OD600) has been the “gold standard” for the industry and research laboratories. Why do you believe that this has been the case and what have you discovered that might challenge this convention?

The use of OD600 represents the single quickest measurement of many pathology, microbiology, and biochemistry laboratories, routinely used to determine the number of cells in a solution. This measurement is based on well-established science using turbidity and the Beer-Lambert law as a measure for cells grown in the solution obtaining a concentration estimate of cells per unit volume. This OD600 measurement can be achieved on a multiwavelength or single wavelength spectrophotometer, which as you all will know, these instruments are abundant in most laboratories.

Our discovery came about when we were using the multiwavelength spectrophotometer, where we hypothesized that common biochemical information is being lost or not even looked at in the whole spectrum. We thought that key pieces of information can be obtained from the spectra were taking, for example DNA absorbs at 260 nm and RNA absorbs in the 280 nm of the UV-Visible region, RNA, among other important biochemicals. We see these pieces of the puzzle as extra information while also obtaining cell numbers, this multicomponent information increases the ability to assign a fingerprint of what is going on in our system. The result of which is, we can understand microbial interactions with the antimicrobial agent and all within the complex matrix using the spectrophotometer.

In a second published paper (2), you investigate the potential of UV‐vis spectrophotometry to examine bacterial growth phases when treated with antibiotics, showcasing the ability to understand the point of resistance when an antibiotic is introduced into the media and therefore yielding improved understanding of the biochemical changes of the infectious pathogens. How would you compare NIR and UV-vis as optimum methods for assessing bacterial counts? How do these two methods compare with other available analytical techniques in terms of speed and accuracy?

We use these two methods for two reasons, the first being that NIR can measure the sample with a greater degree of energy penetration, and UV-vis spectrophotometry provides us with a multi-parameter method to ascertain more than just the standard optical density method. Both are very different methods, but when combined, provide very powerful information for antimicrobial resistance applications. For example, we can scan samples through the sample vials with NIR, and don’t need as much sample preparation; all key methods when time is of the essence.

The spectroscopic methods we use are rapid, non-destructive, green techniques if you will. This provides us with the bonus of less sample preparation, less sample destruction, and near-instantaneous measurement of the samples. We can also perform high-throughput sampling using the methods we have developed. Comparing these to established, yet highly qualitative and quantitative methods like GC–MS, NMR, or LC–MS, its horses for courses; with some techniques you require the lower limits of detection, but if it is the information you require in a rapid way, then spectrophotometry is the way to do it. For example, it currently takes up to one week to get a diagnosis for antimicrobial contamination and treatment. Pushing the time boundaries using our developed methods means that we can explore reducing the timeframes for antimicrobial diagnostics.

How would you distinguish the use of the terms 

? Do you think artificial intelligence could be of value for this application?

Artificial Intelligence is the overall term which encompasses machine learning. Therefore, any use of machine learning is a form of AI. AI is imperative to all applications of these type. It will be able to revolutionize the methods and generate faster responses to diagnostics—we feel that these are imperative to the design of new analytical systems coming into the future. Additionally, once we automate the process of analyzing data, this will lead to better AI models and the ability to understand and characterize an unknown system in far quicker times.

How does this work differ from what has been previously done by yourself or others?

Our group’s work is cutting edge and is some of the first to be reported in this area. Examining multiparameter systems in complex matrices is unheard of, given that the analytical method typically aims to extract the analyte of interest first, this can be readily achieved through solid phase extraction, sample clean-up, chromatography for example. However, our method extracts this information in a single step even when measuring in a complex matrix of antibiotics, microorganisms, and the nutrient broth that the microbes are measured in.

We demonstrate the use of rapid spectrophotometric and high-throughput methods which when combined with chemometrics and machine learning algorithms can be used to obtaining really fundamental information on the type, level of resistance, and the dose of antibiotic necessary to establish optimum diagnosis, treatment, and decontamination strategies. Our early methods can show that adding different concentrations of broad‐spectrum antibiotics to growth medium creates unique changes in the UV‐Vis and NIR spectra. Once we apply our preprocessing techniques to the data, we can obtain more information which allows us to monitor and differentiate treatments.

Were there any limitations or challenges you encountered in your work?

