Raman Techniques: Fundamentals and Frontiers

Nanotechnology-based optical sensors for Baijiu quality and safety control

Baijiu quality and safety have received considerable attention owing to the gradual increase in its consumption. However, owing to the unique and complex process of Baijiu production, issues leading to quality and safety concerns may occur during the manufacturing process. Therefore, establishing appropriate analytical methods is necessary for Baijiu quality assurance and process control. Nanomaterial (NM)-based optical sensing techniques have garnered widespread interest because of their unique advantages. However, comprehensive studies on nano-optical sensing technology for quality and safety control of Baijiu are lacking. In this review, we systematically summarize NM-based optical sensor applications for the accurate detection and quantification of analytes closely related to Baijiu quality and safety. Furthermore, we evaluate the sensing mechanisms for each application. Finally, we discuss the challenges nanotechnology poses for Baijiu analysis and future trends. Overall, nanotechnological approaches provide a potentially useful alternative for simplifying Baijiu analysis and improving final product quality and safety.

A microfluidic ratiometric electrochemical aptasensor for highly sensitive and selective detection of 3,3',4,4'-tetrachlorobiphenyl

This study proposes a strategy using a microfluidic ratiometric electrochemical aptasensor to detect PCB77 with excellent sensitivity and specificity. This sensing platform combines a microfluidic chip, a wireless integrated circuit system for aptamer-based electrochemical detection, and a mobile phone control terminal for parameter configuration, identification, observation, and wireless data transfer. The sensing method utilizes a cDNA (MB-COOH-cDNA-SH) that is labelled with the redox probe Methylene Blue (MB) at the 5' end and has a thiol group at the 3' end. Additionally, it utilizes a single strand PCB aptamer that has been modified with ferrocenes at the 3' end (aptamer-Fc). Through gold-thiol binding, the labelled probe of MB-COOH-cDNA-SH was self-assembled onto the surface of an Au/Nb2CTx/GO modified electrode. On exposure to aptamer-Fc, it will hybridize with MB-COOH-cDNA-SH to form a stable double-stranded structure on the electrode surface. When PCB77 is present, aptamer-Fc binds specifically to the target, enabling the double-stranded DNA to unwind. Such variation caused changes in the differential pulse voltammetry (DPV) peak currents of both MB and Fc. A substantial improvement is observed in the ratio between the two DPV peaks. Under the optimum experimental conditions, this assay has a response that covers the 0.0001 to 1000 ng mL-1 PCB77 concentration range, and the detection limit is 1.56 × 10-5 ng mL-1. The integration of a ratiometric electrochemical aptasensor with designed microfluidic and integrated devices in this work is an innovative and promising approach that offers an efficient platform for on-site applications.

In-line Raman imaging of mixing by herringbone grooves in microfluidic channels

The control over fluid flow achievable in microfluidic devices creates opportunities for applications in many fields. In simple microchannels, flow is purely laminar when one solvent is used, and hence, achieving reliable mixing is an important design consideration. Integration of structures, such as grooves, into the channels to act as static mixers is a commonly used approach. The mixing induced by these structures can be validated by determining concentration profiles in microfluidic channels following convergence of solvent streams from separate inlets. Spatially resolved characterisation is therefore necessary and requires in-line analysis methods. Here we report a line-focused illumination approach to provide operando, spatially resolved Raman spectra across the width of channels in the analysis of single- and multi-phase liquid systems and chemical reactions. A scientific complementary metal oxide semiconductor (sCMOS) sensor is used to overcome smearing encountered during spectral readout of images with CCD sensors. Isotopically labelled probes, in otherwise identical flow streams, show that z-confocality limits the spatial resolution and certainty as to the extent of mixing that can be achieved. These limitations are overcome using fast chemical reactions between reagents entering a microchannel in separate solvent streams. We show here that the progression of a chemical reaction, for which only the product is observable, is a powerful approach to determine the extent of mixing in a microchannel. Specifically resonance enhancement of Raman scattering from a product formed allows for determination of the true efficiency of mixing over the length and width of microchannels. Raman spectral images obtained by line-focused illumination show onset of mixing by observing the product of reagents entering from the separate inlets. Mixing is initially off-centre and immediately before the apex of the first groove of the static mixer, and then evolves along the entire width of the channel after a full cycle of grooves.

Label-Free Detection of Virus-like Particles with Surface-Enhanced Raman Spectroscopy through Analyte Localization and Polymer-Enabled Capture

Virus detection is highly important; the last several years, since the onset of the SARS-CoV-2 pandemic, have highlighted a weakness in the field: the need for highly specialized and complex methodology for sensitive virus detection, which also manifests as sacrifices in limits of detection made to achieve simple and rapid sensing. Surface-enhanced Raman spectroscopy (SERS) has the potential to fill this gap, and two novel approaches to the development of a detection scheme are presented in this study. First, the physical entrapment of vesicular stomatitis virus (VSV) and additional virus-like particles through substrate design to localize virus analytes into SERS hotspots is explored. Then, the use of nonspecific linear polymers as affinity agents to facilitate polymer-enabled capture of the VSV for SERS detection is studied. Quantitative detection of the VSV is achieved down to 101 genetic copies per milliliter with an R2 of 0.987 using the optimized physical entrapment method. Physical entrapment of two more virus-like particles is demonstrated with electron microscopy, and distinctive SERS fingerprints are shown. This study shows great promise for the further exploration of label-free virus detection methods involving thoughtful substrate design and unconventional affinity agents.

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis

Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well as challenges in accurate contagion diagnostics. Addressing AMR requires a strategic approach that emphasizes early stage prevention through screening in clinical, environmental, farming, and livestock settings to identify nonvulnerable antimicrobial agents and the associated genes. Conventional AMR diagnostics, like antibiotic susceptibility testing, possess drawbacks, including high costs, time-consuming processes, and significant manpower requirements, underscoring the need for intelligent, prompt, and on-site diagnostic techniques. Nanoenabled artificial intelligence (AI)-supported smart optical biosensors present a potential solution by facilitating rapid point-of-care AMR detection with real-time, sensitive, and portable capabilities. This Review comprehensively explores various types of optical nanobiosensors, such as surface plasmon resonance sensors, whispering-gallery mode sensors, optical coherence tomography, interference reflection imaging sensors, surface-enhanced Raman spectroscopy, fluorescence spectroscopy, microring resonance sensors, and optical tweezer biosensors, for AMR diagnostics. By harnessing the unique advantages of these nanoenabled smart biosensors, a revolutionary paradigm shift in AMR diagnostics can be achieved, characterized by rapid results, high sensitivity, portability, and integration with Internet-of-Things (IoT) technologies. Moreover, nanoenabled optical biosensors enable personalized monitoring and on-site detection, significantly reducing turnaround time and eliminating the human resources needed for sample preservation and transportation. Their potential for holistic environmental surveillance further enhances monitoring capabilities in diverse settings, leading to improved modern-age healthcare practices and more effective management of antimicrobial treatments. Embracing these advanced diagnostic tools promises to bolster global healthcare capacity to combat AMR and safeguard One Health.

