Biomolecular and bioanalytical applications of infrared spectroscopy - A review

Harmonic and anharmonic vibrational computations for biomolecular building blocks: Benchmarking DFT and basis sets by theoretical and experimental IR spectrum of glycine conformers

Advanced vibrational spectroscopic experiments have reached a level of sophistication that can only be matched by numerical simulations in order to provide an unequivocal analysis, a crucial step to understand the structure-function relationship of biomolecules. While density functional theory (DFT) has become the standard method when targeting medium-size or larger systems, the problem of its reliability and accuracy are well-known and have been abundantly documented. To establish a reliable computational protocol, especially when accuracy is critical, a tailored benchmark is usually required. This is generally done over a short list of known candidates, with the basis set often fixed a priori. In this work, we present a systematic study of the performance of DFT-based hybrid and double-hybrid functionals in the prediction of vibrational energies and infrared intensities at the harmonic level and beyond, considering anharmonic effects through vibrational perturbation theory at the second order. The study is performed for the six-lowest energy glycine conformers, utilizing available "state-of-the-art" accurate theoretical and experimental data as reference. Focusing on the most intense fundamental vibrations in the mid-infrared range of glycine conformers, the role of the basis sets is also investigated considering the balance between computational cost and accuracy. Targeting larger systems, a broad range of hybrid schemes with different computational costs is also tested.

Molecular dynamics simulations reliably identify vibrational modes in far-IR spectra of phospholipids

The properties of self-assembled phospholipid membranes are of essential importance in biochemistry and physical chemistry, providing a platform for many cellular life functions. Far-infrared (far-IR) vibrational spectroscopy, on the other hand, is a highly information-rich method to characterize intermolecular interactions and collective behaviour of lipids that can help explain, e.g., chain packing, thermodynamic phase behaviour, and sequestration. However, reliable interpretation of the far-IR spectra is still lacking. Here we present a molecular dynamics (MD) based approach to simulate vibrational modes of individual lipids and in an ensemble. The results are a good match to synchrotron far-IR measurements and enable identification of the molecular motions corresponding to each vibrational mode, thus allowing the correct interpretation of membrane spectra with high accuracy and resolving the longstanding ambiguities in the literature in this regard. Our results demonstrate the feasibility of using MD simulations for interpreting far-IR spectra broadly, opening new avenues for practical use of this powerful method.

Understanding the structures and interactions in gaseous mixtures of water-alcohol by high-resolution infrared spectroscopy

Interactions of water and chemical or bio-compound have a universal concern and have been extensively studied. For spectroscopic analysis, the complexity and the low resolution of the spectra make it difficult to obtain the spectral features showing the interactions. In this work, the structures and interactions in gaseous water and water-alcohol mixtures were studied using high-resolution infrared (HR-IR) spectroscopy. The spectral features of water clusters of different sizes, including dimer, trimer, tetramer and pentamer, were observed from the measured spectra of the samples in different volume concentrations, and the interactions of water and methanol/ethanol in the mixtures were obtained. In the analysis, a method based on principal component analysis was used to separate the overlapping spectra. In water-alcohol mixtures, when water is less, water molecules tend to interact with the OH groups on the exterior of the alcohol aggregate, and with the increase of water, a water cage forms around the aggregates. Furthermore, the ratio of the molecule number of methanol in the aggregate to that of water in the cage is around 1:2.3, and the ratio for ethanol is about 1:3.2.

Identifying hypothermia death in a mouse model by ATR-FTIR

The identification of hypothermia death (HD) is difficult for cadavers, especially the distinction from death due to alternative causes. A large number of studies have shown that brown adipose tissue (BAT) plays critical roles in thermoregulation of mammals. In this study, BAT of mice was used for the discrimination of HD using attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). A modified mouse HD model conducted by Feeney DM was used in this study to obtain infrared spectra of BAT. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to establish discrimination models. The PLS-DA and OPLS-DA models exhibit prominent discriminative efficiency, and the accuracy of HD identification using fingerprint regions and ratios of absorption intensity is near 100% in both the calibration and validation sets. Our preliminary study suggests that BAT may be an extremely effective target tissue for identification of cadavers of HD, and ATR-FTIR spectra combined with chemometrics have also shown potential for cadaver identification in forensic practice in a fast and accurate manner.

A review of concepts and methods for FTIR imaging of biomarker changes in the post-stroke brain

Stroke represents a core area of study in neurosciences and public health due to its global contribution toward mortality and disability. The intricate pathophysiology of stroke, including ischemic and hemorrhagic events, involves the interruption in oxygen and nutrient delivery to the brain. Disruption of these crucial processes in the central nervous system leads to metabolic dysregulation and cell death. Fourier transform infrared (FTIR) spectroscopy can simultaneously measure total protein and lipid content along with a number of key biomarkers within brain tissue that cannot be observed using conventional techniques. FTIR imaging provides the opportunity to visualize this information in tissue which has not been chemically treated prior to analysis, thus retaining the spatial distribution and in situ chemical information. Here we present a review of FTIR imaging methods for investigating the biomarker responses in the post-stroke brain.