As with all experimental and scientific work, challenges always exist. We are working on obtaining better limits of detection and trying to train our AI systems to have improved quantitation for the models we make. The ability to sort, process, and analyze data is still very much manual; but our group and PhD students are working hard to develop automation. The types of projects we have a re largely multidisciplinary and require several discipline teams to solve these issues, therefore we have mathematicians, chemometricians, materials scientists, computer scientists, biologists, and chemists in the Chapman group all working hard to solve these challenges.

Can you please summarize the feedback that you have received from others regarding this work?

We have received so much interest in this work so far. Industry is keen to partner with us and we have many collaborators now internationally who are looking to broaden the application base, for example, cancer detection in tissue biopsy (looking for those key biochemical markers). We are always looking for new collaborations and challenges to work on, so this network base is rather dynamic, and we welcome anyone to join with us.

What are the next steps in this research?

We are already working on several steps in this work and will be publishing that imminently where we are focused on improving limits of detection, working with multi-polymicrobial samples (reflecting real-world issues better), and different antimicrobial agents including newly developed antimicrobial agents. We are a young and dynamic team, developing new antimicrobial solutions is also another wing within our group, hence characterizing such systems and interactions is at the forefront of what we do. We have automated many of the steps and are now generating deep learning methods. The next stage will be to utilize hyperspectral systems to perform these measurements; all using the spectra, analyzed using chemometrics, and all automated using AI. It’s really, really exciting.

(1) Truong, V. K.; Chapman, J.; Cozzolino, D. Monitoring the Bacterial Response to Antibiotic and Time Growth Using Near-Infrared Spectroscopy Combined with Machine Learning. 

https://doi.org/10.1007/s12161-021-01994-6

(2) Chapman. J.; Orrell‐Trigg, R.; Kwoon, K. Y.; Truong, V. K.; Cozzolino, D. A High‐Throughput and Machine Learning Resistance Monitoring System to Determine the Point of Resistance for 

with Tetracycline: Combining UV‐Visible Spectrophotometry with Principal Component Analysis. 

is an Associate Professor at RMIT University (Australia), and a Program Manager for the Applied Chemistry and Environmental Sciences which includes several degree programs, previously holding roles of Head of Science at Central Queensland University (Australia). He has a BSc (Hons) in Analytical Chemistry from the University of Plymouth (UK) and a PhD in Biofilms and Contamination of Surfaces Characterised Using Analytical Chemistry from Dublin City University (Ireland). His research is motivated by the development of rapid analytical tools to generate new sensors for applications in health, food, and the environment. James has authored >100 articles (i10-index 75, h-index 32, 3657 cites: Google Scholar). He is the Division Chair for The Royal Australian Chemical Institute for Analytical and Environmental Chemistry, a Fellow of the Royal Society of Chemistry and Royal Australian Chemical Institute, and a Chartered Chemist. He is an active member of the spectroscopy community and is a Program Advisory Committee for the Australian Nuclear Science and Technology Organisation (ANSTO).

James is a Principal Investigator and has a research group consisting of 10 PhD, 2 MSc, and 2 honours students as Principal and Associate Supervisor. He has completed 7 PhD students to completion since 2016. His group work on a range of environmental, health, and food based analytical chemistry-based projects underpinned by spectroscopy, chemometrics, and Artificial Intelligence.

In this interview, James Chapman discusses his current and future research efforts, and how combining spectroscopy with machine learning tools can change how bacterial research is conducted.

New Analytical Tools for Biomedical Applications Using Machine Learning and Spectroscopy

The Journal of Physical Chemistry Letters

 by Po-Yen Chen, Kiyou Shibata, Katsumi Hagita, Tomohiro Miyata, and Teruyasu Mizoguchi has showcased the potential of machine learning in predicting the ground-state electronic structure of organic molecules (1). Their study explores the use of core-loss spectra to reveal the unoccupied states and paves the way for new advancements in characterizing nanomaterials and designing tailored properties.

The analysis of local atomic and electronic structures is crucial for understanding and designing nanomaterials with specific properties. In this pursuit, core-loss spectroscopy, encompassing techniques like energy loss near-edge spectroscopy (ELNES) and X-ray absorption near-edge structure (XANES), has emerged as a valuable tool due to its ability to provide high spatial resolution and sensitivity in characterizing atomic and electronic structures of materials.