Evaluation of Ilex guayusa and Piper marginatum Extract Cytotoxicity on Human Dental Pulp Mesenchymal Stem Cells

BACKGROUND

Amelogenesis imperfecta is a hereditary disorder affecting dental enamel. Among its phenotypes, hypocalcified AI is characterized by mineral deficiency, leading to tissue wear and, consequently, dental sensitivity. Excessive fluoride intake (through drinking water, fluoride supplements, toothpaste, or by ingesting products such as pesticides or insecticides) can lead to a condition known as dental fluorosis, which manifests as stains and teeth discoloration affecting their structure. Our recent studies have shown that extracts from Colombian native plants, Ilex guayusa and Piper marginatum, deposit mineral ions such as phosphate and orthophosphate into the dental enamel structure; however, it is unknown whether these extracts produce toxic effects on the dental pulp.

OBJECTIVE

To assess cytotoxicity effects on human dental pulp stem cells (hDPSCs) exposed to extracts isolated from I. guayusa and P. marginatum and, hence, their safety for clinical use.

METHODS

Raman spectroscopy, fluorescence microscopy, and flow cytometry techniques were employed. For Raman spectroscopy, hDPSCs were seeded onto nanobiochips designed to provide surface-enhanced Raman spectroscopy (SERS effect), which enhances their Raman signal by several orders of magnitude. After eight days in culture, I. guayusa and P. marginatum extracts at different concentrations (10, 50, and 100 ppm) were added. Raman measurements were performed at 0, 12, and 24 h following extract application. Fluorescence microscopy was conducted using an OLIMPUS fv1000 microscope, a live-dead assay was performed using a kit employing a BD FACS Canto TM II flow cytometer, and data analysis was determined using a FlowJo program.

RESULTS

The Raman spectroscopy results showed spectra consistent with viable cells. These findings were corroborated using fluorescence microscopy and flow cytometry techniques, confirming high cellular viability.

CONCLUSIONS

The analyzed extracts exhibited low cytotoxicity, suggesting that they could be safely applied on enamel for remineralization purposes. The use of nanobiochips for SERS effect improved the cell viability assessment.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

The mechanism of water oxidation using transition metal-based heterogeneous electrocatalysts

The water oxidation reaction, a crucial process for solar energy conversion, has garnered significant research attention. Achieving efficient energy conversion requires the development of cost-effective and durable water oxidation catalysts. To design effective catalysts, it is essential to have a fundamental understanding of the reaction mechanisms. This review presents a comprehensive overview of recent advancements in the understanding of the mechanisms of water oxidation using transition metal-based heterogeneous electrocatalysts, including Mn, Fe, Co, Ni, and Cu-based catalysts. It highlights the catalytic mechanisms of different transition metals and emphasizes the importance of monitoring of key intermediates to explore the reaction pathway. In addition, advanced techniques for physical characterization of water oxidation intermediates are also introduced, for the purpose of providing information for establishing reliable methodologies in water oxidation research. The study of transition metal-based water oxidation electrocatalysts is instrumental in providing novel insights into understanding both natural and artificial energy conversion processes.

An Ultra-Conductive and Patternable 40 nm-Thick Polymer Film for Reliable Emotion Recognition

Understanding psychology is an important task in modern society which helps predict human behavior and provide feedback accordingly. Monitoring of weak psychological and emotional changes requires bioelectronic devices to be stretchable and compliant for unobtrusive and high-fidelity signal acquisition. Thin conductive polymer film is regarded as an ideal interface; however, it is very challenging to simultaneously balance mechanical robustness and opto-electrical property. Here, a 40 nm-thick film based on photolithographic double-network conductive polymer mediated by graphene layer is reported, which concurrently enables stretchability, conductivity, and conformability. Photolithographic polymer and graphene endow the film photopatternability, enhance stress dissipation capability, as well as improve opto-electrical conductivity (4458 S cm-1@>90% transparency) through molecular rearrangement by π-π interaction, electrostatic interaction, and hydrogen bonding. The film is further applied onto corrugated facial skin, the subtle electromyogram is monitored, and machine learning algorithm is performed to understand complex emotions, indicating the outstanding ability for stretchable and compliant bioelectronics.

Advancements in Neurosurgical Intraoperative Histology

Despite their relatively low incidence globally, central nervous system (CNS) tumors remain amongst the most lethal cancers, with only a few other malignancies surpassing them in 5-year mortality rates. Treatment decisions for brain tumors heavily rely on histopathological analysis, particularly intraoperatively, to guide surgical interventions and optimize patient outcomes. Frozen sectioning has emerged as a vital intraoperative technique, allowing for highly accurate, rapid analysis of tissue samples, although it poses challenges regarding interpretive errors and tissue distortion. Raman histology, based on Raman spectroscopy, has shown great promise in providing label-free, molecular information for accurate intraoperative diagnosis, aiding in tumor resection and the identification of neurodegenerative disease. Techniques including Stimulated Raman Scattering (SRS), Coherent Anti-Stokes Raman Scattering (CARS), Surface-Enhanced Raman Scattering (SERS), and Tip-Enhanced Raman Scattering (TERS) have profoundly enhanced the speed and resolution of Raman imaging. Similarly, Confocal Laser Endomicroscopy (CLE) allows for real-time imaging and the rapid intraoperative histologic evaluation of specimens. While CLE is primarily utilized in gastrointestinal procedures, its application in neurosurgery is promising, particularly in the context of gliomas and meningiomas. This review focuses on discussing the immense progress in intraoperative histology within neurosurgery and provides insight into the impact of these advancements on enhancing patient outcomes.

Photon counting Raman spectroscopy: a benchmarking study vs surface plasmon enhancement

We demonstrate a single-photon counting Raman spectroscope and benchmark it against conventional and surface-enhanced Raman spectroscopy. For direct comparison without ambiguity, we use the same solutions of Rhodamine 6G and a common optical setup with either a spectrometer or an acousto-optic tunable filter, whereas the surface enhancement is realized with immobilized Ag nanoparticles. Our results find that the single photon counting significantly elevates the detection sensitivity by up to eight orders of magnitude, arriving at a comparable level of surface-enhanced Raman spectroscopy. Another significant advantage is with the time-resolving measurement, where we demonstrate time-gated and time-correlated single-photon counting with sub-nanosecond resolution. It offers insights into the samples' transient responses and enables the isolation of Raman scattering from fluorescence signals.

