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Matrix effects limit the application of surface-enhanced Raman scattering (SERS) technology in the field of food safety. This study elucidated it from the perspective of protein corona by employing a model system for melamine SERS detection in milk. Compared with the melamine standard solution, higher detection limits (1 mg/L and 10 mg/L) are observed in milk matrix. The melamine signal exhibits an 80% reduction in whey protein solution, suggesting that protein has a significant impact on SERS signals. The changes in particle size, zeta potential and UV-vis spectra indicate the AuNPs interact with whey protein. Forming protein corona inhibits the melamine-induced AuNPs aggregation, reducing the number of 'hot spot' and the adsorption of melamine on AuNPs (from 0.28 mg/L to 0.07 mg/L), which may be responsible for signal loss. The found matrix effect from protein corona provides new insights for developing strategies about reducing matrix effect in SERS application., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Colorants, especially synthetic colorants, play a crucial role in enhancing the aesthetic qualities of food owing to their cost-effectiveness and stability against environmental factors. Ensuring the safe and regulated use of colorants is essential for maintaining consumer trust in food safety. Various preparation and analytical technologies, which are continuously undergoing improvement, are currently used to quantify of synthetic colorants in food products. This paper reviews recent developments in analytical techniques for synthetic food colorants, detection and compares the operational principles, advantages, and disadvantages of each technology. Additionally, it also explores advancements in these technologies, discussing several invaluable tools of analysis, such as high-performance liquid chromatography, liquid chromatography-tandem mass spectrometry, electrochemical sensors, digital image analysis, near-infrared spectroscopy, and surface-enhanced Raman spectroscopy. This comprehensive overview aims to provide valuable insights into current progress and research in the field of food colorant analysis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Chiral-selective molecular interactions are considered crucial in numerous physiological processes. Chiral-selective analytical methods of biomolecules with sufficient sensitivity are of great interest in numerous applications. Several surface-enhanced Raman scattering (SERS)-based methods have recently been reported for chiral sensing of biomolecules. However, the lack of molecular-level understanding of SERS spectral changes of reporter and analyte molecules may mislead the development of chiral detection methods., Results: We report the chiral sensing of glucose (Glu) by SERS of L- and D-phenylalanine (Phe) with colloidal gold nanoparticles (AuNPs) synthesized by borohydride ions. The Phe SERS showed drastic spectral changes only when Glu of the same chirality as Phe was added, which also showed strong dependence on Glu concentration. The increase of δ(COO - ) and decrease of ν s (COO - ) modes in Phe SERS, exclusively observed with the chiral-selective bimolecular interactions of chirally matching Glu, are understood as modified surface adsorption geometry of the carboxylate group. Quantitative spectral analysis for the Glu concentration of a specific chirality showed the detection limit down to 2 × 10 -9 - 2 × 10 -7  M levels depending on the existence of the opposite enantiomer of Glu., Significance: In this study, we demonstrated that the Phe SERS on AuNPs can be utilized in the chiral sensing of Glu molecules with quantitative concentration analysis. The bimolecular interactions of surface-adsorbed Phe and chirally matching Glu are suggested for the chiral recognition of Phe SERS. These results imply that a molecule-level understanding is indispensable for developing SERS-based chiral sensing methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Versatile methods for the quantification of intracellular cholesterol are essential for understanding cellular physiology and for diagnosing disorders linked to cholesterol metabolism. Here we used Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) to measure changes in cholesterol after incubating human fibroblasts with increasing concentrations of cholesterol-methyl-β-cyclodextrin. RS and SERS were sensitive and accurate enough to detect high levels of cholesterol in fibroblasts from patients affected by type C Niemann-Pick disease (NPC), a lysosomal storage disorder characterized by the primary accumulation of cholesterol. Moreover, SERS was able to distinguish between fibroblasts from different NPC patients, demonstrating higher accuracy than RS and standard fluorescent labeling of cholesterol with filipin III. We show that the type of gold nanoparticles used as signal enhancer surfaces in our SERS measurements are internalized by the cells and are eventually found in lysosomes, the main site of accumulation of cholesterol in NPC fibroblasts. The higher sensitivity of SERS can thus be attributed to the specific trafficking of our gold nanoparticles into these organelles. Our results indicate that RS and SERS can be used as sensitive and accurate methods for the evaluation of intracellular cholesterol content, allowing for the potential development of an optical detection tool for the ex-vivo screening and monitoring of those diseases characterized by abnormal modification in cholesterol levels., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

The microbiome represents the natural presence of microorganisms, and exploring, understanding, and leveraging its functions will bring about significant breakthroughs in life sciences and applications. Raman-activated cell sorting (RACS) enables the correlation of phenotype and genotype at the single-cell level, offering a solution to the bottleneck in microbial community functional analysis caused by challenges in cultivating diverse microorganisms. However, current labor-intensive manual procedures fall short in catering to the demands of single-cell functional analysis in microbial communities. To address this issue, we developed an artificial intelligence-assisted Raman-activated cell sorting system (AI-RACS) that integrates precise single-cell positioning, automated data collection, optical tweezers capture, and single-cell printing to elevate microbial single-cell RACS from manual to automated, validating the efficacy of the system by isolating aluminum-tolerant microbes from acidic soil microbiota. Leveraging the AI-RACS framework, we sorted 13 strains from red soil samples under near-in situ conditions, with all demonstrating strong aluminum tolerance. AI-RACS efficiently segregates microbial cells from intricate environmental samples, investigating their functional attributes and presenting a novel tool for microbial research and applications.

This study reports development of a microfluidic device for highly sensitive and selective detection of a β-amyloid peptide (Aβ 1-42 ) in simulated cerebrospinal fluid, using surface-enhanced Raman spectroscopy (SERS). The device ensemble comprises a purine ligand (Pu) and its interaction with silver nanoparticles (AgNPs) to generate SERS hotspots. The low surface energy of the synthesized Pu ligand and high surface energy of AgNPs are utilized for the functionalization and formation of a Pu-AgNP SERS substrate. We have integrated a novel polydimethylsiloxane (PDMS) microfluidic device with Pu-AgNPs using a combination of photo- and soft lithography fabrication, sealed by thermal cross-linking with another layer of PDMS, to produce an effective screening platform for Aβ 1-42 . The SERS spectrum from the microfluidic device affords almost noise-free measurements, with excellent limit-of-detection values.

In this study, we developed ratiometric surface-enhanced Raman scattering (SERS) biosensors using Ag-Au alloy nanoflowers as SERS substrates, molecules having amide bonds and alkyne groups (Tag A) as Raman reporters, and sodium thiocyanate as an internal standard molecule (Tag B) for the sensitive detection of human carboxylesterase-1 (hCE1) in HepG-2 cells. The correlation between HepG-2 cell damage and hCE1 activity levels was investigated. Both Tag A's alkyne group and Tag B's cyanide group produced characteristic SERS signals in the Raman-silent region ( I 2000 cm -1 and I 2115 cm -1 , respectively). The hydrolysis of the amide bond in Tag A via hCE1 and the shedding of the alkyne group led to a reduction in the SERS signal intensity observed at I 2000 cm -1 . Conversely, the SERS signal intensity of Tag B at I 2115 cm -1 exhibited a consistent pattern. As the activity level of hCE1 and the ratiometric peak intensity ( I 2000 cm -1 / I 2115 cm -1 ) correlated negatively, hCE1 could be quantitatively detected within the range of 10 -2 to 2 × 10 2 ng·mL -1 , with a detection limit of 7.3 pg·mL -1 . The ratiometric SERS probe strategy, in which a ratio response is employed, permits sensitive and reproducible SERS detection by facilitating intrinsic calibration to rectify signal fluctuations resulting from temporal and spatial variations in the detection conditions. Concurrently, the implementation of Raman-silent region reporter molecules mitigates the interference from endogenous biomolecules in SERS measurements and offers a novel approach for achieving highly sensitive and interference-free detection of intracellular hCE1.