Core-loss spectroscopy provides insights into the atomic and electronic structures of materials. It encompasses various techniques, ELNES and XANES. ELNES involves the measurement of energy loss by electrons during interactions with atoms, providing information about the unoccupied states and local atomic environments. XANES, on the other hand, examines the absorption of X-rays by atoms, revealing details about the electronic structure and oxidation states of materials. Both techniques offer high spatial resolution and sensitivity, making them widely used in characterizing nanomaterials and understanding catalysis, chemical reactions, and the dynamics of materials. For carbon materials, ELNES spectra have been employed to discriminate local electronic and atomic structures, such as in graphene.

However, limitations arise when attempting to extract information about molecular properties governed by the ground-state electronic structure from core-loss spectra. To overcome this challenge, the team of researchers embarked on a groundbreaking endeavor, constructing a machine learning model capable of predicting the ground-state carbon s- and p-orbital partial density of states (PDOS) in both occupied and unoccupied states based on the C K-edge spectra.

The study also delved into the extrapolation prediction of PDOS for larger molecules using a model trained on smaller molecules. Remarkably, the researchers discovered that the exclusion of tiny molecules enhanced the extrapolation prediction performance. Furthermore, the application of smoothing preprocessing and training with specific noise data proved instrumental in improving PDOS prediction for spectra contaminated with noise, opening avenues for the utilization of the prediction model in experimental data.

The implications of this research are vast, as it unlocks new possibilities for understanding the intricate relationship between local atomic and electronic structures and the properties of nanomaterials. By harnessing the power of machine learning and core-loss spectroscopy, scientists can gain invaluable insights into catalysis, oxidation states, chemical reactions, and the structural dynamics of materials.

The groundbreaking work not only showcases the potential of machine learning in revealing the hidden secrets of organic molecules but also highlights its significance in the field of nanomaterial characterization and design.

(1) Chen, P.-Y.; Shibata, K.; Hagita, K.; Miyata, T.; Mizoguchi, T. Prediction of the Ground-State Electronic Structure from Core-Loss Spectra of Organic Molecules by Machine Learning. 

Revolutionary research demonstrates the power of machine learning in predicting the ground-state electronic structure of organic molecules from core-loss spectra, offering new insights into nanomaterial design. 

Unraveling the Mysteries of Molecular Structures: Machine Learning Enables Ground-State Electronic Structure Prediction from Core-Loss Spectra

In a quest to unlock valuable insights into 

 Tratt (RRT), a study conducted by a team of researchers from Qingdao Agricultural University and China Agricultural University has delved into the analysis of its quality and identification of geographical origin. Published in the journal 

, the study employed Fourier transform infrared spectroscopy (FT-IR) to unravel the nutritional components and distinguish the origins of RRT (1).

The fruit of Rosa roxburghii Tratt(Chinese:cili),Varieties grown in Guizhou,China | Image Credit: © wei - stock.adobe.com

Led by Shuqin Li, Yuemeng Lv, Qingli Yang, Juan Tang, Yue Huang, Haiyan Zhao, and Fangyuan Zhao, the study aimed to provide valuable information for the production of functional foods using RRT while also enabling the differentiation of RRT based on its geographical origin.

The team analyzed the nutritional components of RRT obtained from three different regions in China. They focused on key components such as vitamin C, polysaccharides, total flavonoids, and total phenolics, and evaluated their antioxidant activities using one-way analysis of variance (ANOVA). The results revealed significant variations in nutrient contents and antioxidant activities among RRT samples obtained from different regions.

FT-IR spectroscopy, combined with advanced statistical techniques including principal component analysis (PCA), stepwise linear discriminant analysis (SLDA), 

-NN), and support vector machine (SVM), played a pivotal role in establishing discriminant models to identify the geographical origin of RRT. The researchers identified characteristic fingerprint bands within the FT-IR spectra of RRT (specifically, in the ranges of 1679–1618 cm

) that were closely correlated with its geographical origins.

By utilizing SLDA, the team successfully developed a discriminant model that achieved high accuracy (reported as 100%) in classifying and identifying RRT samples from different regions. This breakthrough model demonstrated its effectiveness in determining the geographical origin of RRT, allowing for quicker and more accurate identification.

SLDA is a statistical technique used for feature selection and classification purposes. It is particularly effective when dealing with datasets containing a large number of variables or predictors. SLDA sequentially selects a subset of variables based on their discriminatory power, gradually building a discriminant model that optimally separates different classes or groups. The algorithm starts with an empty model and iteratively adds or removes variables based on their impact on the classification accuracy. This stepwise procedure continues until the desired number of variables, or a stopping criterion is reached. SLDA is widely employed machine learning to identify the most informative features and improve classification performance.