Effect of polyethylene glycol on polysaccharides: From molecular modification, composite matrixes, synergetic properties to embeddable application in food fields

Polyethylene glycol (PEG) is a flexible, water-soluble, non-immunogenic, as well as biocompatible polymer, and it could synergize with polysaccharides for food applications. The molecular modification strategies, including covalent bond interactions (amino groups, carboxyl groups, aldehyde groups, tosylate groups, etc.), and non-covalent bond interactions (hydrogen bonding, electrostatic interactions, etc.) on PEG molecular chains are discussed. Its versatile structure, group modifiability, and amphiphilic block buildability could improve the functions of polysaccharides (e.g., chitosan, cellulose, starch, alginate, etc.) and adjust the properties of combined PEG/polysaccharides with outstanding chain tunability and matrix processability owing to plasticizing effects, compatibilizing effects, steric stabilizing effects and excluded volume effects by PEG, for achieving the diverse performance targets. The synergetic properties of PEG/polysaccharides with remarkable architecture were summarized, including mechanical properties, antibacterial activity, antioxidant performance, self-healing properties, carrier and delivery characteristics. The PEG/polysaccharides with excellent combined properties and embeddable merits illustrate potential applications including food packaging, food intelligent indication/detection, food 3D printing and nutraceutical food absorption. Additionally, prospects (like food innovation and preferable nutrient utilization) and key challenges (like structure-effectiveness-applicability relationship) for PEG/polysaccharides are proposed and addressed for food fields.

Beating the spectroscopic Rayleigh limit via post-processed heterodyne detection

Quantum-inspired superresolution methods surpass the Rayleigh limit in imaging, or the analogous Fourier limit in spectroscopy. This is achieved by carefully extracting the information carried in the emitted optical field by engineered measurements. An alternative to complex experimental setups is to use simple homodyne detection and customized data analysis. We experimentally investigate this method in the time-frequency domain and demonstrate the spectroscopic superresolution for two distinct types of light sources: thermal and phase-averaged coherent states. The experimental results are backed by theoretical predictions based on estimation theory.

Advances in the Regulation of Neural Function by Infrared Light

In recent years, with the rapid development of optical technology, infrared light has been increasingly used in biomedical fields. Research has shown that infrared light could play roles in light stimulation and biological regulation. Infrared light has been used to regulate neural function due to its high spatial resolution, safety and neural sensitivity and has been considered a useful method to replace traditional neural regulation approaches. Infrared neuromodulation methods have been used for neural activation, central nervous system disorder treatment and cognitive enhancement. Research on the regulation of neural function by infrared light stimulation began only recently, and the underlying mechanism remains unclear. This article reviews the characteristics of infrared light, the advantages and disadvantages of infrared neuromodulation, its effects on improving individual health, and its mechanism. This article aims to provide a reference for future research on the use of infrared neural regulation to treat neuropsychological disorders.

Rapid discrimination of Lentilactobacillus parabuchneri biofilms via in situ infrared spectroscopy

Microbial contamination in food industry is a source of foodborne illnesses and biofilm-related diseases. In particular, biogenic amines (BAs) accumulated in fermented foods via lactic acid bacterial activity exert toxic effects on human health. Among these, biofilms of histamine-producer Lentilactobacillus parabuchneri strains adherent at food processing equipment surfaces can cause food spoilage and poisoning. Understanding the chain of contamination is closely related to elucidating molecular mechanisms of biofilm formation. In the present study, an innovative approach using integrated chemical sensing technologies is demonstrated to fundamentally understand the temporal behavior of biofilms at the molecular level by combining mid-infrared (MIR) spectroscopy and fluorescence sensing strategies. Using these concepts, the biofilm forming capacity of six cheese-isolated L. parabuchneri strains (IPLA 11151, 11150, 11129, 11125, 11122 and 11117) was examined. The cut-off values for the biofilm production ability of each strain were quantified using crystal violet (CV) assays. Real-time infrared attenuated total reflection spectroscopy (IR-ATR) combined with fluorescence quenching oxygen sensing provides insight into distinct molecular mechanisms for each strain. IR spectra showed significant changes in characteristic bands of amides, lactate, nucleic acids, and extracellular polymeric substances (i.e., lipopolysaccharides, phospholipids, phosphodiester, peptidoglycan, etc.), which are major contributors to biofilm maturation involved in the initial adhesion processes. Chemometric methods including principal component analysis and partial least square-discriminant analysis facilitated the rapid determination and classification of cheese isolated L. parabuchneri strains unambiguously differentiating the IR signatures based on their ability to produce biofilm. All biofilms were morphologically characterized by confocal laser scanning microscopy on relevant industrial equipment surfaces. In summary, this innovative approach combining MIR spectroscopy with luminescence sensing enables real-time insight into the molecular composition and formation of L. parabuchneri biofilms.

Exploration of macromolecular phenotype of human skeletal muscle in diabetes using infrared spectroscopy

Introduction

The global burden of diabetes mellitus is escalating, and more efficient investigative strategies are needed for a deeper understanding of underlying pathophysiological mechanisms. The crucial role of skeletal muscle in carbohydrate and lipid metabolism makes it one of the most susceptible tissues to diabetes-related metabolic disorders. In tissue studies, conventional histochemical methods have several technical limitations and have been shown to inadequately characterise the biomolecular phenotype of skeletal muscle to provide a holistic view of the pathologically altered proportions of macromolecular constituents.

Materials and methods

In this pilot study, we examined the composition of five different human skeletal muscles from male donors diagnosed with type 2 diabetes and non-diabetic controls. We analysed the lipid, glycogen, and collagen content in the muscles in a traditional manner with histochemical assays using different staining techniques. This served as a reference for comparison with the unconventional analysis of tissue composition using Fourier-transform infrared spectroscopy as an alternative methodological approach.

Results

A thorough chemometric post-processing of the infrared spectra using a multi-stage spectral decomposition allowed the simultaneous identification of various compositional details from a vibrational spectrum measured in a single experiment. We obtained multifaceted information about the proportions of the different macromolecular constituents of skeletal muscle, which even allowed us to distinguish protein constituents with different structural properties. The most important methodological steps for a comprehensive insight into muscle composition have thus been set and parameters identified that can be used for the comparison between healthy and diabetic muscles.