A bimetallic core-shell-shell nanorods structure with gap-embedded Raman reporter 5,5'-dithio-bis-(2-nitrobenzoic acid) (DTNB) was developed and applied as a SERS-active nanotag in surface-enhanced Raman scattering lateral flow immunoassay (SERS-LFIA) of the insecticide fipronil. Due to the strong SERS signal of the Au DTNB @Ag DTNB @AgNRods, fipronil is detected with extremely low detection limit of 4.6 pg/mL. To the best of our knowledge, the proposed technique is the first SERS-LFIA of fipronil, proven to be effective in the selective determination of the target analyte and capable of detecting fipronil in a matrix of food samples (cucumber and apple juice) with recoveries of 97.0-117.0%. Moreover, the applied functionalization of the SERS nanotag with anti-species antibodies has provided a versatile immunoprobe that could improve performance of different LFIAs., Competing Interests: Declarations Conflict of interest The authors have no relevant financial or non-financial interests to disclose.The authors have no conflicts of interest to declare that are relevant to the content of this article.All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.The authors have no financial or proprietary interests in any material discussed in this article., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Skin health relies heavily on a well-maintained cutaneous barrier. While the detrimental effects of UV radiation on the epidermis are established, the impact of blue light, a significant component of sunlight and artificial sources, is less clear. This study aims to explore blue light's influence on the reconstructed human epidermis (RHE) using two complementary analytical approaches: Raman microspectroscopy and normal phase liquid chromatography coupled with high-resolution mass spectrometry (NPLC/HR-MS n ). RHE samples were exposed to blue light (415 nm and 455 nm) during different stages of their maturation. Raman spectra were acquired for both irradiated and non-irradiated (control) samples. Raman descriptors were analyzed to assess potential alterations in the structural organization of proteins and lipids' conformational changes. Additionally, lipids from RHE samples were extracted and analyzed using NPLC/HR-MS n . Blue light exposure led to changes in the structural organization of RHE lipids and proteins, as well as changes in the lipid composition. These changes varied depending on the wavelength and exposure dose. Exposure to blue light could disrupt the integrity of the skin's protective barrier, leading to increased sensitivity to environmental stressors and potential skin damage.

When exposed to an alternating current (AC) electric field, a polarized microparticle is moved by the interaction between the voltage-induced dipoles and the AC electric field under dielectrophoresis (DEP). The DEP force is widely used for manipulation of microparticles in diverse practical applications such as 3D manipulation, sorting, transfer, and separation of various particles such as living cells. In this study, we propose the integration of surface-enhanced Raman spectroscopy (SERS), an extremely sensitive and versatile technique based on the Raman scattering of molecules supported by nanostructured materials, with DEP using a microfluidic device. The microfluidic device combines microelectrodes with gold nanohole arrays to characterize the electrophysiological and biochemical properties of biological cells. The movement of particles, which varies depending on the electrical properties such as conductivity and permittivity of particles, can be manipulated by the cross-frequency change. For proof of concept, Raman spectroscopy using the DEP-SERS integration was performed for polystyrene beads and biological cells and resulted in an improved signal-to-noise ratio by determining the direction of the DEP force applied to the cells with respect to the applied AC power and collecting them on the nanohole arrays. The result illustrates the potential of the concept for simultaneously examining the electrical and biochemical properties of diverse chemical and biological microparticles in the microfluidic environment.

Aβ plaques are a main feature of Alzheimer's disease, and pathological alterations especially in their microenvironment have recently come into focus. However, a holistic imaging approach unveiling these changes and their biochemical nature is still lacking. In this context, we leverage confocal Raman microscopy as unbiased tool for non-destructive, label-free differentiation of progressive biomolecular changes in the Aβ plaque microenvironment in brain tissue of a murine model of cerebral amyloidosis. By developing a detailed approach, overcoming many challenges of chemical imaging, we identify spatially-resolved molecular signatures of disease-associated structures. Specifically, our study reveals nuclear condensation, indicating cellular degeneration, and increased levels of cytochrome c, showing mitochondrial dysfunction, in the vicinity of Aβ plaques. Further, we observe severe accumulation of especially unsaturated lipids. Thus, our study contributes to a comprehensive understanding of disease progression in the Aβ plaque microenvironment, underscoring the prospective of Raman imaging in neurodegenerative disorder research., Competing Interests: Competing interests The authors declare no competing interests. Ethics Animal experiments and husbandry were approved by the animal welfare committee of the Johann Wolfgang Goethe University (ethical committee) and the Regierungspraesidium Darmstadt (board); protocol numbers FR1001/FR2018/FR2010., (© 2024. The Author(s).)