Optical Methods for Brain Tumor Detection: A Systematic Review

Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.

Simultaneous determination of multiple quality indices of dried shrimp (Parapenaeopsis hardwickii) during storage using Raman spectroscopy

BACKGROUND

Dried shrimp is a high-value fishery product worldwide, but rapid and accurate assessment of its quality remains challenging. In the present study, a new method based on Raman spectroscopy was developed for assessing the quality changes in dried shrimp (Parapenaeopsis hardwickii) during storage.

RESULTS

A high-quality Raman spectrum of astaxanthin (AST) was obtained from the third abdominal segment of dried shrimp. The intensity ratio (I1520/I1446) of the band from 1520 cm-1 to that at 1446 cm-1, which was ascribed to AST and protein/lipid, respectively, was calculated. I1520/I1446 can probe AST degradation in dried shrimp during storage at both 37 and 4 °C and further reflect quality changes of dried shrimp, as indicated by indices including total volatile basic nitrogen, pH and thiobarbituric acid reactive substances.

CONCLUSION

Compared to conventional methods, the proposed method avoids complex and time-consuming preprocessing and provides significant advantages including cost-effectiveness and rapid detection. © 2024 Society of Chemical Industry.

Advances in performance degradation mechanism and safety assessment of LiFePO4for energy storage

In the context of 'energy shortage', developing a novel energy-based power system is essential for advancing the current power system towards low-carbon solutions. As the usage duration of lithium-ion batteries for energy storage increases, the nonlinear changes in their aging process pose challenges to accurately assess their performance. This paper focuses on the study LiFeO4(LFP), used for energy storage, and explores their performance degradation mechanisms. Furthermore, it introduces common battery models and data structures and algorithms, which used for predicting the correlation between electrode materials and physical parameters, applying to state of health assessment and thermal warning. This paper also discusses the establishment of digital management system. Compared to conventional battery networks, dynamically reconfigurable battery networks can realize real-time monitoring of lithium-ion batteries, and reduce the probability of fault occurrence to an acceptably low level.

Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy

In light of the growing awareness regarding the ubiquitous presence of microplastics (MPs) in our environment, recent efforts have been made to integrate Artificial Intelligence (AI) technology into MP detection. Among spectroscopic techniques, Raman spectroscopy is preferred for the detection of MP particles measuring less than 10 μm, as it overcomes the diffraction limitations encountered in Fourier transform infrared (FTIR). However, Raman spectroscopy's inherent limitation is its low scattering cross section, which often results in prolonged data collection times during practical sample measurements. In this study, we implemented a convolutional neural network (CNN) model alongside a tailored data interpolation strategy to expedite data collection for MP particles within the 1-10 μm range. Remarkably, we achieved the classification of plastic types for individual particles with a mere 0.4 s of exposure time, reaching an approximate confidence level of 85.47(±5.00)%. We postulate that the result significantly accelerates the aggregation of microplastic distribution data in diverse scenarios, contributing to the development of a comprehensive global microplastic map.

Toward the nanoscale chemical and physical probing of milk-derived extracellular vesicles using Raman and tip-enhanced Raman spectroscopy

Tip-enhanced Raman spectroscopy (TERS) is an advanced technique to perform local chemical analysis of the surface of a sample through the improvement of the sensitivity and the spatial resolution of Raman spectroscopy by plasmonic enhancement of the electromagnetic signal in correspondence with the nanometer-sized tip of an atomic force microscope (AFM). In this work, TERS is demonstrated to represent an innovative and powerful approach for studying extracellular vesicles, in particular bovine milk-derived extracellular vesicles (mEVs), which are nanostructures with considerable potential in drug delivery and therapeutic applications. Raman spectroscopy has been used to analyze mEVs at the micrometric and sub-micrometric scales to obtain a detailed Raman spectrum in order to identify the 'signature' of mEVs in terms of their characteristic molecular vibrations and, therefore, their chemical compositions. With the ability to improve lateral resolution, TERS has been used to study individual mEVs, demonstrating the possibility of investigating a single mEV selected on the surface of the sample and, moreover, analyzing specific locations on the selected mEV with nanometer lateral resolution. TERS potentially allows one to reveal local differences in the composition of mEVs providing new insights into their structure. Also, thanks to the intrinsic properties of TERS to acquire the signal from only the first few nanometers of the surface, chemical investigation of the lipid membrane in correspondence with the various locations of the selected mEV could be performed by analyzing the peaks of the Raman shift in the relevant range of the spectrum (2800-3000 cm-1). Despite being limited to mEVs, this work demonstrates the potential of TERS in the analysis of extracellular vesicles.

In Situ Imaging of Subcutaneous Drug Delivery Systems Using Microspatially Offset Low-Frequency Raman Spectroscopy

The noninvasive in situ monitoring of the status of drug retention and implant integrity of subcutaneous implants would allow optimization of therapy and avoid periods of subtherapeutic delivery kinetics. A proof-of principle study was conducted to determine the use of microspatially offset low-frequency Raman spectroscopy (micro-SOLFRS) for nonintrusive in situ analysis of subcutaneous drug delivery systems. Caffeine was used as the model drug, and it was embedded in a circular-shape Soluplus matrix via vacuum compression molding. For the exploratory analysis, prototype implants were positioned underneath skin tissue samples, and various caffeine concentrations (1-50% w/w) and micro-SOLFRS displacement settings (Δz = 0-8 mm) were tested from the pseudo three-dimensional (3D)-imaging perspective. This format allowed the optimization of real-time micro-SOLFRS analysis of implants through skin tissue that was embedded in an agarose hydrogel. Notably, this analytical approach allowed the temporal and spatial erosion of the implant and solid-state transformations of caffeine to be distinguished. The spectrometric results correlated with complementary high-performance liquid chromatography (HPLC) determination of changes in drug concentration, illustrating drug dissipation/diffusion characteristics. The discovered capability of micro-SOLFRS for in situ measurements of drugs and implants makes it attractive for biomedical diagnostics that, ultimately, could result in development of a new point-of-care technology.