Conclusion

We have established a methodological framework based on vibrational spectroscopy for the detailed macromolecular analysis of human skeletal muscle that can effectively complement or may even serve as an alternative to histochemical assays. As this is a pilot study with relatively small sample sets, we remain cautious at this stage in drawing definitive conclusions about diabetes-related changes in skeletal muscle composition. However, the main focus and contribution of our work has been to provide an alternative, simple and efficient approach for this purpose. We are confident that we have achieved this goal and have brought our methodology to a level from which it can be successfully transferred to a large-scale study that allows the effects of diabetes on skeletal muscle composition and the interrelationships between the macromolecular tissue alterations due to diabetes to be investigated.

Precise temperature control and rapid heating/cooling of infrared spectroscopy samples with a two-stage thermoelectric device

The design and performance of an apparatus for heating and cooling samples during variable temperature infrared spectroscopy studies are described. The apparatus incorporates two thermoelectric device modules in a stacked configuration. The cascaded devices are powered in parallel and contained within a metal enclosure that maintains their alignment and applies clamping pressure between them to maximize thermal conductivity. By using this apparatus, sample temperatures can be increased or decreased at 2 °C s-1 rates and isothermal temperatures can be maintained precisely (±0.1 °C). The rapid heating and cooling capabilities of the apparatus facilitate programmed temperature step heating/cooling profiles with isothermal infrared spectrum measurements at pre-selected temperatures. Using linear heating and cooling temperature ramps, subtle temperature-dependent poly(styrene) infrared spectrum changes are elucidated and correlated with sample temperature. Results obtained by using a temperature step sample heating profile are compared with those obtained by using linear temperature ramp heating and cooling to characterize silica gel dehydration and re-hydration processes. By comparing infrared spectra acquired at different temperatures while heating and cooling the sample, silica gel spectrum changes associated with water desorption/adsorption and the thermal expansion/contraction of the Si-O-Si network are differentiated.

Sample Preparation Method for MALDI Mass Spectrometry Imaging of Fresh-Frozen Spines

Technologies assessing the lipidomics, genomics, epigenomics, transcriptomics, and proteomics of tissue samples at single-cell resolution have deepened our understanding of physiology and pathophysiology at an unprecedented level of detail. However, the study of single-cell spatial metabolomics in undecalcified bones faces several significant challenges, such as the fragility of bone, which often requires decalcification or fixation leading to the degradation or removal of lipids and other molecules. As such, we describe a method for performing mass spectrometry imaging on undecalcified spine that is compatible with other spatial omics measurements. In brief, we use fresh-frozen rat spines and a system of carboxyl methylcellulose embedding, cryofilm, and polytetrafluoroethylene rollers to maintain tissue integrity while avoiding signal loss from variations in laser focus and artifacts from traditional tissue processing. This reveals various tissue types and lipidomic profiles of spinal regions at 10 μm spatial resolutions using matrix-assisted laser desorption/ionization mass spectrometry imaging. We expect this method to be adapted and applied to the analysis of the spinal cord, shedding light on the mechanistic aspects of cellular heterogeneity, development, and disease pathogenesis underlying different bone-related conditions and diseases. This study furthers the methodology for high spatial metabolomics of spines and adds to the collective efforts to achieve a holistic understanding of diseases via single-cell spatial multiomics.

New Approaches to Stretched Film Sample Alignment and Data Collection for Vibrational Linear Dichroism

Rapid measurements of vibrational linear dichroism (VLD) infrared spectra are shown to be possible by using stretched polymer films and an extension of existing instrumentation designed for vibrational circular dichroism spectroscopy. Earlier techniques can be extended using additional inexpensive polymer substrates to record good-quality VLD spectra of a significantly wider range of compounds with comparatively short sample-preparation times. The polymer substrates used, polyethylene and polytetrafluoroethylene, are commonly available and inexpensive, and samples are more easily prepared than that for many earlier stretched-film and crystal studies. Data are presented for neutral hydrophobic organic molecules on hydrophobic films including acridine, anthracene, fluorene, and recently synthesized S-(4-((4-cyanophenyl)ethynyl)phenyl)ethanethioate. We extend the approach to polar or ionic species, including 2,2'-bipyridine, 1,10-phenanthroline, and sodium dodecyl sulfate, by oxidizing polyethylene films to change their wetting properties. The combination of new instrumentation and modified sample preparation methods is useful in basic spectroscopy for untangling and assigning complicated infrared spectra. Nevertheless, it is not a panacea as surface-adsorbed molecules are often not monodispersed, and higher analyte concentrations can lead to aggregation and resonance phenomena that have previously been observed for infrared spectra on surfaces. These effects can be assessed by varying the sample concentration. The focus of this paper is experimental, and detailed analysis of most of the spectra lies outside its scope, including some well-studied compounds such as acridine and anthracene that allow comparisons with earlier research.

Spatial lipidomics of fresh-frozen spines

Technologies assessing the lipidomics, genomics, epigenomics, transcriptomics, and proteomics of tissue samples at single-cell resolution have deepened our understanding of physiology and pathophysiology at an unprecedented level of detail. However, the study of single-cell spatial metabolomics in undecalcified bones faces several significant challenges, such as the fragility of bone which often requires decalcification or fixation leading to the degradation or removal of lipids and other molecules and. As such, we describe a method for performing mass spectrometry imaging on undecalcified spine that is compatible with other spatial omics measurements. In brief, we use fresh-freeze rat spines and a system of carboxyl methylcellulose embedding, cryofilm, and polytetrafluoroethylene rollers to maintain tissue integrity, while avoiding signal loss from variations in laser focus and artifacts from traditional tissue processing. This reveals various tissue types and lipidomic profiles of spinal regions at 10 μm spatial resolutions using matrix-assisted laser desorption/ionization mass spectrometry imaging. We expect this method to be adapted and applied to the analysis of spinal cord, shedding light on the mechanistic aspects of cellular heterogeneity, development, and disease pathogenesis underlying different bone-related conditions and diseases. This study furthers the methodology for high spatial metabolomics of spines, as well as adds to the collective efforts to achieve a holistic understanding of diseases via single-cell spatial multi-omics.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