The study shed light on the prominent impact of geographical factors on the nutritional components and antioxidant activities found within RRT. The distinct variations observed among RRT samples from different regions highlight the influence of geographic factors on the plant's composition.

The findings also emphasized the potential of FT-IR spectroscopy as a rapid and precise tool for identifying the geographical origins of RRT. By utilizing characteristic fingerprint bands obtained through FT-IR analysis, researchers can effectively discern the origin of RRT samples, providing valuable information for quality control and traceability.

This study not only contributes to our understanding of RRT but also paves the way for improved quality analysis and geographical origin identification of this valuable botanical resource. The research demonstrates the power of advanced spectroscopic techniques in enhancing our knowledge of natural products and their origins.

(1) Li, S.; Lv, Y.; Yang, Q.; Tang, J.; Huang, Y.; Zhao, H.; Zhao, F. Quality analysis and geographical origin identification of Rosa roxburghii Tratt from three regions based on Fourier transform infrared spectroscopy. 

Spectrochimica Acta Part A: Mol. Biomol. Spectrosc. 

A new study leverages Fourier transform infrared spectroscopy (FT-IR) to determine the geographical origin and assess the quality of Rosa roxburghii Tratt (RRT), providing valuable insights for functional food production. 

Unveiling the Origin and Quality of Rosa roxburghii Tratt: Fourier Transform Infrared Spectroscopy Sheds Light

A groundbreaking study conducted at Ariel University's Physical Department in Israel has brought us closer to unraveling the mysteries of rare earth elements (REEs). Led by M. Gaft, the research team employed high-resolution laser-induced breakdown spectroscopy (LIBS) and LIBS-MLIF techniques to explore the molecular emissions of REEs in laser-induced plasma (LIP) and evaluate their potential for isotopic shift analysis. The results of their study have been published in the esteemed journal S

pectrochimica Acta Part B: Atomic Spectroscopy 

Piles of mined and refined rare earth elements. Generative AI. Generative, AI | Image Credit: © Southern Creative - stock.adobe.com

The study focused on determining the most suitable molecule for natural isotope analysis by considering both molecular weight and emission spectra. After meticulous investigation, it was discovered that GdO (gadolinium oxide) exhibits remarkable characteristics that make it an excellent choice for initiating isotopic shift determination in LIP. The isotopic shifts observed were found to depend on specific vibrational transitions, ranging from tenths to several cm

. Interestingly, for artificially induced isotopes, the lighter REEs such as Sc (scandium) and Y (yttrium) demonstrated potential as optimal candidates.

One of the key advancements highlighted in the study is the superiority of LIBS-MLIF over traditional LIBS. The technique of LIBS-MLIF offers significantly higher spectral resolution for vibrational transitions. This improved resolution facilitates better detection of vibrational transitions with elevated Δυ, a parameter that proves advantageous for isotopic shift analysis. Moreover, the rotational transitions also exhibited substantially higher spectral resolution, enabling more precise observations.

The findings of this study open up new avenues for researchers and scientists to delve deeper into the isotopic composition of rare earth elements. Understanding isotopic shifts in REEs holds immense potential for a wide range of applications, including environmental monitoring, geological studies, and even forensic investigations. By harnessing the power of high-resolution spectroscopy techniques like LIBS-MLIF, researchers can now explore and analyze the intricate molecular emissions of REEs in laser-induced plasma, offering a more comprehensive understanding of their isotopic behavior.

The research team at Ariel University's Physical Department have provided a valuable resource for future investigations into rare earth elements. As the world becomes increasingly reliant on rare earth elements for various technological advancements, this study paves the way for improved methodologies in analyzing and understanding these crucial elements. With this research, we move closer to unlocking the secrets hidden within the atomic world of rare earth elements, offering promising prospects for future discoveries and applications.

(1) Gaft, M.; Nagli, L.; Gorychev, A.; Raichlin, Y.High-resolution LIBS and LIBS-MLIF of REE molecular emission in laser-induced plasma. 

Spectrochimica Acta Part B: At. Spectrosc. 

New research uncovers novel findings regarding rare earth element (REE) isotopes through the utilization of high-resolution laser-induced breakdown spectroscopy (LIBS) and laser-induced breakdown spectroscopy-molecular laser-induced fluorescence (LIBS-MLIF) techniques. 

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