Label-Free Techniques for Probing Biomolecular Condensates

Biomolecular condensates play important roles in a wide array of fundamental biological processes, such as cellular compartmentalization, cellular regulation, and other biochemical reactions. Since their discovery and first observations, an extensive and expansive library of tools has been developed to investigate various aspects and properties, encompassing structural and compositional information, material properties, and their evolution throughout the life cycle from formation to eventual dissolution. This Review presents an overview of the expanded set of tools and methods that researchers use to probe the properties of biomolecular condensates across diverse scales of length, concentration, stiffness, and time. In particular, we review recent years' exciting development of label-free techniques and methodologies. We broadly organize the set of tools into 3 categories: (1) imaging-based techniques, such as transmitted-light microscopy (TLM) and Brillouin microscopy (BM), (2) force spectroscopy techniques, such as atomic force microscopy (AFM) and the optical tweezer (OT), and (3) microfluidic platforms and emerging technologies. We point out the tools' key opportunities, challenges, and future perspectives and analyze their correlative potential as well as compatibility with other techniques. Additionally, we review emerging techniques, namely, differential dynamic microscopy (DDM) and interferometric scattering microscopy (iSCAT), that have huge potential for future applications in studying biomolecular condensates. Finally, we highlight how some of these techniques can be translated for diagnostics and therapy purposes. We hope this Review serves as a useful guide for new researchers in this field and aids in advancing the development of new biophysical tools to study biomolecular condensates.

A review of clinical use of surface-enhanced Raman scattering-based biosensing for glioma

Glioma is the most common malignant tumor of the nervous system in recent centuries, and the incidence rate of glioma is increasing year by year. Its invasive growth and malignant biological behaviors make it one of the most challenging malignant tumors. Maximizing the resection range (EOR) while minimizing the impact on normal brain tissue is crucial for patient prognosis. Changes in metabolites produced by tumor cells and their microenvironments might be important indicators. As a powerful spectroscopic technique, surface-enhanced Raman scattering (SERS) has many advantages, including ultra-high sensitivity, high specificity, and non-invasive features, which allow SERS technology to be widely applied in biomedicine, especially in the differential diagnosis of malignant tumor tissues. This review first introduced the clinical use of responsive SERS probes. Next, the sensing mechanisms of microenvironment-responsive SERS probes were summarized. Finally, the biomedical applications of these responsive SERS probes were listed in four sections, detecting tumor boundaries due to the changes of pH-responsive SERS probes, SERS probes to guide tumor resection, SERS for liquid biopsy to achieve early diagnosis of tumors, and the application of free-label SERS technology to detect fresh glioma specimens. Finally, the challenges and prospects of responsive SERS detections were summarized for clinical use.

The application of laser‑induced fluorescence in oil spill detection

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

Raman Flow Cytometry and Its Biomedical Applications

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Measurement of Carbon Dioxide Isotopes with Air-Lasing-Based Coherent Raman Spectroscopy

Isotope detection is crucial for geological research, medical diagnostics, industrial production, and environmental monitoring. Various spectroscopic techniques are continually emerging for isotopic identification and accurate measurement. Herein, coherent Raman scattering (CRS) spectroscopy is developed for the quantitative detection of carbon dioxide isotopes, in which the N2+ air lasing coherently created in the interaction region is used as the probe. Benefiting from the narrow spectral width of air lasing, the Raman peaks of 12CO2 and 13CO2 can be well discerned, although their spectra partially overlap. The overlapped signals were proven to be the result of the coherent superposition of individual Raman signals. Based on that fact, a deconvolution algorithm was designed to retrieve the concentration ratio of the two isotopes. The relative error of the measurement is less than 6%. The CRS technique based on air lasing offers a potential approach for the quantitative characterization of molecular isotopes, especially in application scenarios of remote sensing or in situ detection.

Submicron- and nanoplastic detection at low micro- to nanogram concentrations using gold nanostar-based surface-enhanced Raman scattering (SERS) substrates

The presence of submicron- (1 μm-100 nm) and nanoplastic (<100 nm) particles within various sample matrices, ranging from marine environments to foods and beverages, has become a topic of increasing interest in recent years. Despite this interest, very few analytical techniques are known that allow for the detection of these small plastic particles in the low concentration ranges that they are anticipated to be present at. Research focused on optimizing surface-enhanced Raman scattering (SERS) to enhance signal obtained in Raman spectroscopy has been shown to have great potential for the detection of plastic particles below conventional resolution limits. In this study, we produce SERS substrates composed of gold nanostars and assess their potential for submicron- and nanoplastic detection. The results show 33 nm polystyrene could be detected down to 1.25 μg mL-1 while 36 nm poly(ethylene terephthalate) was detected down to 5 μg mL-1. These results confirm the promising potential of the gold nanostar-based SERS substrates for nanoplastic detection. Furthermore, combined with findings for 121 nm polypropylene and 126 nm polyethylene particles, they highlight potential differences in analytical performance that depend on the properties of the plastics being studied.

A Structured Illumination Microscopy Framework with Spatial-Domain Noise Propagation

Biological images captured by a microscope are characterized by heterogeneous signal-to-noise ratios (SNRs) across the field of view due to spatially varying photon emission and camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in the Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised methods rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains a challenging task. Here, we propose Bayesian-SIM (B-SIM), an unsupervised Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating all noise sources in the spatial domain. To accelerate the reconstruction process for computational feasibility, we devise a parallelized Monte Carlo sampling strategy for inference. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low-SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

Unified and vector theory of Raman scattering in gas-filled hollow-core fiber across temporal regimes

Raman scattering has found renewed interest owing to the development of gas-filled hollow-core fibers, which constitute a unique platform for exploration of novel ultrafast nonlinear phenomena beyond conventional solid-core-fiber and free-space systems. Much progress has been made through models for particular interaction regimes, which are delineated by the relation of the excitation pulse duration to the time scales of the Raman response. However, current experimental settings are not limited to one regime, prompting the need for tools spanning multiple regimes. Here, we present a theoretical framework that accomplishes this goal. The theory allows us to review recent progress with a fresh perspective, makes new connections between distinct temporal regimes of Raman scattering, and reveals new degrees of freedom for controlling Raman physics. Specific topics that are addressed include transient Raman gain, the interplay of electronic and Raman nonlinearities in short-pulse propagation, and interactions of short pulses mediated by phonon waves. The theoretical model also accommodates vector effects, which have been largely neglected in prior works on Raman scattering in gases. The polarization dependence of transient Raman gain and vector effects on pulse interactions via phonon waves is investigated with the model. Throughout this Perspective, theoretical results are compared to the results of realistic numerical simulations. The numerical code that implements the new theory is freely available. We hope that the unified theoretical framework and numerical tool described here will accelerate the exploration of new Raman-scattering phenomena and enable new applications.