FTIR study on the CO interactions of benzeneacetamide in solution

The main attention of present work is to study how benzeneacetamide interacts with other molecules in organic solvents. The frequencies of CO groups in 18 solvents were obtained by using infrared spectroscopy. The empirical parameters of the solvents as the acceptor number (AN), the van der Waals interaction parameters (S_VW) and the linear solvation energy relationships (LSER) were correlated with the frequencies of carbonyl stretching vibration (ν(CO)) of benzeneacetamide to estimate the contributions in intermolecular interactions. The results showed that solvent effects on the frequencies of CO stretching vibrations of benzeneacetamide were obvious. Self-association of alkanol leads to enhancement of O-H⋯O=C hydrogen bond strength and red-shift of the ν(CO) peak. The ν(CO) of benzeneacetamide is more vulnerable to the acidity of the hydrogen bond donor of the solvent. This research contributes to a thorough understanding of the molecular interactions and microstructures in the liquid mixtures.

Vibrational spectroscopies for biochemical investigation of X-ray exposure effects on SH-SY5Y human neuroblastoma cells

Neuroblastoma is the most recurring cancer in childhood and adolescence. The SH-SY5Y neuroblastoma cell line is generally adopted for elaborating new therapeutical approaches and/or elaborating strategies for the prevention of central nervous system disturbances. In fact, it represents a valid model system for investigating in vitro the effects on the brain of X-ray exposure using vibrational spectroscopies that can detect early radiation-induced molecular alterations of potential clinical usefulness. In recent years, we dedicated significant efforts in the use of Fourier-transform and Raman microspectroscopy techniques for characterizing such radiation-induced effects on SH-SY5Y cells by examining the contributions from different cell components (DNA, proteins, lipids, and carbohydrates) to the vibrational spectra. In this review, we aim at revising and comparing the main results of our studies to provide a wide outlook of the latest outcomes and a framework for future radiobiology research using vibrational spectroscopies. A short description of our experimental approaches and data analysis procedures is also reported.

A combined X-ray fluorescence and infrared microspectroscopy study for new insights into elemental-biomolecular obesity-induced changes in rat brain structures

The objective of our research was to determine the brain changes at the molecular and elemental levels typical of early-stage obesity. Therefore a combined approach using Fourier transform infrared micro-spectroscopy (FTIR-MS) and synchrotron radiation induced X-ray fluorescence (SRXRF) was introduced to evaluate some brain macromolecular and elemental parameters in high-calorie diet (HCD)- induced obese rats (OB, n = 6) and in their lean counterparts (L, n = 6). A HCD was found to alter the lipid- and protein- related structure and elemental composition of the certain brain areas important for energy homeostasis. The increased lipid unsaturation in the frontal cortex and ventral tegmental area, the increased fatty acyl chain length in the lateral hypothalamus and substantia nigra as well as the decreased both protein α helix to protein β- sheet ratio and the percentage fraction of β-turns and β-sheets in the nucleus accumbens were revealed in the OB group reflecting obesity-related brain biomolecular aberrations. In addition, the certain brain elements including P, K and Ca were found to differentiate the lean and obese groups at the best extent. We can conclude that HCD-induced obesity triggers lipid- and protein- related structural changes as well as elemental redistribution within various brain structures important for energy homeostasis. In addition, an approach applying combined X-ray and infrared spectroscopy was shown to be a reliable tool for identifying elemental-biomolecular rat brain changes for better understanding the interplay between the chemical and structural processes involved in appetite control.

Unsupervised model adaptation for multivariate calibration by domain adaptation-regularization based kernel partial least square

In chemometrics, calibration model adaptation is desired when training- and test-samples come from different distributions. Domain-invariant feature representation is currently a successful strategy to realize model adaptation and has received wide attention. The paper presents a nonlinear unsupervised model adaptation method termed as domain adaption regularization-based kernel partial least squares regression (DarKPLS). DarKPLS aims to minimize the source and target distributions in a low-dimensional latent space projected from the reproducing kernel Hilbert space (RKHS) generated with the labeled source data and unlabeled target data. Specially, the distributional means and variances between source and target latent variables are aligned in the RKHS. By extending existing domain invariant partial least square regression (di-PLS) with the projected maximum mean discrepancy (PMMD) to reduce the mean discrepancy in the RKHS further, DarKPLS could realize fine-grained domain alignment that further improves the adaptation performance. DarKPLS is applied to the γ-polyglutamic acid fermentation dataset, tobacco dataset and corn dataset, and it demonstrates improved prediction results in comparison with No adaptation partial least squares (PLS), null augmented regression (NAR), extended linear joint trained framework (ExtJT), scatter component analysis (SCA) and domain-invariant iterative partial least squares (DIPALS).

Rapid discrimination of Brucellosis in sheep using serum Fourier transform infrared spectroscopy combined with PCA-LDA algorithm

Brucellosis in sheep is an infectious disease caused by Brucella melitensis in sheep. The current conventional serological methods for screening Brucella-infected sheep have the disadvantage of time consuming and low accuracy, so a simple, rapid and highly accurate screening method is needed. The aim of this study was to evaluate the feasibility of diagnosing Brucella-infected sheep by serum samples based on the Fourier transform infrared (FTIR) spectroscopy. In this study, FTIR spectroscopy of serum from Brucella-infected sheep (n=102) and healthy sheep (n=125) revealed abnormal protein and lipid metabolism in serum from Brucella-infected sheep compared to healthy sheep. Principal component analysis-Linear discriminant analysis (PCA-LDA) method was used to differentiate the FTIR spectra of serum from Brucella-infected sheep and healthy sheep in the protein band (3700-3090 cm^-1) and lipid band (3000-2800 cm^-1), and its overall diagnostic accuracy was 100% (sensitivity 100%, specificity 100%). In conclusion, our results suggest that serum FTIR spectroscopy combined with PCA-LDA algorithm has great potential for brucellosis in sheep screening.