Background: Antibiotic resistance stands as a critical medical concern, notably evident in commonly prescribed beta-lactam antibiotics. The imperative need for expeditious and precise early detection methods underscores their role in facilitating timely intervention, curbing the propagation of antibiotic resistance, and enhancing patient outcomes., Results: This study introduces the utilization of surface-enhanced Raman spectroscopy (SERS) in tandem with machine learning (ML) for the sensitive detection of characteristic gene fragments responsible for antibiotic resistance appearance and spreading. To make the detection procedure close to the real case, we used bacterial plasmids as starting biological objects, containing or not the characteristic gene fragment (up to 1:10 ratio), encoding beta-lactam antibiotics resistance. The plasmids were subjected to enzymatic digestion and without preliminary purification or isolation the created fragments were captured by functional SERS substrates. Based on subsequent SERS measurements, a database was created for the training and validation of ML. Method validation was performed using separately measured spectra, which did not overlap with the database used for ML training. To check the efficiency of recognising the target fragment, control experiments involved bacterial plasmids containing different resistance genes, the use of inappropriate enzymes, or the absence of plasmid., Significance: SERS-ML allowed express detection of bacterial plasmids containing a characteristic gene fragment up to the 10 -7 concentration of the initial plasmid, despite the complex composition of the biological sample, including the presence of interfering plasmids. Our approach offers a promising alternative to existing methods for monitoring antibiotic-resistant bacteria, characterized by its simplicity, low detection limit, and the potential for rapid and straightforward analysis., Competing Interests: Declaration of competing interest X The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: In the context of modern agriculture, the proliferation of chemical use calls for enhanced pesticide detection to safeguard food quality and public health. The development of accurate testing methodologies is imperative to mitigate the environmental impact of pesticides and ensure the integrity of ecosystems, thereby reflecting the pressing need for advancements in agricultural safety protocols. Therefore, the development of highly sensitive monitoring technology for detecting pesticide residues in agricultural products is necessary for safeguarding human health, ensuring food safety, and maintaining environmental sustainability., Results: Herein, a controllable surface charge on single tungsten atom-modified gold nanoparticles was used to create an electrostatic force with positively charged pesticide residues. Moreover, hydrogen bonds formed by single-atom sites can induce analyte-adsorbed nanoparticle aggregation, and the sizes of single-tungsten-atom-decorated AuNPs can maintain a gap between each other, resulting in improved SERS detection sensitivity through analyte enrichment at gold nanoparticle hotspots. In terms of the detection limits for pesticide residue analysis, we can effectively achieve an ultrahigh sensitivity of 0.1 ppb for acetamiprid, paraquat and carbendazim, which is among the best SERS sensitivities at the state of the art. For apple sample analysis, our work demonstrated good reproductivity (RSD<6 %) and a strong linear relationship (R 2  ≥ 0.97) for 4 pesticide residues after optimizing the pretreatment process, which proves the enormous potential in quantitative analysis., Significance: Single-atom sites hotspot are firstly successfully achieved and uniformly dispersed between Au nanoparticle, which can effectively increase the sensitivity, keep stability of the Raman scattering signals and possess a significant improvement beyond that of undecorated hotspots when applied in pesticide residue detection. This method can be employed as a universal strategy to capture pesticide residues at hotspots for SERS detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Hg 2+ is a highly toxic heavy metal ion that poses serious risks to human health and the environment. Due to its tendency to accumulate, it can easily enter the human body through the food chain, making it crucial to develop detection sensors that mimic real environmental conditions. To achieve this, our study employed a surface-enhanced Raman scattering (SERS) sensor using two strategies. First, we designed a highly selective probe by optimizing the probe and reporter DNA strands to bind Hg 2+ within a thymine-thymine mismatch. Second, we used the double coffee ring effect to concentrate the optimized probe DNA. These two strategies greatly enhanced the SERS signal, resulting in a sensor with exceptional sensitivity, a low detection limit of 208.71 fM, and superior selectivity for Hg 2+ . The practical application of the sensor was demonstrated by successfully detecting Hg 2+ in drinking water, tap water, canned tuna, and tuna sashimi. Additionally, the experimental results were presented in a pizza-shaped SERS mapping image, allowing users to estimate Hg 2+ concentrations through color, providing a user-friendly and intuitive method for data comprehension and analysis. Our study presents a promising approach for sensitive and reliable Hg 2+ detection, with potential implications for environmental monitoring and food safety., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Jinsung Park reports financial support was provided by National Research Foundation of South Korea (NRF). Joohyung Park reports financial support was provided by National Research Foundation of South Korea (NRF). Juneseok You reports financial support was provided by Korea University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Surface-enhanced Raman Scattering (SERS) has become a powerful spectroscopic technology for highly sensitive detection. However, SERS is still limited in the lab because it either requires complicated preparation or is limited to specific compounds, causing poor applicability for practical applications. Herein, a micro-macro SERS strategy, synergizing polymer-assisted printed process with paper-tip enrichment process, is proposed to fabricate highly sensitive paper cartridges for sensitive practical applications. The polymer-assisted printed process finely aggregates nanoparticles with a discrete degree of 1.77, and SERS results are matched with theoretical enhancement, indicating small cluster-dominated hotspots at the micro-scale and thus 41-fold SERS increase compared to other aggregation methods. The paper-tip enrichment process moves molecules in a fluid into small tips filled with plasmonic clusters, and molecular localization at hotspots is achieved by the simulation and optimization of fluidic velocity at the macro-scale, generating a 39.5-fold SERS sensibility increase in comparison with other flow methods. A highly sensitive paper cartridge contains a paper-tip and a 3D-printed cartridge, which is simple, easy-to-operate, and costs around 2 US dollars. With a detection limit of 10 -12  M for probe molecules, the application of real samples and multiple analytes achieves single-molecule level sensitivity and reliable repeatability with a 30-min standardized procedure. The micro-macro SERS strategy demonstrates its potential in practical applications that require point-of-care detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He/she is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Spatially offset Raman scattering (SORS) line-mapping was explored as a versatile tool to examine accuracy variations in compositional analyses of tablets with different particle sizes. SORS spectra collected near the laser irradiation were less representative of tablet composition due to the limited spectroscopic sampling volume, while the signal-to-noise (S/N) ratios of corresponding spectra were higher. On the other hand, SORS spectra at longer offset distances were better representative of tablet composition, while their S/N ratios were decreased considerably. Therefore, the use of only a certain portion of sliced (line-mapped) spectra balanced with the sample representation and S/N ratio could be advantageous to enhance accuracy. Moreover, a group of optimal slice spectra is expected to vary when the particle size of the tablet changes since the characteristics of internal photon propagation also would change. For the overall examination, SORS spectra of 30 Anaprox tablets (composed of 4 constituents including naproxen sodium) with 2 particle sizes (88.4 ± 11.8 µm and 118.9 ± 38.8 µm) were analyzed, and the concentrations of three components in these tablets were determined. A total of 6 cases (3 components and 2 particle sizes) were examined. When the average optimal slice spectra were employed in each case, the errors were lower compared to those using the average of all slice spectra. The demonstrated scheme was versatile to study the offset distance-dependent accuracy variations according to particle size and target component., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Early and accurate detection of colorectal cancer (CRC) is critical for improving patient outcomes. Existing diagnostic techniques are often invasive and carry risks of complications. Herein, we introduce a plasmonic gold nanopolyhedron (AuNH)-coated needle-based surface-enhanced Raman scattering (SERS) sensor, integrated with endoscopy, for direct mucus sampling and label-free detection of CRC. The thin and flexible stainless-steel needle is coated with polymerized dopamine, which serves as an adhesive layer and simultaneously initiates the nucleation of gold nanoparticle (AuNP) seeds on the needle surface. The AuNP seeds are further grown through a surface-directed reduction using Au ions-hydroxylamine hydrochloride solution, resulting in the formation of dense AuNHs. The formation mechanism of AuNHs and the layered structure of the plasmonic needle-based SERS (PNS) sensor are thoroughly analyzed. Furthermore, a strong field enhancement of the PNS sensor is observed, amplified around the edges of the polyhedral shapes and at nanogap sites between AuNHs. The feasibility of the PNS sensor combined with endoscopy system is further investigated using mouse models for direct colonic mucus sampling and verifying noninvasive label-free classification of CRC from normal controls. A logistic regression-based machine learning method is employed and successfully differentiates CRC and normal mice, achieving 100% sensitivity, 93.33% specificity, and 96.67% accuracy. Moreover, Raman profiling of metabolites and their correlations with Raman signals of mucus samples are analyzed using the Pearson correlation coefficient, offering insights for identifying potential cancer biomarkers. The developed PNS-assisted endoscopy technology is expected to advance the early screening and diagnosis approach of CRC in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Pancreatic cancer is notoriously lethal due to its late diagnosis and poor patient response to treatments, posing a significant clinical challenge. This study introduced a novel approach that combines a single-cell capturing platform, tumor-targeted silver (Ag) nanoprobes, and precisely docking tapered fiber integrated with Raman spectroscopy. This approach focuses on early detection and progression monitoring of pancreatic cancer. Utilizing tumor-targeted Ag nanoparticles and tapered multimode fibers enhances Raman signals, minimizes light loss, and reduces background noise. This advanced Raman system allows for detailed molecular spectroscopic examination of individual cells, offering more practical information and enabling earlier detection and accurate staging of pancreatic cancer compared to conventional multicellular Raman spectroscopy. Transcriptomic analysis using high-throughput gene screening and transcriptomic databases confirmed the ability and accuracy of this method to identify molecular changes in normal, early, and metastatic pancreatic cancer cells. Key findings revealed that cell adhesion, migration, and the extracellular matrix are closely related to single-cell Raman spectroscopy (SCRS) results, highlighting components such as collagen, phospholipids, and carotene. Therefore, the SCRS approach provides a comprehensive view of the molecular composition, biological function, and material changes in cells, offering a novel, accurate, reliable, rapid, and efficient method for diagnosing and monitoring pancreatic cancer., Competing Interests: Declaration of Competing interest All authors disclosed no relevant relationships., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Surface-enhanced Raman scattering (SERS) has been extensively applied to detect complex analytes due to its ability to enhance the fingerprint signals of molecules around nanostructured metallic surfaces. Thus, it is essential to design SERS-active nanostructures with abundant electromagnetic hotspots in a probed volume according to the dimensions of the analytes, as the analytes must be located in their hotspots for maximum signal enhancement. Herein, we demonstrate a simple method for detecting robust SERS signals from multi-scaled bioanalytes, regardless of their dimensions in the liquid state, through a photothermally driven co-assembly with colloidal plasmonic nanoparticles as signal enhancers. Under resonant light illumination, plasmonic nanoparticles and analytes in the solution quickly assemble at the focused surface area by convective movements induced by the photothermal heating of the plasmonic nanoparticles without any surface modification. Such collective assemblies of plasmonic nanoparticles and analytes were optimized by varying the optical density and surface charge of the nanoparticles, the viscosity of the solvent, and the light illumination time to maximize the SERS signals. Using these light-induced co-assemblies, the intrinsic SERS signals of small biomolecules can be detected down to nanomolar concentrations based on their fingerprint spectra. Furthermore, large-sized biomarkers, such as viruses and exosomes, were successfully detected without labels, and the complexity of the collected spectra was statistically analyzed using t-distributed stochastic neighbor embedding combined with support vector machine (t-SNE + SVM). The proposed method is expected to provide a robust and convenient method to sensitively detect biologically and environmentally relevant analytes at multiple scales in liquid samples., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Aflatoxin B 1 (AFB 1 ), a pernicious constituent of the aflatoxin family, predominantly contaminates cereals, oils, and their derivatives. Acknowledged as a Class I carcinogen by the World Health Organization (WHO), the expeditious and quantitative discernment of AFB 1 remains imperative. This investigation delineates that aluminum ions can precipitate the coalescence of iodine-modified silver nanoparticles, thereby engendering hot spots conducive for label-free AFB 1 identification via Surface-Enhanced Raman Spectroscopy (SERS). This methodology manifests a remarkable limit of detection (LOD) at 0.47 fg/mL, surpassing the sensitivity thresholds of conventional survey techniques. Moreover, this method has good anti-interference ability, with a relative error of less than 10% and a relative standard deviation of less than 6% in quantitative results. Collectively, these findings illuminate the substantial application potential and viability of this approach in the quantitative analysis of AFB 1 , underpinning a significant advancement in food safety diagnostics., Competing Interests: Declaration of competing interest We confirm that the present research is original and this manuscript has not been published previously in any other journal. There is no conflict of interest exits in the submission of this manuscript. All authors have approved the submission., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The molecule of 2-Biphenyl Carboxylic Acid (2BCA), which contains peculiar features, was explored making use of density functional theory (DFT) and experimental approaches in the area of quantum computational research. The optimised structure, atomic charges, vibrational frequencies, electrical properties, electrostatic potential surface (ESP), natural bond orbital analysis and potential energy surface (PES) were obtained applying the B3LYP approach with the 6-311++ G (d,p) basis set.. The 2BCA molecule was examined for possible conformers using a PES scan. The methods applied for spectral analyses included FT-IR, FT-RAMAN, NMR, and UV-Vis results. Vibrational frequencies for all typical modes of vibration were found using the Potential Energy Distribution (PED) data. The UV-Vis spectrum was simulated using the TD-DFT technique, which is also seen empirically. The Gauge-Invariant Atomic Orbital (GIAO) approach was employed to model and study the 13 C and 1 H NMR spectra of the 2BCA molecule in a CDCL3 solution. The spectra were then exploited experimentally to establish their chemical shifts. To predict the donor and acceptor interaction, the NBO analysis was used. The electrostatic potential surface was employed to anticipate the locations of nucleophilic and electrophilic sites. Hirshfeld surfaces and their related fingerprint plots are exploited for the investigation of intermolecular interactions. Reduced Density Gradient (RDG) helps to measure and illustrate electron correlation effects, offering precise insights into chemical bonding, reactivity, and the electronic structure of 2BCA. According to Lipinski and Veber's drug similarity criteria, 2BCA exhibits the typical physicochemical and pharmacokinetic properties that make it a potential oral pharmaceutical candidate. According to the findings of a molecular docking study, the 2BCA molecule has promise as a treatment agent for the Nipah virus (PDB ID: 6 EB9), which causes severe respiratory and neurological symptoms in humans., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In recent years, incidents of pesticide pollution and abuse of feed additives have occurred frequently, which pose a great threat to human health. Raman spectroscopy has become an important method in the field of food safety due to its rapidity, simplicity and sensitivity. It is important to obtain complex structure to promote surface-enhanced Raman scattering (SERS) effect. In this study, gold helical nanoparticles with rich surface structure were synthesized using cysteine as induce agent. Notably, the complex helical structure and tip led to an excellent electromagnetic enhancement property. The helical structure showed ultra-sensitive detection of hazardous molecular, such as thiram and ractopamine. Interestingly, the D/L-Au structure had significant chiral optical activity and could be used as an unlabeled SERS platform for enantiomer identification. This study provided an effective strategy for the detection of pesticides and feed additives, which could be applied in other aspects of food safety in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of 1 H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of 1 H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