Hyper spectral resolution stimulated Raman spectroscopy with amplified fs pulse bursts

We present a novel approach for Stimulated Raman Scattering (SRS) spectroscopy in which a hyper spectral resolution and high-speed spectral acquisition are achieved by employing amplified offset-phase controlled fs-pulse bursts. We investigate the method by solving the coupled non-linear Schrödinger equations and validate it by numerically characterizing SRS in molecular nitrogen as a model compound. The spectral resolution of the method is found to be determined by the inverse product of the number of pulses in the burst and the intraburst pulse separation. The SRS spectrum is obtained through a motion-free scanning of the offset phase that results in a sweep of the Raman-shift frequency. Due to high spectral resolution and fast motion-free scanning the technique is beneficial for a number SRS-based applications such as gas sensing and chemical analysis.

Applications of Raman spectroscopy in the diagnosis and monitoring of neurodegenerative diseases

Raman scattering is an inelastic light scattering that occurs in a manner reflective of the molecular vibrations of molecular structures and chemical conditions in a given sample of interest. Energy changes in the scattered light can be assessed to determine the vibration mode and associated molecular and chemical conditions within the sample, providing a molecular fingerprint suitable for sample identification and characterization. Raman spectroscopy represents a particularly promising approach to the molecular analysis of many diseases owing to clinical advantages including its instantaneous nature and associated high degree of stability, as well as its ability to yield signal outputs corresponding to a single molecule type without any interference from other molecules as a result of its narrow peak width. This technology is thus ideally suited to the simultaneous assessment of multiple analytes. Neurodegenerative diseases represent an increasingly significant threat to global public health owing to progressive population aging, imposing a severe physical and social burden on affected patients who tend to develop cognitive and/or motor deficits beginning between the ages of 50 and 70. Owing to a relatively limited understanding of the etiological basis for these diseases, treatments are lacking for the most common neurodegenerative diseases, which include Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. The present review was formulated with the goal of briefly explaining the principle of Raman spectroscopy and discussing its potential applications in the diagnosis and evaluation of neurodegenerative diseases, with a particular emphasis on the research prospects of this novel technological platform.

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.

SRS conversion efficiency assessment of a single cell Raman gas mixture for DIAL ozone lidar

A single Raman cell configuration useful for DIAL ozone lidar is designed and optimized. The conversion efficiency and flexibility of using a single Raman cell filled with a mixture of high pressure Raman active gases hydrogen (H 2) and methane (C H 4) have been examined and reported. The stimulated Raman scattering (SRS) conversion efficiency of Raman active gases with different total cell pressures and the volume mixing ratio excited with a focused, frequency quadrupled Nd:YAG laser with a maximum pulse energy of 25 mJ and a pulse duration of 10 ns at 100 Hz repetition rate are examined in detail. The gas combination of H 2:C H 4 emits a coaxial beam of two wavelengths, 288.4 nm (C H 4) and 299.1 nm (H 2), with a maximum total conversion efficiency of about 45%. The optimum volume mixing ratio for generating the required wavelength pair with almost equal energies is found to be 2:1 (H 2:C H 4) at a total cell pressure of 18 bar. The contribution of cascade Raman scattering (CRS) and four-wave mixing (FWM) to the higher order Stokes lines is examined. The laser attenuation due to soot formation under various mixing ratios in the cell is also presented.

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Surface-enhanced Raman spectroscopy (SERS) has been well explored as a highly effective characterization technique that is capable of chemical pollutant detection and identification at very low concentrations. Machine learning has been previously used to identify compounds based on SERS spectral data. However, utilization of SERS to quantify concentrations, with or without machine learning, has been difficult due to the spectral intensity being sensitive to confounding factors such as the substrate parameters, orientation of the analyte, and sample preparation technique. Here, we demonstrate an approach for predicting the concentration of sample pollutants from SERS spectra using machine learning. Frequency domain transform methods, including the Fourier and Walsh-Hadamard transforms, are applied to spectral data sets of three analytes (rhodamine 6G, chlorpyrifos, and triclosan), which are then used to train machine learning algorithms. Using standard machine learning models, the concentration of the sample pollutants is predicted with >80% cross-validation accuracy from raw SERS data. A cross-validation accuracy of 85% was achieved using deep learning for a moderately sized data set (∼100 spectra), and 70-80% was achieved for small data sets (∼50 spectra). Performance can be maintained within this range even when combining various sample preparation techniques and environmental media interference. Additionally, as a spectral pretreatment, the Fourier and Hadamard transforms are shown to consistently improve prediction accuracy across multiple data sets. Finally, standard models were shown to accurately identify characteristic peaks of compounds via analysis of their importance scores, further verifying their predictive value.

Episymbiotic Saccharibacteria induce intracellular lipid droplet production in their host bacteria

Saccharibacteria (formerly TM7) are a group of widespread and genetically diverse ultrasmall bacteria with highly reduced genomes that belong to Candidate Phyla Radiation, a large monophyletic lineage with poorly understood biology. Nanosynbacter lyticus type strain TM7x is the first Saccharibacteria member isolated from the human oral microbiome. With restrained metabolic capacities, TM7x lives on the surface of, and forms an obligate episymbiotic relationship with its bacterial host, Schaalia odontolytica strain XH001. The symbiosis allows TM7x to propagate but presents a burden to host bacteria by inducing stress response. Here, we employed super-resolution fluorescence imaging to investigate the physical association between TM7x and XH001. We showed that the binding with TM7x led to a substantial alteration in the membrane fluidity of XH001. We also revealed the formation of intracellular lipid droplets in XH001 when forming episymbiosis with TM7x, a feature that has not been reported in oral bacteria. The TM7x-induced lipid droplets accumulation in XH001 was confirmed by label-free Raman spectroscopy, which also unveiled additional phenotypical features when XH001 cells are physically associated with TM7x. Further exploration through culturing XH001 under various stress conditions showed that lipid droplets accumulation was a general response to stress. A survival assay demonstrated that the presence of lipid droplets plays a protective role in XH001, enhancing its survival under adverse conditions. In conclusion, our study sheds new light on the intricate interaction between Saccharibacteria and their host bacteria, highlighting the potential benefit conferred by TM7x to its host and further emphasizing the context-dependent nature of symbiotic relationships.

Intraoperative Assessment of Resection Margin in Oral Cancer: The Potential Role of Spectroscopy

In parallel with the increasing number of oncological cases, the need for faster and more efficient diagnostic tools has also appeared. Different diagnostic approaches are available, such as radiological imaging or histological staining methods, but these do not provide adequate information regarding the resection margin, intraoperatively, or are time consuming. The purpose of this review is to summarize the current knowledge on spectrometric diagnostic modalities suitable for intraoperative use, with an emphasis on their relevance in the management of oral cancer. The literature agrees on the sensitivity, specificity, and accuracy of spectrometric diagnostic modalities, but further long-term prospective, multicentric clinical studies are needed, which may standardize the intraoperative assessment of the resection margin and the use of real-time spectroscopic approaches.