Infrared analysis in the aqueous humor of patients with uveitis: Preliminary results

Inflammatory processes affecting the uvea result in a temporary o permanent blurred vision and represent an important cause of visual impairment worldwide. It is often hard to make a precise diagnosis which is dependent on the clinical expertise, diagnostic tests, laboratory investigations in blood and sometimes in the aqueous humor (AH). With the aim of obtaining proof of principle Fourier Transformed Infrared (FT-IR) absorbance spectroscopy was applied to study the molecular composition of 72 AH samples collected in 26 patients with uveitis and in 44 controls. The unsupervised exploration of the internal structure of the dataset by principal component analysis reduced hundreds IR variables to those most representative allowing to obtain the predictive model that distinguished the AH spectra of patients with uveitis from controls. The same result was obtained by unsupervised agglomerative cluster analysis. After labeling the spectra with some clinical information it was observed that most severe uveitis with active processes were grouped separately from chronic and relapsing uveitis and controls. The consistence of prediction models is discussed in the light of supporting etiological diagnosis by machine learning processes. In conclusion, proof of principle has been obtained that the IR spectral pattern of AH may reflect particular uveal diseases.

Vibrational Spectroscopy as a Tool for Bioanalytical and Biomonitoring Studies

The review briefly describes various types of infrared (IR) and Raman spectroscopy methods. At the beginning of the review, the basic concepts of biological methods of environmental monitoring, namely bioanalytical and biomonitoring methods, are briefly considered. The main part of the review describes the basic principles and concepts of vibration spectroscopy and microspectrophotometry, in particular IR spectroscopy, mid- and near-IR spectroscopy, IR microspectroscopy, Raman spectroscopy, resonance Raman spectroscopy, Surface-enhanced Raman spectroscopy, and Raman microscopy. Examples of the use of various methods of vibration spectroscopy for the study of biological samples, especially in the context of environmental monitoring, are given. Based on the described results, the authors conclude that the near-IR spectroscopy-based methods are the most convenient for environmental studies, and the relevance of the use of IR and Raman spectroscopy in environmental monitoring will increase with time.

The Potential Use of Near- and Mid-Infrared Spectroscopy in Kidney Diseases

Traditional renal biomarkers such as serum creatinine and albuminuria/proteinuria are rather insensitive since they change later in the course of the disease. In order to determine the extent and type of kidney injury, as well as to administer the proper therapy and enhance patient management, new techniques for the detection of deterioration of the kidney function are urgently needed. Infrared spectroscopy is a label-free and non-destructive technique having the potential to be a vital tool for quick and inexpensive routine clinical diagnosis of kidney disorders. The aim of this review is to provide an overview of near- and mid-infrared spectroscopy applications in patients with acute kidney injury and chronic kidney disease (e.g., diabetic nephropathy and glomerulonephritis).

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part II. Recent noteworthy developments

This comprehensive survey review compiles noteworthy developments and new concepts of two-dimensional correlation spectroscopy (2D-COS) for the last two years. It covers review articles, books, proceedings, and numerous research papers published on 2D-COS, as well as patent and publication trends. 2D-COS continues to evolve and grow with new significant developments and versatile applications in diverse scientific fields. The healthy, vigorous, and diverse progress of 2D-COS studies in many fields strongly confirms that it is well accepted as a powerful analytical technique to provide an in-depth understanding of systems of interest.

Rapid Identification of Insecticide- and Herbicide-Tolerant Genetically Modified Maize Using Mid-Infrared Spectroscopy

Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize.

The Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery

In conjunction with imaging analysis, pathology-based assessments of biopsied tissue are the gold standard for diagnosing solid tumors. However, the disadvantages of tissue biopsies, such as being invasive, time-consuming, and labor-intensive, have urged the development of an alternate method, liquid biopsy, that involves sampling and clinical assessment of various bodily fluids for cancer diagnosis. Meanwhile, extracellular vesicles (EVs) are circulating biomarkers that carry molecular profiles of their cell or tissue origins and have emerged as one of the most promising biomarkers for cancer. Owing to the biological information that can be obtained through EVs' membrane surface markers and their cargo loaded with biomolecules such as nucleic acids, proteins, and lipids, EVs have become useful in cancer diagnosis and therapeutic applications. Fourier-transform infrared spectroscopy (FTIR) allows rapid, non-destructive, label-free molecular profiling of EVs with minimal sample preparation. Since the heterogeneity of EV subpopulations may result in complicated FTIR spectra that are highly diverse, computational-assisted FTIR spectroscopy is employed in many studies to provide fingerprint spectra of malignant and non-malignant samples, allowing classification with high accuracy, specificity, and sensitivity. In view of this, FTIR-EV approach carries a great potential in cancer detection. The progression of FTIR-based biomarker identification in EV research, the rationale of the integration of a computationally assisted approach, along with the challenges of clinical translation are the focus of this review.