In this study, electroporation-surface-enhanced Raman scattering (SERS) was applied to rapidly measure intracellular pH. The generation of a sensitive SERS probe for measuring pH in the range of 6.0-8.0 was accomplished through the conjugation of the pH-sensitive molecule 4-mercaptobenzoic acid (4-MBA) to the surface of gold nanoparticles (Au NPs) through its thiol functional group. This bioprobe was then rapidly introduced into nasopharyngeal carcinoma CNE-1 cells by electroporation, followed by SERS scanning and the fitting of intensity ratios of each detection point's Raman peaks at 1423 cm -1 and 1072 cm -1 , to create the pH distribution map of CNE-1 cells. The electroporation-SERS assay introduces pH bioprobes into a living cell in a very short time and disperses the nanoprobe throughout the cytoplasm, ultimately enabling rapid and comprehensive pH analysis of the entire cell. Our work demonstrates the potential of electroporation-SERS for the biochemical analysis of live cells., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The impact of microbial infections is increasing over time, and it is one of the major reasons for death in both developed and developing countries. colistin is considered as the antibiotic of last choice for infections brought by major multidrug-resistant (MDR), gram-negative bacteria such as Enterobacter species, Acinetobacter species, and Pseudomonas aeruginosa. Existing approaches to diagnose these resistant species are relatively slow and take up to 2 to 3 days. In this work, we propose a novel interdisciplinary method based on Raman spectroscopy and heavy water to identify colistin-resistant microbes. Our hypothesis is based on the fact that resistant bacteria will be metabolically active in the culture medium containing antibiotics and heavy water, and these bacteria will take up deuterium instead of hydrogen to newly synthesized lipids and proteins. This effect will generate a 'C - D' bond-specific Raman spectral marker. Successful identification of this band in the spectral profile can confirm the presence of colistin-resistant bacteria. We have validated the efficacy of this approach in identifying colistin-resistant bacteria spiked in artificial urine and have compared sensitivity at different bacterial concentrations. Overall findings suggest that heavy water can potentially serve as a suitable Raman probe for identifying metabolically active colistin-resistant bacteria via urine under clinically implementable time and can be used in clinical settings after validation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The COVID-19 pandemic has highlighted the importance of point-of-care (POC) pathogen detection. Accurate and accessible diagnostic techniques for virus identification are crucial for controlling the spread of diseases and have profound implications for our communities and global health. Reagentless surface-enhanced Raman scattering (SERS) sensors offer a promising solution for POC testing due to their capability to function without complex processing steps. However, their application in this space is limited by the fact that these solution-based assays are challenging to administer, transport and store. To overcome these limitations, we employed a freeze-drying (lyophilization) process on reagentless SERS sensors and investigated their improved stability and shelf-life. We explored this mechanism using different concentrations of cryoprotectants. Lyophilized sensors were then tested in a mix-and-detect fashion by adding to the dry sensors a drop of the sample, consisting of saliva spiked with target DNA oligonucleotides relative to different SARS-CoV-2 variants. In addition, we further uncovered how lyophilization benefits sensors with a DNA-catalysis mechanism. In summary, our findings indicate that lyophilization substantially enhances the practicality and usability of reagentless SERS sensors, contributing to the translation of this powerful diagnostic tool to POC testing in remote areas with limited resources.