Visual detection of microplastics using Raman spectroscopic imaging

As a new type of pollutant in the marine environment and terrestrial ecosystems, microplastics have attracted widespread attention. Assessing the ecological risk of microplastics relies on accurately detecting small-sized particles in the environment. Microplastics exhibit unique "fingerprint" characteristics in Raman spectroscopy, making them suitable for rapid identification. In this study, we achieved visualization of microplastics through pseudo-color images generated by Raman spectroscopy imaging. Pseudo-color imaging maps were generated by selecting characteristic peaks and the classical least-squares fitting method was used to visually represent the distribution of different microplastics. The study explored the potential of Raman spectroscopy and its mapping mode in distinguishing various types of mixed microplastics and demonstrated that this approach can identify microplastics in complex environmental samples. Specifically, a cloud-point extraction followed by membrane filtration method was successfully applied to identifying mixed-component microplastics. In summary, the category, quantity, location, and differentiation of microplastics can be accurately analyzed by Raman spectroscopy, which provides a basis for assessing their ecological risk.

Research progress of the combination of COFs materials with food safety detection

Covalent organic frameworks (COFs) have received lots of attention due to their multiple advantages such as high specific surface area, controlled pore size, and excellent stability. When detecting food contaminants, the matrix effect brought by complex food samples can significantly affect the accuracy of the results. How to attenuate matrix effect has always been a major challenge. Utilizing the advantages of COFs and applying them to detect food contaminants is currently a key research direction. The aim of this work is to provide a systematic summary of sample pretreatment techniques and detection techniques combined with COFs, which include almost all current techniques combined with COFs. In addition, the principles of combining COFs with different techniques are explained. Finally, the research foci and development direction of COFs in food contaminant detection are discussed. This is an important reference for the future development of food safety and the design of COFs.

A Prototype Method for the Detection and Recognition of Pigments in the Environment Based on Optical Property Simulation

The possibility of pigment detection and recognition in different environments such as solvents or proteins is a challenging, and at the same time demanding, task. It may be needed in very different situations: from the nondestructive in situ identification of pigments in paintings to the early detection of fungal infection in major agro-industrial crops and products. So, we propose a prototype method, the key feature of which is a procedure analyzing the lineshape of a spectrum. The shape of the absorption spectrum corresponding to this transition strongly depends on the immediate environment of a pigment and can serve as a marker to detect the presence of a particular pigment molecule in a sample. Considering carotenoids as an object of study, we demonstrate that the combined operation of the differential evolution algorithm and semiclassical quantum modeling of the optical response based on a generalized spectral density (the number of vibronic modes is arbitrary) allows us to distinguish quantum models of the pigment for different solvents. Moreover, it is determined that to predict the optical properties of monomeric pigments in protein, it is necessary to create a database containing, for each pigment, in addition to the absorption spectra measured in a predefined set of solvents, the parameters of the quantum model found using differential evolution.

Advances in Diagnostic Tools and Therapeutic Approaches for Gliomas: A Comprehensive Review

Gliomas, a prevalent category of primary malignant brain tumors, pose formidable clinical challenges due to their invasive nature and limited treatment options. The current therapeutic landscape for gliomas is constrained by a "one-size-fits-all" paradigm, significantly restricting treatment efficacy. Despite the implementation of multimodal therapeutic strategies, survival rates remain disheartening. The conventional treatment approach, involving surgical resection, radiation, and chemotherapy, grapples with substantial limitations, particularly in addressing the invasive nature of gliomas. Conventional diagnostic tools, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), play pivotal roles in outlining tumor characteristics. However, they face limitations, such as poor biological specificity and challenges in distinguishing active tumor regions. The ongoing development of diagnostic tools and therapeutic approaches represents a multifaceted and promising frontier in the battle against this challenging brain tumor. The aim of this comprehensive review is to address recent advances in diagnostic tools and therapeutic approaches for gliomas. These innovations aim to minimize invasiveness while enabling the precise, multimodal targeting of localized gliomas. Researchers are actively developing new diagnostic tools, such as colorimetric techniques, electrochemical biosensors, optical coherence tomography, reflectometric interference spectroscopy, surface-enhanced Raman spectroscopy, and optical biosensors. These tools aim to regulate tumor progression and develop precise treatment methods for gliomas. Recent technological advancements, coupled with bioelectronic sensors, open avenues for new therapeutic modalities, minimizing invasiveness and enabling multimodal targeting with unprecedented precision. The next generation of multimodal therapeutic strategies holds potential for precision medicine, aiding the early detection and effective management of solid brain tumors. These innovations offer promise in adopting precision medicine methodologies, enabling early disease detection, and improving solid brain tumor management. This review comprehensively recognizes the critical role of pioneering therapeutic interventions, holding significant potential to revolutionize brain tumor therapeutics.

In-situ analysis of trace components in proportioning distilled spirits using Raman integrating sphere spectroscopy

In situ and on-site analysis of trace components, such as methanol and ethyl acetate, in distilled spirits poses significant challenges. In this study, we have proposed a simple, yet effective and rapid approach that combines Raman spectroscopy with Raman integrating sphere technology to accurately detect trace constituents in distilled spirits. An external standard method to effectively separate overlapping Raman peaks from different substances are developed. Experimental results demonstrate that with an exposure time of 180 s under normal temperature and pressure, the detection limits for methanol, acetic acid, and ethyl acetate in proportioned distilled spirits are below 0.1 g/L. Importantly, the detection limit of methanol and acetic acid remains unaffected by the concentration of distilled spirits and the types of trace substances. Notably, the concentration of trace solute exhibits a highly linear relationship with its corresponding Raman intensity, offering a reliable probe for identifying unknown components in distilled spirits.

Light-induced synthesis of silver nanoprisms as a surface-enhanced Raman scattering substrate for N-acetyl procainamide drug quantification

Triangle-shaped silver nanoprisms (AgNPMs) were prepared by a photo-induced method through a seed-mediated growth process and were successfully employed as an ultra-sensitive surface-enhanced Raman scattering (SERS) substrate for the detection of the chemotherapeutic N-acetyl procainamide (NAPA) compound. The transformation of the morphology of the nanoprisms substrate could be noted with a remarkable change in color, possessing an average size of 95 nm. The shape-modified AgNPMs exhibited interesting optical characteristics owing to the truncated dual edges, which led to a pronounced longitudinal localized surface plasmonic resonance (LLSPR) behavior. The nanoprisms-based SERS substrate demonstrated an outstanding sensitivity for NAPA in aqueous solutions with the lowest ever reported detection limit of 0.5 × 10-13 M corresponding to excellent recovery and stability. A steady linear response with a broad dynamic range (10-4-10-12 M) and an R2 of 0.945 was also achieved. The results proved that the NPMs demonstrated excellent efficiency, reproducibility (97%), and stability (30 days) with a superior Raman signal enhancement reaching an ultralow detection limit of 0.5 × 10-13 M compared to the nanosphere particles which could show an LOD of 0.5 × 10-9 M.