Neither too little nor too much: Finding the ideal proportion of excipients using confocal Raman and chemometrics

The applications of Raman imaging in pharmaceutical field are ever-increasing due its ability to obtain spatial and spectral information simultaneously, once it allows determine the chemical distribution of compounds. In this sense, it is used to study homogeneity, of paramount importance during the development of pharmaceutical formulations due to its relation to stability, safety and efficacy. Commonly, just surface is analyzed, but confocal Raman spectroscopy can also characterize the inner part of samples, allowing to determine phase separation in the early stages. In this sense, confocal 3D Raman microscopy was crucial to obtain the optimal proportion of Apifil®, Capryol® 90 and Transcutol® to promote controlled release of the local anesthetic butamben (BTB). 3D chemical maps were obtained by classical least squares (CLS) using pure compound spectra as S matrix, showing that chemical distribution throughout the material was different. Knowing that the composition of samples affects the homogeneity parameter, standard deviation and distributional homogeneity index (DHI) were used in mixture experimental design (DoE). From this analysis, it was revealed that a correct amount of Capryol® 90 enhances both miscibility and solubility. Furthermore, suitable miscibility was observed in two ratio proportions of excipients with a desirability of 0.783 and 0.742. These results unequivocally demonstrated that confocal Raman microscopy combined to DoE can bring pharmaceutical development to a higher level.

High-performance Bloch surface wave biosensor based on a prism-coupled porous silicon composite structure for the detection of hemoglobin

Biosensors have various potential applications in biomedical research and clinical diagnostic, especially in detection of biomolecules in highly diluted solutions. In this study, a high-performance Bloch surface wave biosensor was constructed for the detection of hemoglobin. The procedure consisted of designing a porous silicon-based Kretschmann configuration to ensure excitation of the Bloch surface wave. The performance of the resulting sensor was then optimized by adjusting the buffer layer parameters based on the impedance matching method. The results showed an increase in the quality factor and figure of merit of the biosensor as a function of the decrease in thickness and refractive index of the buffer layer. The combination of the two optimization methods resulted in the quality factor and figure of merit of the optimized biosensor reaching as high as Q = 6967.4 and FOM = 11050RIU^-1, respectively. In sum, the designed biosensor with high performance looks promising for future detection of hemoglobin.

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.

Application of FTIR Spectroscopy to Detect Changes in Skeletal Muscle Composition Due to Obesity with Insulin Resistance and STZ-Induced Diabetes

Age, obesity, and diabetes mellitus are pathophysiologically interconnected factors that significantly contribute to the global burden of non-communicable diseases. These metabolic conditions are associated with impaired insulin function, which disrupts the metabolism of carbohydrates, lipids, and proteins and can lead to structural and functional changes in skeletal muscle. Therefore, the alterations in the macromolecular composition of skeletal muscle may provide an indication of the underlying mechanisms of insulin-related disorders. The aim of this study was to investigate the potential of Fourier transform infrared (FTIR) spectroscopy to reveal the changes in macromolecular composition in weight-bearing and non-weight-bearing muscles of old, obese, insulin-resistant, and young streptozotocin (STZ)-induced diabetic mice. The efficiency of FTIR spectroscopy was evaluated by comparison with the results of gold-standard histochemical techniques. The differences in biomolecular phenotypes and the alterations in muscle composition in relation to their functional properties observed from FTIR spectra suggest that FTIR spectroscopy can detect most of the changes observed in muscle tissue by histochemical analyses and more. Therefore, it could be used as an effective alternative because it allows for the complete characterization of macromolecular composition in a single, relatively simple experiment, avoiding some obvious drawbacks of histochemical methods.

In silico NIR spectroscopy - A review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference

Quantum mechanical calculations are routinely used as a major support in mid-infrared (MIR) and Raman spectroscopy. In contrast, practical limitations for long time formed a barrier to developing a similar synergy between near-infrared (NIR) spectroscopy and computational chemistry. Recent advances in theoretical methods suitable for calculation of NIR spectra opened the pathway to modeling NIR spectra of various molecules. Accurate theoretical reproduction of NIR spectra of molecules reaching the size of long-chain fatty acids was accomplished so far. In silico NIR spectroscopy, where the spectra are calculated ab initio, provides substantial improvement in our understanding of the overtones and combination bands that overlap in staggering numbers and create complex lineshape typical for NIR spectra. This improves the comprehension of the spectral information enabling access to rich and detail molecular footprint, essential for fundamental research and useful in routine analysis by NIR spectroscopy and chemometrics. This review article summarizes the most recent accomplishments in the emerging field with examples of simulated NIR spectra of molecules reaching long-chain fatty acids and polymers. In addition to detailed NIR band assignments and new physical insights, simulated spectra enable innovative support in applications. Understanding of the difference in the performance observed between miniaturized NIR spectrometers and chemical interpretation of the chemometric models are noteworthy here. These new elements integrated into NIR spectroscopy framework enable a knowledge-based design of the analysis with comprehension of the processed chemical information.

Site-Specific Evaluation of Bioactive Coumarin-Loaded Dendrimer G4 Nanoparticles against Methicillin Resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is a foremost treatment challenge in today's clinical practice. Natural coumarins contain a variety of bioactivities and have the ability to alter resistance in several ways. In developing effective drug delivery methods, the goal is to maximize biocompatibility while minimizing toxicity. With this in mind, this work investigated the site-specific potential of dendrimer G4 poloxamer nanoparticles loaded with bioactive coumarin. The goal of the current work is to deliver a complete evaluation of dendrimer G4 poloxamer nanoparticles against MRSA. Coumarin-loaded dendrimer G4 poloxamer nanoparticles were thoroughly investigated and characterized using various techniques, including particle size, shape, entrapment efficiency, in vitro drug release, hemolysis assay, cytotoxicity, antibacterial activity, and bactericidal kinetics. Studies showed that the newly developed dendrimer G4 poloxamer nanoparticles exhibited significantly lower levels of hemolysis and cytotoxicity. The results showed that the in vitro drug release of coumarin from dendrimer G4 poloxamer nanoparticles was slower compared to coumarin in its free form. This innovative therapeutic delivery technology may enhance the defense of coumarin against MRSA.