Semi-solid preparations such as ointments, creams, gels, and pastes are common topical dosage forms with complex compositions and microstructures. pH is a critical quality attribute for semi-solid preparations, affecting properties such as particle size distribution, drug dissociation state, and rheological behavior. Currently, traditional pH electrode methods only provide an "average" pH for large-volume matrices, with low spatial resolution and accuracy. Microenvironment pH detection is crucial for accurately assessing semi-solid preparations. Herein, we developed pH-sensitive surface enhanced Raman scattering (SERS) microtips to achieve localized pH detection in semi-solid preparations. SERS microtips were prepared from glass needles with a tip size of around 1 μm and gold nanoparticles (Au NPs) grown in situ on glass surfaces for SERS enhancement. 4-Mercaptopyridine was selected as a pH sensitive Raman reporter and immobilized on the Au NPs, exhibiting characteristic Raman peak shifts within the pH range of 3-10. The SERS microtips were employed to conduct highly sensitive pH measurements in localized areas of 15 commercial ointments, 8 gels, and 1 laboratory-made ointment, providing higher spatial resolution and microenvironment differentiation compared to pH meters. The SERS microtips were used to monitor pH changes over time in ointment applied to localized wounds on live mice. This work introduces a new tool for pH detection in semi-solid preparations, offering a new method to enhance the prescription process and quality assessment of complex preparations like topical semi-solid preparations.

Dysregulation of receptor tyrosine kinases (RTKs) has been shown to correlate with cancer cell proliferation and drug resistance. Thus, monitoring the activity of RTKs at a chemical level could provide new biomedical insights and methods to assess the drug efficacy. However, direct monitoring of kinase activity is challenging and most commonly relies on in vitro techniques such as Western blotting and ELISAs. Herein, we report the development of a gold nanoparticle-based surface-enhanced Raman scattering (SERS) sensor, which allows the real-time monitoring of tyrosine phosphorylation of a reporter peptide (Axltide) by the Axl enzyme. We demonstrate that our sensor shows strong signal localization, and we are able to detect tyrosine phosphorylation of the reporter peptide through chemical phosphorylation and enzymatically with similar peak changes to those observed in the spontaneous Raman spectra. Through monitoring the SERS spectrum, we can observe changes in phosphorylation in real time.

In the current biopharmaceutical scenario, constant bioprocess monitoring is crucial for the quality and integrity of final products. Thus, process analytical techniques, such as those based on Raman spectroscopy, have been used as multiparameter tracking methods in pharma bioprocesses, which can be combined with chemometric tools, like Partial Least Squares (PLS) and Artificial Neural Networks (ANN). In some cases, applying spectra pre-processing techniques before modeling can improve the accuracy of chemometric model fittings to observed values. One of the biological applications of these techniques could have as a target the virus-like particles (VLP), a vaccine production platform for viral diseases. A disease that has drawn attention in recent years is Zika, with large-scale production sometimes challenging without an appropriate monitoring approach. This work aimed to define global models for Zika VLP upstream production monitoring with Raman considering different laser intensities (200 mW and 495 mW), sample clarification (with or without cells), spectra pre-processing approaches, and PLS and ANN modeling techniques. Six experiments were performed in a benchtop bioreactor to collect the Raman spectral and biochemical datasets for modeling calibration. The best models generated presented a mean absolute error and mean relative error respectively of 3.46 × 10 5  cell/mL and 35 % for viable cell density (Xv); 4.1 % and 5 % for cell viability (CV); 0.245 g/L and 3 % for glucose (Glc); 0.006 g/L and 18 % for lactate (Lac); 0.115 g/L and 26 % for glutamine (Gln); 0.132 g/L and 18 % for glutamate (Glu); 0.0029 g/L and 3 % for ammonium (NH 4 + ); and 0.0103 g/L and 2 % for potassium (K + ). Sample without conditioning (with cells) improved the models' adequacy, except for Glutamine. ANN better predicted CV, Gln, Glu, and K+, while Xv, Glc, Lac, and NH 4 + presented no statistical difference between the chemometric tools. For most of the assessed experimental parameters, there was no statistical need for spectra pre-filtering, for which the models based on the raw spectra were selected as the best ones. Laser intensity impacts quality model predictions in some parameters, Xv, Gln, and K + had a better performance with 200 mW of intensity (for PLS, ANN, and ANN, respectively), for CV the 495 mW laser intensity was better (for PLS), and for the other biochemical variables, the use of 200 or 495 mW did not impact model fitting adequacy., Competing Interests: Declaration of competing interest The authors declare that no relevant conflicts of interest concerning the content of this scientific paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

In this research, women were diagnosed as pregnant based on blood serum samples Raman spectroscopy. Raman spectroscopy is a vibrational technique that provides information on the chemical composition of samples. The Raman techniques have significantly impacted the study of various degenerative diseases, particularly cancer detection, using less invasive methods such as the analysis of blood serum samples. Additionally, these techniques have been used to study the health status of patients, which is often difficult to monitor using conventional techniques. This study obtained serum samples from 12 women diagnosed as pregnant and 11 non-pregnant volunteers (controls). Spectra were collected using a LabRAM HR800 Raman Spectrometer (Horiba Jobin-Yvon) with an 830 nm wavelength laser. For each serum sample, 10 Raman spectra were obtained by focusing the laser, using a 100 × microscope objective, on different points of the sample with an exposure time of 40 seconds and an irradiation power of 17 mW. The raw spectra were processed using baseline correction and smoothing to remove noise, fluorescence, and shot noise. Subsequently, the spectra were normalized and analyzed using the multivariate statistical method of Principal Component Analysis (PCA). In these spectra, the characteristic bands of main blood serum biomolecules such as phenylalanine (Phe), tyrosine (Tyr), glutathione, amide III, phospholipid, carotene, and tryptophan (Trp) can be observed. Nevertheless, when analyzing the average spectra of pregnant and non-pregnant women, the main spectral differences were associated with variations in molecules like glutathione, tryptophan (Trp), tyrosine, and phenylalanine, which occur during the first trimesters of pregnancy. This aligns with findings previously reported by other studies. Furthermore, the serum samples from pregnant and non-pregnant patients can be effectively discriminated using PCA applied to the Raman spectra, revealing two distinct clusters in the PCA plot corresponding to each group's status. The results demonstrate that pregnancy can be determined based on blood serum samples Raman spectroscopy with 92.5 % sensitivity and 97.3 % specificity. Although there are very effective devices on the market to determine pregnancy based on the Human Chorionic Gonadotropin (HCG) hormone detection in urine samples, these preliminary results indicate an alternative method known as Raman spectroscopy. On the other hand, the results could also suggest the possibility of carrying out other gynecological or fetal-related studies in women using these Raman techniques., Competing Interests: Declarations Conflicts of Interest There are no conflicts to declare. Ethical Statement Hereby, the authors consciously assure that: - The research was carried out in accordance with a named standard. - The paper reflects the authors own research and analysis in a truthful and complete manner. - This material is the authors own original work, which has not been previously published elsewhere. - The paper is not currently being considered for publication elsewhere. - The paper properly credits the meaningful contributions of authors. - The results are appropriately placed in the context of prior and existing research. - All sources used are properly disclosed (correct citation). - Authors have been personally and actively involved in substantial work leading to the paper. Informed Consent Written consent letters were obtained from the patients., (© 2024. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.)