Physicochemical understanding of biomineralization by molecular vibrational spectroscopy: From mechanism to nature

The process and mechanism of biomineralization and relevant physicochemical properties of mineral crystals are remarkably sophisticated multidisciplinary fields that include biology, chemistry, physics, and materials science. The components of the organic matter, structural construction of minerals, and related mechanical interaction, etc., could help to reveal the unique nature of the special mineralization process. Herein, the paper provides an overview of the biomineralization process from the perspective of molecular vibrational spectroscopy, including the physicochemical properties of biomineralized tissues, from physiological to applied mineralization. These physicochemical characteristics closely to the hierarchical mineralization process include biological crystal defects, chemical bonding, atomic doping, structural changes, and content changes in organic matter, along with the interface between biocrystals and organic matter as well as the specific mechanical effects for hardness and toughness. Based on those observations, the special physiological properties of mineralization for enamel and bone, as well as the possible mechanism of pathological mineralization and calcification such as atherosclerosis, tumor micro mineralization, and urolithiasis are also reviewed and discussed. Indeed, the clearly defined physicochemical properties of mineral crystals could pave the way for studies on the mechanisms and applications.

Simple and Inexpensive Fabrication of High Surface-Area Paper-Based Gold Electrodes for Electrochemical and Surface-Enhanced Raman Scattering Sensing

Paper has emerged as an excellent alternative to create environmentally benign disposable electrochemical sensing devices. The critical step to fabricating electrochemical sensors is making paper conductive. In this work, paper-based electrodes with a high electroactive surface area (ESA) were fabricated using a simple electroless deposition technique. The polymerization time of a polydopamine adhesion layer and the gold salt concentration during the electroless deposition step were optimized to obtain uniformly conductive paper-based electrodes. The optimization of these fabrication parameters was key to obtaining the highest ESA possible. Roughening factors (Rf) of 7.2 and 2.3 were obtained when cyclic voltammetry was done in sulfuric acid and potassium ferricyanide, respectively, demonstrating a surface prone to fast electron transfer. As a proof of concept, mercury detection was done through anodic stripping, achieving a limit of quantification (LOQ) of 0.9 ppb. By changing the metal deposition conditions, the roughness of the metalized papers could also be tuned for their use as surface-enhanced Raman scattering (SERS) sensors. Metallized papers with the highest SERS signal for thiophenol detection yielded a LOQ of 10 ppb. We anticipate that this method of fabricating nanostructured paper-based electrodes can accelerate the development of simple, cost-effective, and highly sensitive electrochemical and SERS sensing platforms.

Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice

Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

Combination of Resonance and Non-Resonance Chiral Raman Scattering in a Cobalt(III) Complex

Resonance Raman optical activity (RROA) spectra with high sensitivity reveal details on molecular structure, chirality, and excited electronic properties. Despite the difficulty of the measurements, the recorded data for the Co(III) complex with S,S-N,N-ethylenediaminedisuccinic acid are of exceptional quality and, coupled with the theory, spectacularly document the molecular behavior in resonance. This includes a huge enhancement of the chiral scattering, contribution of the antisymmetric polarizabilities to the signal, and the Herzberg-Teller effect significantly shaping the spectra. The chiral component is by about one order of magnitude bigger than for an analogous aluminum complex. The band assignment and intensity profile were confirmed by simulations based on density functional and vibronic theories. The resonance was attributed to the S0 →S3 transition, with the strongest signal enhancement of Raman and ROA spectral bands below about 800 cm-1 . For higher wavenumbers, other excited electronic states contribute to the scattering in a less resonant way. RROA spectroscopy thus appears as a unique tool to study the structure and electronic states of absorbing molecules in analytical chemistry, biology, and material science.

RaMALDI: Enabling simultaneous Raman and MALDI imaging of the same tissue section

Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain fused images with the advantages of both modalities has the potential of providing spatially resolved, sensitive, specific biomolecular information, but has so far involved two separate sample preparations, or even consecutive tissue sections for RSI and MALDI MSI, resulting in images with inherent disparities. We have developed RaMALDI, a streamlined, integrated, multimodal imaging workflow of RSI and MALDI MSI, performed on a single tissue section with one sample preparation protocol. We show that RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample, which will drive discoveries in cell biology, biomedicine, and pathology, and advance tissue diagnostics.

Research progress of sensors based on molecularly imprinted polymers in analytical and biomedical analysis

Molecularly imprinted polymers (MIPs) have had tremendous impact on biomimetic recognition due to their precise specificity and high affinity comparable to that of antibodies, which has shown the great advantages of easy preparation, good stability and low cost. The combination of MIPs with other analytical technologies can not only achieve rapid extraction and sensitive detection of target compounds, improving the level of analysis, but also achieve precise targeted delivery, in-vivo imaging and other applications. Among them, the recognition mechanism plays a vital role in chemical and biological sensing, while the improvement of the recognition element, such as the addition of new nanomaterials, can greatly improve the analytical performance of the sensor, especially in terms of selectivity. Currently, due to the need for rapid diagnosis and improved sensing properties (such as selectivity, stability, and cost-effectiveness), researchers are investigating new recognition elements and their combinations to improve the recognition capabilities of chemical sensing and bio-sensing. Therefore, this review mainly discusses the design strategies of optical sensors, electrochemical sensors and photoelectric sensors with molecular imprinting technology and their applications in environmental systems, food fields, drug detection and biology including bacteria and viruses.

Evading the Illusions: Identification of False Peaks in Micro-Raman Spectroscopy and Guidelines for Scientific Best Practice

Micro-Raman spectroscopy is an important analytical tool in a large variety of science disciplines. The technique is suitable for both identification of chemical bonds and studying more detailed phenomena like molecular interactions, material strain, crystallinity, defects, and bond formations. Raman scattering has one major weakness however: it is a very low probability process. The weak signals require very sensitive detection systems, which leads to a high probability of picking up signals from origins other than the sample. This complicates the analysis of the results and increases the risk of misinterpreting data. This work provides an overview of the sources of spurious signals occurring in Raman spectra, including photoluminescence, cosmic rays, stray light, artefacts caused by spectrometer components, and signals from other compounds in or surrounding the sample. The origins of these false Raman peaks are explained and means to identify and counteract them are provided.