Amomum longiligulare polysaccharide 1- PLGA nanoparticle promotes the immune activities of T lymphocytes and dendritic cells

Amomum longiligulare polysaccharide 1 (ALP1) was extracted from Amomum longiligulare T.L. Wu fruits and the poly (lactic-co-glycolic acid) nanoparticle enveloping ALP1 (ALPP) showed a good promoting effect on the activation of macrophages in our previous study. To further understand the immunomodulatory property of ALPP, the effect of ALPP on T lymphocytes and dendritic cells was investigated in the present study. The proliferation rates of chicken T lymphocytes and chicken bone marrow dendritic cells (chBM-DCs) that were treated with ALP1 or ALPP were determined by using MTT method. Meanwhile, the relative mRNA levels of cytokines from T lymphocytes and surface molecules of chBM-DCs were determined by using qRT-PCR method. In addition, the drug uptake capacity of chBM-DCs was also tested. As a result, the promoting effect on the proliferation of T lymphocytes and the Th1-type immune response of ALPP was better than that of ALP1. In addition, ALPP was much more effectively swallowed by chBM-DCs so that its promoting effect on the proliferation and maturation of chBM-DCs was higher than that of ALP1. To conclude, ALPP had a stronger immunomodulatory activity than ALP1, and showed the potential to become a new type of immune booster.

Rapid Classification and Recognition Method of the Species and Chemotypes of Essential Oils by ATR-FTIR Spectroscopy Coupled with Chemometrics

In the present work, the applicability of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric tools in recognizing essential oils (EOs) for routine control, was evaluated. EOs belonging to Mentha, Cymbopogon, and Lavandula families and to S. rosmarinus and T. vulgaris species were analyzed, and the performance of several untargeted approaches, based on the synergistic combination of ATR-FTIR and Partial Least Squares Discriminant Analysis (PLS-DA), was tested to classify the species and chemotypes. Different spectra pre-processing methods were employed, and the robustness of the built models was tested by means of a Receiver Operating Characteristic (ROC) curve and random permutations test. The application of these approaches revealed fruitful results in terms of sensitivity and specificity, highlighting the potentiality of ATR-FTIR and chemometrics techniques to be used as a sensitive, cost-effective, and rapid tool to differentiate EO samples according to their species and chemotype.

Synchrotron Infrared Microspectroscopy for Stem Cell Research

Stem cells have shown great potential functions for tissue regeneration and repair because of their unlimited self-renewal and differentiation. Stem cells reside in their niches, making them a hotspot for the development and diagnosis of diseases. Complex interactions between niches and stem cells create the balance between differentiation, self-renewal, maturation, and proliferation. However, the multi-facet applications of stem cells have been challenged since the complicated responses of stem cells to biological processes were explored along with the limitations of current systems or methods. Emerging evidence highlights that synchrotron infrared microspectroscopy, known as synchrotron radiation-based Fourier transform infrared microspectroscopy, has been investigated as a potentially attractive technology with its non-invasive and non-biological probes in stem cell research. With their unique vibration bands, the quantitative mapping of the content and distribution of biomolecules can be detected and characterized in cells or tissues. In this review, we focus on the potential applications of synchrotron infrared microspectroscopy for investigating the differentiation and fate determination of stem cells.

Tolerance and Cadmium (Cd) Immobilization by Native Bacteria Isolated in Cocoa Soils with Increased Metal Content

Twelve cadmium native bacteria previously isolated in soils of cocoa farms located in the western Colombian Andes (Santander), and tolerant to 2500 µM CdCl2 (120 mg Cd/L), were chosen in order to test their tolerance and Cd immobilization using liquid culture medium (Nutritive broth) at different concentrations of heavy metals. Furthermore, in the greenhouse experiments, the strains Exiguobacterium sp. (11-4A), Klebsiella variicola sp. (18-4B), and Enterobacter sp. (29-4B) were applied in combined treatments using CCN51 cacao genotype seeds grown in soil with different concentrations of Cd. All bacterial strains’ cell morphologies were deformed in TEM pictures, which also identified six strain interactions with biosorption and four strain capacities for bioaccumulation; FT-IR suggested that the amide, carbonyl, hydroxyl, ethyl, and phosphate groups on the bacteria biomass were the main Cd binding sites. In the pot experiments, the concentration of Cd was distributed throughout the cacao plant, but certain degrees of immobilization of Cd can occur in soil to prevent an increase in this level in roots with the presence of Klebsiella sp.

Infrared Spectroscopy–Quo Vadis?

Given the exquisite capability of direct, non-destructive label-free sensing of molecular transitions, IR spectroscopy has become a ubiquitous and versatile analytical tool. IR application scenarios range from industrial manufacturing processes, surveillance tasks and environmental monitoring to elaborate evaluation of (bio)medical samples. Given recent developments in associated fields, IR spectroscopic devices increasingly evolve into reliable and robust tools for quality control purposes, for rapid analysis within at-line, in-line or on-line processes, and even for bed-side monitoring of patient health indicators. With the opportunity to guide light at or within dedicated optical structures, remote sensing as well as high-throughput sensing scenarios are being addressed by appropriate IR methodologies. In the present focused article, selected perspectives on future directions for IR spectroscopic tools and their applications are discussed. These visions are accompanied by a short introduction to the historic development, current trends, and emerging technological opportunities guiding the future path IR spectroscopy may take. Highlighted state-of-the art implementations along with novel concepts enhancing the performance of IR sensors are presented together with cutting-edge developments in related fields that drive IR spectroscopy forward in its role as a versatile analytical technology with a bright past and an even brighter future.