Cellular senescence occurs through the accumulation of many kinds of stresses. Senescent cells in tissues also cause various age-related disorders. Therefore, detecting them without labeling is beneficial for medical research and developing diagnostic methods. However, existing biomarkers have limitations of requiring fixation and labeling, or their molecular backgrounds are uncertain. Coherent anti-Stokes Raman scattering (CARS) spectroscopic imaging is a novel option because it can assess and visualize molecular structures based on their molecular fingerprint. Here, we present a new label-free method to visualize cellular senescence using CARS imaging in nucleoli. We found the peak of the nucleolar amide I band shifted to a higher wavenumber in binuclear senescent cells, which reflects changes in the protein secondary structure from predominant α-helices to β-sheets originating from amyloid-like aggregates. Following this, we developed a procedure that can visualize the senescent cells by providing the ratios and subtractions of these two components. We also confirmed that the procedure can visualize nucleolar aggregates due to unfolded/misfolded proteins produced by proteasome inhibition. Finally, we found that this method can help visualize the nucleolar defects in naïve cells even before binucleation. Thus, our method is beneficial to evaluate ongoing cellular senescence through label-free imaging of nucleolar defects., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Efficient enrichment and accurate diagnosis of cancer cells from biological samples can guide effective treatment strategies. However, the accessibility and accuracy of rapid identification of tumor cells have been hampered due to the overlap of white blood cells (WBCs) and cancer cells in size. Therefore, a diagnosis system for the identification of tumor cells using reliable surface-enhanced Raman spectroscopy (SERS) bioprobes assisted with high-efficiency microfluidic chips for rapid enrichment of cancer cells was developed. According to this, a homogeneous flower-like Cu 2 O@Ag composite with high SERS performance was constructed. It showed a favorable spectral stability of 5.81% and can detect trace alizarin red (10 -9 mol L -1 ). Finite-difference time-domain (FDTD) simulation of Cu 2 O, Ag and Cu 2 O@Ag, decreased the fluorescence lifetime of methylene blue after adsorption on Cu 2 O@Ag, and surface defects of Cu 2 O observed using a spherical aberration-corrected transmission electron microscope (AC-TEM) demonstrated that the combined effects of electromagnetic enhancement and promoted charge transfer endowed the Cu 2 O@Ag with good SERS activity. In addition, the modulation of the absorption properties of flower-like Cu 2 O@Ag composites significantly improved electromagnetic enhancement and charge transfer effects at 532 nm, providing a reliable basis for the label-free SERS detection. After the cancer cells in blood were separated by a spiral inertial microfluidic chip (purity >80%), machine learning-assisted linear discriminant analysis (LDA) successfully distinguished three types of cancer cells and WBCs with high accuracy (>90%). In conclusion, this study provides a profound reference for the rational design of SERS probes and the efficient diagnosis of malignant tumors.

The agricultural sector plays a pivotal role in driving the economy of many developing countries. Any dent in this economical structure may have a severe impact on a country's population. With rising climate change and increasing pollution, the agricultural sector is experiencing significant damage. Over time this cumulative damage will affect the integrity of food crops and create food security issues around the world. Therefore, an early warning system is needed to detect possible stress on food crops. Here we present a review of the recent developments in nanomaterial-based Surface Enhanced Raman Spectroscopy (SERS) substrates which could be utilized to monitor agricultural crop responses to natural and anthropogenic stress. Initially, our review delves into diverse and cost-effective strategies for fabricating SERS substrates, emphasizing their intelligent utilization across various agricultural scenarios. In the second phase of our review, we spotlight the specific application of SERS in addressing critical food security issues. By detecting nutrients, hormones, and effector molecules in plants, SERS provides valuable insights into plant health. Furthermore, our exploration extends to the detection of contaminants, chemicals, and foodborne pathogens within plants, showcasing the versatility of SERS in ensuring food safety. The cumulative knowledge derived from these discussions illustrates the transformative potential of SERS in bolstering the agricultural economy. By enhancing precision in nutrient management, monitoring plant health, and enabling rapid detection of harmful substances, SERS emerges as a pivotal tool in promoting sustainable and secure agricultural practices. Its integration into agricultural processes not only augments productivity but also establishes a robust defence against potential threats to crop yield and food quality. As SERS continues to evolve, its role in shaping the future of agriculture becomes increasingly pronounced, promising a paradigm shift in how we approach and address challenges in food production and safety., Competing Interests: Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Identifying the species and origin of adhesives in criminal investigations aids in narrowing inquiry scope and supporting case detection. This study introduces two advanced combined analytical techniques for distinguishing adhesive species, including attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with Raman spectroscopy, and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) together with multivariate statistical analysis. ATR-FTIR categorized seven adhesives into three groups based on the base materials, with further differentiation achieved via Raman spectra. Analysis of volatile components identified 79 volatile organic compounds (VOCs), with esters being the most concentrated. The fingerprint profile clearly illustrated the characteristic fingerprint sequence and unique marker compounds of each adhesive, effectively enabling their differentiation. Multivariate statistical analysis methods, including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), heatmap, and hierarchical cluster analysis (HCA), were utilized to visually interpret the classification of adhesives. This integrated analytical approach provides a comprehensive analysis of adhesive compositions, facilitating the diversification and precision of adhesive species identification, and broadening the scope for detecting and analyzing trace evidence in forensic science., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Sensitive and accurate detection of thiamethoxam in tea is significant to ensuring consumer health. In this study, surface-enhanced Raman scattering (SERS) tags were prepared by using a polyphenol-mediated coating engineering strategy. This approach involved the self-assembly of tannic acid (TA) and self-polymerization of benzene-1,4-dithiol (BDT) on the surface of gold nanoparticles, resulting in the formation of Au@pBDT-TA. The SERS tags possess high Raman signals and antibody adsorption properties, avoiding the complex decoration or label steps of SERS reporters. We discovered that Au@pBDT-TA, composed of gold nanoparticles at 40 nm and a pBDT-TA thickness of 10 nm, was optimal for the development of the SERS lateral flow immunoassay (LFIA). In comparison to the gold nanoparticle-based LFIA, the SERS-LFIA demonstrated 4-fold and 720-fold enhancements in visual and quantitative limits of detection in buffer solution. The SERS-LFIA demonstrated quantitative limits of detection of 0.06 and 0.1 ng/g for black and green tea, respectively, with a broader linear range spanning over 2 orders of magnitude and a short detection time of 20 min. The proposed SERS-LFIA not only offers a sensitive and reliable method for monitoring thiamethoxam in tea but also possesses a versatile potential that can be easily adapted for the trace detection of various other targets.

The use of nanozymes has become a promising auxiliary approach to "turn on" surface-enhanced Raman scattering (SERS) signals for the label-free detection of disease markers. Nevertheless, there are still major challenges to develop bifunctional nanomaterials with both excellent enzyme-like activity and high SERS performance. To this end, a novel Z-scheme MoO 3- x /CuS heterojunction was first constructed as a powerful "two-in-one" substrate, which can not only catalyze leucomalachite green (LMG) to SERS-active malachite green (MG) but also serve as an efficient substrate to amplify the SERS signal of catalysate. Due to the strong interfacial coupling effect between the MoO 3- x and CuS nanomaterial, which promoted the separation and transport of carriers in the heterojunction, the MoO 3- x /CuS heterojunction showed higher peroxidase-like activity compared to individual components and the previously reported heterojunction nanozymes. Inspired by these results, a sandwich-type SERS immunoassay for the detection of the cerebral infarction biomarker S100 calcium-binding protein (S100B) was proposed based on the output signal of MG at 1620 cm -1 . Furthermore, introducing the antifouling material chitosan on the surface of the MoO 3- x /CuS heterojunction can effectively resist nonspecific protein adsorption and significantly improve the detection accuracy of the immunoassay. Therefore, the SERS immunoassay based on the MoO 3- x /CuS heterojunction realized highly sensitive and selective detection of S100B in the concentration range of 0.001 to 100 ng/mL, with a low limit of detection of 0.47 pg/mL. The developed method has been successfully used for the accurate detection of S100B in clinical serum. The results showed that the level of S100B in the serum of cerebral infarction patients can be distinguished from those of healthy individuals and intracranial tumor patient controls. In addition, the acquired values of S100B in the serum of cerebral infarction patients based this strategy were well consistent with the results of electrochemiluminescence (ECL) detection with a relative error of less than ±7.3. It is expected that this work may open up a paradigm for improving detection sensitivity and accuracy for the early diagnosis and treatment monitoring of cerebral infarction in the clinic.