RamanCMP: A Raman spectral classification acceleration method based on lightweight model and model compression techniques

In recent years, Raman spectroscopy combined with deep learning techniques has been widely used in various fields such as medical, chemical, and geological. However, there is still room for optimization of deep learning techniques and model compression algorithms for processing Raman spectral data. To further optimize deep learning models applied to Raman spectroscopy, in this study time, accuracy, sensitivity, specificity and floating point operations numbers(FLOPs) are used as evaluation metrics to optimize the model, which is named RamanCompact(RamanCMP). The experimental data used in this research are selected from the RRUFF public dataset, which consists of 723 Raman spectroscopy data samples from 10 different mineral categories. In this paper, 1D-EfficientNet adapted to the spectral data as well as 1D-DRSN are proposed to improve the model classification accuracy. To achieve better classification accuracy while optimizing the time parameters, three model compression methods are designed: knowledge distillation using 1D-EfficientNet model as a teacher model to train convolutional neural networks(CNN), proposing a channel conversion method to optimize 1D-DRSN model, and using 1D-DRSN model as a feature extractor in combination with linear discriminant analysis(LDA) model for classification. Compared with the traditional LDA and CNN models, the accuracy of 1D-EfficientNet and 1D-DRSN is improved by more than 20%. The time of the distilled model is reduced by 9680.9s compared with the teacher model 1D-EfficientNet under the condition of losing 2.07% accuracy. The accuracy of the distilled model is improved by 20% compared to the CNN student model while keeping inference efficiency constant. The 1D-DRSN optimized with channel conversion method saves 60% inference time of the original 1D-DRSN model. Feature extraction reduces the inference time of 1D-DRSN model by 93% with 94.48% accuracy. This study innovatively combines lightweight models and model compression algorithms to improve the classification speed of deep learning models in the field of Raman spectroscopy, forming a complete set of analysis methods and laying the foundation for future research.

Superhydrophobic Surface Modification of Polymer Microneedles Enables Fabrication of Multimodal Surface-Enhanced Raman Spectroscopy and Mass Spectrometry Substrates for Synthetic Drug Detection in Blood Plasma

Microneedles are widely used substrates for various chemical and biological sensing applications utilizing surface-enhanced Raman spectroscopy (SERS), which is indeed a highly sensitive and specific analytical approach. This article reports the fabrication of a nanoparticle (NP)-decorated microneedle substrate that is both a SERS substrate and a substrate-supported electrospray ionization (ssESI) mass spectrometry (MS) sample ionization platform. Polymeric ligand-functionalized gold nanorods (Au NRs) are adsorbed onto superhydrophobic surface-modified polydimethylsiloxane (PDMS) microneedles through the control of various interfacial interactions. We show that the chain length of the polymer ligands dictates the NR adsorption process. Importantly, assembling Au NRs onto the micrometer-diameter needle tips allows the formation of highly concentrated electromagnetic hot spots, which provide the SERS enhancement factor as high as 1.0 × 106. The micrometer-sized area of the microneedle top and high electromagnetic field enhancement of our system can be loosely compared with tip-enhanced Raman spectroscopy, where the apex of a plasmonic NP-functionalized sharp probe produces high-intensity plasmonic hot spots. Utilizing our NR-decorated microneedle substrates, the synthetic drugs fentanyl and alprazolam are analyzed with a subpicomolar limit of detection. Further analysis of drug-molecule interactions on the NR surface utilizing the Langmuir adsorption model suggests that the higher polarizability of fentanyl allows for a stronger interaction with hydrophilic polymer layers on the NR surface. We further demonstrate the translational aspect of the microneedle substrate for both SERS- and ssESI-MS-based detection of these two potent drugs in 10 drug-of-abuse (DOA) patient plasma samples with minimal preanalysis sample preparation steps. Chemometric analysis for the SERS-based detection shows a very good classification between fentanyl, alprazolam, or a mixture thereof in our selected 10 samples. Most importantly, ssESI-MS analysis also successfully identifies fentanyl or alprazolam in these same 10 DOA plasma samples. We believe that our multimodal detection approach presented herein is a highly versatile detection technology that can be applicable to the detection of any analyte type without performing any complicated sample preparation.

Spectroscopic techniques for monitoring stem cell and organoid proliferation in 3D environments for therapeutic development

Spectroscopic techniques for monitoring stem cell and organoid proliferation have gained significant attention in therapeutic development. Spectroscopic techniques such as fluorescence, Raman spectroscopy, and infrared spectroscopy offer noninvasive and real-time monitoring of biochemical and biophysical changes that occur during stem cell and organoid proliferation. These techniques provide valuable insight into the underlying mechanisms of action of potential therapeutic agents, allowing for improved drug discovery and screening. This review highlights the importance of spectroscopic monitoring of stem cell and organoid proliferation and its potential impact on therapeutic development. Furthermore, this review discusses recent advances in spectroscopic techniques and their applications in stem cell and organoid research. Overall, this review emphasizes the importance of spectroscopic techniques as valuable tools for studying stem cell and organoid proliferation and their potential to revolutionize therapeutic development in the future.

Addressing the unmet clinical need for low-volume assays in early diagnosis of pancreatic cancer

The incidental detection of pancreatic cysts, an opportunity for the early detection of pancreatic cancer, is increasing, owing to an aging population and improvements in imaging technology. The classification of pancreatic cystic precursors currently relies on imaging and cyst fluid evaluations, including cytology and protein and genomic analyses. However, there are persistent limitations that obstruct the accuracy and quality of information for clinicians, including the limited volume of the complex, often acellular, and proteinaceous milieu that comprises pancreatic cyst fluid. The constraints of currently available clinical assays lead clinicians to the subjective and inconsistent application of diagnostic tools, which can contribute to unnecessary surgery and missed pancreatic cancers. Herein, we describe the pathway toward pancreatic cyst classification and diagnosis, the volume requirements for several clinically available diagnostic tools, and some analytical and diagnostic limitations for each assay. We then discuss current and future work on novel markers and methods, and how to expand the utility of clinical pancreatic cyst fluid samples. Results of ongoing studies applying SERS as a detection mode suggest that 50 μL of pancreatic cyst fluid is more than sufficient to accurately rule out non-mucinous pancreatic cysts with no malignant potential from further evaluation. This process is expected to leave sufficient fluid to analyze a follow-up, rule-in panel of markers currently in development that can stratify grades of dysplasia in mucinous pancreatic cysts and improve clinical decision-making.