Development of Gum-Acacia-Stabilized Silver Nanoparticles Gel of Rutin against Candida albicans

Candida spp. is one of the most causative pathogens responsible for fungal infections. It is often a hospital-acquired form of sepsis with a very high number of deaths. Currently, the most effective anti-fungal agents are based on polyenes or echinocandins. However, long-term treatments or repeated use of these anti-fungals lead to therapy limitations. Current research is urgently needed to overcome existing challenges for antimicrobials from natural sources. This study aims to determine the anti-fungal activity of rutin, which has the advantage of increasing the therapeutic value. Because of its low solubility in water and oils, rutin is limited in use. To address these constraints, we encapsulated rutin in a nanocarrier system. Silver nanoparticles (SNPs) and gum acacia (GAs) are emerging as attractive components and are widely studied as biologically safe nanomaterials/carrier systems for various drugs. Still, they are barely investigated as nano-sized vectors for the targeted delivery of rutin. In the present work, GA stabilised SNPs of rutin were successfully formulated and evaluated. It was later incorporated into carbapol 940 gels and formed SNP gels. Rutin-SNPs were developed with a consistent size in the nano range of 59.67 ± 44.24 nm in size, 0.295 ± 0.014 polydispersity index (PDI), and −11.2 ± 6.66 mV zeta potential. The drug released was found to be 81. 26 ± 4.06% in 600 min by following zero-order kinetics. The rutin-SNP gel showed considerable activity against C. albicans skin candidiasis at MIC 1.56 g/mL. The developed formulation was biocompatible. This first-ever interdisciplinary study suggests that the rutin-SNPs gel could play a vital role in drug resistance in this fungal pathogen.

A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra

Vibrational spectroscopies provide information about the biochemical and structural environment of molecular functional groups inside samples. Over the past few decades, Raman and infrared-absorption-based techniques have been extensively used to investigate biological materials under different pathological conditions. Interesting results have been obtained, so these techniques have been proposed for use in a clinical setting for diagnostic purposes, as complementary tools to conventional cytological and histological techniques. In most cases, the differences between vibrational spectra measured for healthy and diseased samples are small, even if these small differences could contain useful information to be used in the diagnostic field. Therefore, the interpretation of the results requires the use of analysis techniques able to highlight the minimal spectral variations that characterize a dataset of measurements acquired on healthy samples from a dataset of measurements relating to samples in which a pathology occurs. Multivariate analysis techniques, which can handle large datasets and explore spectral information simultaneously, are suitable for this purpose. In the present study, two multivariate statistical techniques, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to analyse three different datasets of vibrational spectra, each one including spectra of two different classes: (i) a simulated dataset comprising control-like and exposed-like spectra, (ii) a dataset of Raman spectra measured for control and proton beam-exposed MCF10A breast cells and (iii) a dataset of FTIR spectra measured for malignant non-metastatic MCF7 and metastatic MDA-MB-231 breast cancer cells. Both PCA-LDA and PLS-DA techniques were first used to build a discrimination model by using calibration sets of spectra extracted from the three datasets. Then, the classification performance was established by using test sets of unknown spectra. The achieved results point out that the built classification models were able to distinguish the different spectra types with accuracy between 93% and 100%, sensitivity between 86% and 100% and specificity between 90% and 100%. The present study confirms that vibrational spectroscopy combined with multivariate analysis techniques has considerable potential for establishing reliable diagnostic models.

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.

Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food

In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.

Non-destructive molecular FTIR spectromicroscopy for real time assessment of redox metallodrugs

Recent emergence of FTIR spectromicroscopy (micro-FTIR) as a dynamic spectroscopy for imaging to study biological chemistry has opened new possibilities for investigating in situ drug release, redox chemistry effects on biological molecules, DNA and drug interactions, membrane dynamics, and redox reactions with proteins at the single cell level. Micro-FTIR applied to metallodrugs has been playing an important role since the last decade because of its great potential to achieve more robust and controlled pharmacological effects against several diseases, including cancer. An important aspect in the development of these drugs is to understand their cellular properties, such as uptake, accumulation, activity, and toxicity. In this review, we present the potential application of micro-FTIR and its importance for studying metal-based drugs, highlighting the perspectives of chemistry of living cells. We also emphasise bioimaging, which is of high importance to localize the cellular processes, for a proper understanding of the mechanism of action.

Origin of thiocyanate spectral shifts in water and organic solvents

Vibrational spectroscopy is a useful technique for probing chemical environments. The development of models that can reproduce the spectra of nitriles and azides is valuable because these probes are uniquely suited for investigating complex systems. Empirical vibrational spectroscopic maps are commonly employed to obtain the instantaneous vibrational frequencies during molecular dynamics simulations but often fail to adequately describe the behavior of these probes, especially in its transferability to a diverse range of environments. In this paper, we demonstrate several reasons for the difficulty in constructing a general-purpose vibrational map for methyl thiocyanate (MeSCN), a model for cyanylated biological probes. In particular, we found that electrostatics alone are not a sufficient metric to categorize the environments of different solvents, and the dominant features in intermolecular interactions in the energy landscape vary from solvent to solvent. Consequently, common vibrational mapping schemes do not cover all essential interaction terms adequately, especially in the treatment of van der Waals interactions. Quantum vibrational perturbation (QVP) theory, along with a combined quantum mechanical and molecular mechanical potential for solute-solvent interactions, is an alternative and efficient modeling technique, which is compared in this paper, to yield spectroscopic results in good agreement with experimental FTIR. QVP has been used to analyze the computational data, revealing the shortcomings of the vibrational maps for MeSCN in different solvents. The results indicate that insights from QVP analysis can be used to enhance the transferability of vibrational maps in future studies.