Conventional solid-based SERS substrates often face challenges with inconsistent sample distribution, while liquid-based SERS substrates are prone to aggregation and precipitation, resulting in irreproducible signals in both cases. In this study, we tackled this dilemma by designing and synthesizing raspberry-like plasmonic nanoaggregates that exhibit a high density of hotspots and are colloidally stable at the same time. In particular, the nanoaggregates consist of a core made of functionalized polystyrene (PS) microspheres, which act as a template for rapid self-assembly of Au@Ag core-shell nanoparticles to form raspberry-like hierarchical nanoaggregates within 5 min of mixing. The optimized nanoaggregates can be used as reproducible and stable SERS substrates for a range of wastewater pollutants (e.g., rhodamine 6G (R6G) and malachite green (MG)) and nucleobases (e.g., adenine and uracil), with the detection limits as low as 1 × 10 -10 , 1 × 10 -16 , 3 × 10 -8 , and 3 × 10 -7 M, respectively. Additionally, the trace detection of adenine in clinical urine samples has been successfully demonstrated. Our modular assembly approach opens up new possibilities in SERS substrate design and advanced trace-chemical detection technologies.

Confocal Raman Spectroscopy is recognised as a potent tool for molecular characterisation of biological specimens. There is a growing demand for In Vitro Permeation Tests (IVPT) in the pharmaceutical and cosmetic areas, increasingly conducted using Reconstructed Human Epidermis (RHE) skin models. In this study, chemical fixation of RHE in 10 % Neutral Buffered Formalin for 24 h has been examined for storing RHE samples at 4 °C for up to 21 days. Confocal Raman Spectroscopy (CRS), combined with Principal Components Analysis, revealed the molecular-level effects of fixation, notably in protein and lipid conformation within the stratum corneum and viable epidermis. IVPT by means of high-performance liquid chromatography, using caffeine as a model compound, showed minimal impact of formalin fixation on the cumulative amount, flux, and permeability coefficient after 12 h. While the biochemical architecture is altered, the function of the model as a barrier to maintain rate-limiting diffusion of active molecules within skin layers remains intact. This study opens avenues for enhanced flexibility and utility in skin model research, promising insights into mitigating the limited shelf life of RHE models by preserving performance in fixed samples for up to 21 days., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The SERS spectra of six bacterial biomarkers, 2,3-DHBA, 2,5-DHBA, Pyocyanin, lipoteichoic acid (LTA), Enterobactin, and β-carotene, of various concentrations, were obtained from silver nanorod array substrates, and the spectral peaks and the corresponding vibrational modes were identified to classify different spectra. The spectral variations in three different concentration regions due to various reasons have imposed a challenge to use classic calibration curve methods to quantify the concentration of biomarkers. Depending on baseline removal strategy, i.e., local or global baseline removal, the calibration curve differed significantly. With the aid of convolutional neural network (CNN), a two-step process was established to classify and quantify biomarker solutions based on SERS spectra: using a specific CNN model, a remarkable differentiation and classification accuracy of 99.99 % for all six biomarkers regardless of the concentration can be achieved. After classification, six regression CNN models were established to predict the concentration of biomarkers, with coefficient of determination R 2  > 0.97 and mean absolute error (MAE) < 0.27. The feature of important calculations indicates the high classification and quantification accuracies were due to the intrinsic spectral features in SERS spectra. This study showcases the synergistic potential of SERS and advanced machine learning algorithms and holds significant promise for bacterial infection diagnostics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In this study, Gordonia sp. HS126-4N was employed for dibenzothiophene (DBT) biodesulfurization, tracked over 9 days using SERS. During the initial lag phase, no significant spectral changes were observed, but after 48 h, elevated metabolic activity was evident. At 72 h, maximal bacterial population correlated with peak spectrum variance, followed by stable spectral patterns. Despite 2-hydroxybiphenyl (2-HBP) induced enzyme suppression, DBT biodesulfurization persisted. PCA and PLS-DA analysis of the SERS spectra revealed distinctive features linked to both bacteria and DBT, showcasing successful desulfurization and bacterial growth stimulation. PLS-DA achieved a specificity of 95.5 %, sensitivity of 94.3 %, and AUC of 74 %, indicating excellent classification of bacteria exposed to DBT. SERS effectively tracked DBT biodesulfurization and bacterial metabolic changes, offering insights into biodesulfurization mechanisms and bacterial development phases. This study highlights SERS' utility in biodesulfurization research, including its use in promising advancements in the field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu 2 O template etching method as SERS substrates, which enhanced the plasmonic effect through sharp edges and hollow nanostructures. Afterwards, five chemometric algorithms, like partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), competitive adaptive reweighted sampling-PLS (CARS-PLS), random frog-PLS (RF-PLS), and convolutional neural network (CNN) were applied for TTC and CAP. RF-PLS performed optimally for TTC and CAP (Rc = 0.9686, Rp = 0.9648, RPD = 3.79 for TTC and Rc = 0.9893, Rp = 0.9878, RPD = 5.88 for CAP). Furthermore, the detection limit of 0.0001 µg/mL for both TTC and CAP was obtained. Finally, satisfactory (p > 0.05) results were obtained with the standard HPLC method. Therefore, SERS combined RF-PLS could be applied for fast, nondestructive sensing of TTC and CAP in milk., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple symptoms, and its rapid screening is the research focus of surface-enhanced Raman scattering (SERS) technology. In this study, gold@silver-porous silicon (Au@Ag-PSi) composite substrates were synthesized by electrochemical etching and in-situ reduction methods, which showed excellent sensitivity and accuracy in the detection of rhodamine 6G (R6G) and serum from SLE patients. SERS technology was combined with deep learning algorithms to model serum features using selected CNN, AlexNet, and RF models. 92 % accuracy was achieved in classifying SLE patients by CNN models, and the reliability of these models in accurately identifying sera was verified by ROC curve analysis. This study highlights the great potential of Au@Ag-PSi substrate in SERS detection and introduces a novel deep learning approach for SERS for accurate screening of SLE. The proposed method and composite substrate provide significant value for rapid, accurate, and noninvasive SLE screening and provide insights into SERS-based diagnostic techniques., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Computer-aided vibrational spectroscopy detection technology has achieved promising results in the field of early disease diagnosis. Yet limited by factors such as the number of actual samples and the cost of spectral acquisition in clinical medicine, the data available for model training are insufficient, and the amount of data varies greatly between different diseases, which constrain the performance optimization and enhancement of the diagnostic model. In this study, vibrational spectroscopy data of three common diseases are selected as research objects, and experimental research is conducted around the class imbalance situation that exists in medical data. When dealing with the challenge of class imbalance in medical vibrational spectroscopy research, it no longer relies on some kind of independent and single method, but considers the combined effect of multiple strategies. SVM, K-Nearest Neighbor (KNN), and Decision Tree (DT) are used as baseline comparison models on Raman spectroscopy medical datasets with different imbalance rates. The performance of the three strategies, Ensemble Learning, Feature Extraction, and Resampling, is verified on the class imbalance dataset by G-mean and AUC metrics, respectively. The results show that all the above three methods mitigate the negative impact caused by unbalanced learning. Based on this, we propose a hybrid ensemble classifier (HEC) that integrates resampling, feature extraction, and ensemble learning to verify the effectiveness of the hybrid learning strategy in solving the class imbalance problem. The G-mean and AUC values of the HEC method are 82.7 % and 83.12 % for the HBV dataset, is 2.02 % and 1.98 % higher than the optimal strategy; 83.62 % and 83.76 % for the HCV dataset, is 9.79 % and 8.47 % higher than the optimal strategy; while for the thyroid dysfunction dataset are 77.56 % and 77.85 %, is 6.92 % and 6.36 % higher than that of the optimal strategy, respectively. The experimental results show that the G-mean and AUC metrics of the HEC method are higher than those of the baseline classifier as well as the optimal combination using separate strategies. It can be seen that the HEC method can effectively counteract the unfavorable effects of imbalance learning and is expected to be applied to a wider range of imbalance scenarios., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

This work aimed to set inline Raman spectroscopy models to monitor biochemically (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, and ammonium) all upstream stages of a virus-like particle-making process. Linear (Partial least squares, PLS; Principal components regression, PCR) and nonlinear (Artificial neural networks, ANN; supported vector machine, SVM) modeling approaches were assessed. The nonlinear models, ANN and SVM, were the more suitable models with the lowest absolute errors. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01-8.83 × 10 6 cells/mL), cell viability (1.3-100.0 %), glucose (5.22-10.93 g/L), lactate (18.6-152.7 mg/L), glutamine (158-1761 mg/L), glutamate (807.6-2159.7 mg/L), and ammonium (62.8-117.8 mg/L) were 1.55 ± 1.37 × 10 6 cells/mL (ANN), 5.01 ± 4.93 % (ANN), 0.27 ± 0.22 g/L (SVM), 4.7 ± 2.6 mg/L (SVM), 51 ± 49 mg/L (ANN), 57 ± 39 mg/L (SVM) and 2.0 ± 1.8 mg/L (ANN), respectively. The errors achieved, and best-fitted models were like those for the same bioprocess using offline data and others, which utilized inline spectra for mammalian cell lines as a host., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Phosphorylated peptides are instrumental in studying protein phosphorylation events. In the present study, Raman optical activity (ROA) is employed to elucidate the structure of a dipeptide, L-alanyl-L-glutamine (L-Ala-L-Gln) and its two differently alkylated N-phosphorylated derivatives. Theoretical simulations were conducted to aid the interpretation of peptide conformation variations upon phosphorylation, and of the measured Raman and ROA spectra. Induced circularly polarized luminescence (CPL) was also recorded in solution, in the presence of a simple europium aqua ion. As the spectra are peptide specific, this type of stereochemical analysis is expected to aid identification of the phosphorylation sites also in other peptides and possibly proteins., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced Raman scattering (SERS) substrate with deep learning network for achieving rapid and quantitative detection of MG in fish. The flexible and transparent SERS substrate was prepared by depositing silver (Ag) film on the polydimethylsiloxane (PDMS) film using laser molecular beam epitaxy (LMBE) technique. The wrinkled Ag NPs@PDMS film exhibits high SERS activity, excellent reproducibility and good mechanical stability. Additionally, the fast in situ detection of MG residues onfishscales was achieved by using the wrinkled Ag NPs/PDMS film and a portable Raman spectrometer, with a minimum detectable concentration of 10 -6 M. Subsequently, a one-dimensional convolutional neural network (1D CNN) model was constructed for rapid quantification of MG concentration. The results demonstrated that the 1D CNN quantitative analysis model possessed superior predictive performance, with a coefficient of determination (R 2 ) of 0.9947 and a mean squared error (MSE) of 0.0104. The proposed POCT platform, integrating a transparent flexible SERS substrate, a portable Raman spectrometer and a 1D CNN model, provides an efficient strategy for rapid identification and quantitative analysis of MG in fish., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening techniques use specific reagents, and as viruses mutate, test accuracy decreases. Here, we present the first real-time, reagent-free, portable analysis platform for viral detection in liquid saliva, using COVID-19 as a proof-of-concept. We show that vibrational molecular spectroscopy and machine learning (ML) detect biomolecular changes consistent with the presence of viral infection. Saliva samples were collected from 470 individuals, including 65 that were infected with COVID-19 (28 from hospitalized patients and 37 from a walk-in testing clinic) and 251 that had a negative polymerase chain reaction (PCR) test. A further 154 were collected from healthy volunteers. Saliva measurements were achieved in 6 minutes or less and led to machine learning models predicting COVID-19 infection with sensitivity and specificity reaching 90%, depending on volunteer symptoms and disease severity. Machine learning models were based on linear support vector machines (SVM). This platform could be deployed to manage future pandemics using the same hardware but using a tunable machine learning model that could be rapidly updated as new viral strains emerge.

Fluorescence labeling via fluorescent proteins (FPs) or immunofluorescence has been routinely applied for microscopic imaging of specific proteins. However, due to these over-weight and oversized labels ( e.g. GFP, 238 aa, 27 kDa, ∼4 nm in size), the potential physiological malfunctions of the target proteins are largely underestimated in living cells. Herein, for living cells, we report a small and minimally-invasive Raman reporter (about 2 aa and <1 kDa), which can be site-specifically introduced into proteins by genetic codon expansion. After a single unnatural amino acid (UAA) is precisely incorporated into the target protein, the strained alkyne can rapidly undergo copper-free Diels-Alder cycloaddition reactions with the tetrazine-functionalized Raman reporter, which features a fine vibrational spectrum in contrast to fluorescence. In our experimental results, the UAA-based Raman tag was successfully incorporated into vimentin, histone 3.3 and huntingtin (Htt74Q) proteins in living HeLa cells and further utilized for stimulated Raman imaging. The site-specific bioorthogonal fusion of small Raman tags with intracellular proteins will pave the way for minimally-invasive protein labeling and multi-color imaging in living cells.

Acute lymphoblastic leukaemia (ALL) is a complex disease in pediatric oncology, necessitating accurate diagnostic strategies for effective treatment planning. The ability to differentiate between B-cell ALL (B-ALL) and T-cell ALL (T-ALL) is crucial for targeted interventions. However, current diagnostic methods are time-consuming and require rapid, dependable tests. This study explores the potential of label-free Raman imaging coupled with chemometrics for rapid blast phenotyping of B-ALL and T-ALL. Our findings demonstrate the efficacy of Raman spectroscopy in sensitively and specifically screening and classifying ALL, as well as its rapidity and reliability. The obtained molecular information allows for label-free and precise leukaemia diagnosis at the single-cell level, surpassing the capabilities of traditional diagnostic techniques. Raman spectra of cancer cells reveal distinctive molecular signatures, specifically heightened protein and nucleic acid content, revealing molecular signatures unique to leukemic phenotypes. Based on that, they could be distinguished from each other and their normal B and T lymphocyte counterparts. This research underscores the analytical power of Raman spectroscopy, positioning it as a valuable tool for identifying and classifying pediatric ALL subtypes. The potential translational applications in clinical practice offer a promising avenue for an expedited and accurate leukaemia diagnosis, paving the way for more targeted and personalised therapeutic approaches.

Cerumen, commonly known as earwax, is a complex mixture composed of secretions from ceruminous glands. These secretions are heterogeneous mixtures mainly composed of lipids and proteins. Despite its prevalence, the potential diagnostic value of cerumen remains largely unexplored. Here, we present an in-depth analysis of cerumen utilizing well-known vibrational approaches such as conventional Raman spectroscopy or surface-enhanced Raman spectroscopy (SERS) together with advanced vibrational spectroscopy techniques such as coherent Raman scattering (CRS), i.e. broadband coherent anti-Stokes Raman scattering (CARS) or stimulated Raman scattering (SRS), as well as optical photothermal infrared (OPTIR) spectroscopy. Through the integration of these vibrational spectroscopic methods, lipids and proteins can be identified as the main components of cerumen; however, they contribute to the final spectral information to various extents depending on the vibrational detection scheme applied. The inherently weak Raman signal could be enhanced by linear (SERS) and non-linear (CRS) processes, resulting in efficient acquisition of fingerprint information and allowing for the detection of marker modes, which cannot be addressed by conventional Raman spectroscopy. OPTIR spectroscopy provides complementary information to Raman spectroscopy, however, without the contribution of a fluorescence background. Our findings underscore the utility of these cutting-edge techniques in unveiling the intricate molecular landscape of cerumen, paving the way for novel point-of-care diagnostic methodologies and therapeutic interventions.