A breakthrough in phytochemical profiling: ultra-sensitive surface-enhanced Raman spectroscopy platform for detecting bioactive components in medicinal and edible plants.

A perennial challenge in harnessing the rich biological activity of medicinal and edible plants is the accurate identification and sensitive detection of their active compounds. In this study, an innovative, ultra-sensitive detection platform for plant chemical profiling is created using surface-enhanced Raman spectroscopy (SERS) technology. The platform uses silver nanoparticles as the enhancing substrate, excess sodium borohydride prevents substrate oxidation, and methanol enables the tested molecules to be better adsorbed onto the silver nanoparticles. Subsequently, nanoparticle aggregation to form stable "hot spots" is induced by Ca2+, and the Raman signal of the target molecule is strongly enhanced. At the same time, deuterated methanol was used as the internal standard for quantitative determination. The method has excellent reproducibility, RSD ≤ 1.79%, and the enhancement factor of this method for the detection of active ingredients in the medicinal plant Coptis chinensis was 1.24 × 109, with detection limits as low as 3 fM. The platform successfully compared the alkaloid distribution in different parts of Coptis chinensis: root > leaf > stem, and the difference in content between different batches of Coptis chinensis decoction was successfully evaluated. The analytical technology adopted by the platform can speed up the determination of Coptis chinensis and reduce the cost of analysis, not only making better use of these valuable resources but also promoting development and innovation in the food and pharmaceutical industries. This study provides a new method for the development, evaluation, and comprehensive utilization of both medicinal and edible plants. It is expected that this method will be extended to the modern rapid detection of other medicinal and edible plants and will provide technical support for the vigorous development of the medicinal and edible plants industry.

A method for accurate identification of Uyghur medicinal components based on Raman spectroscopy and multi-label deep learning.

Uyghur medicine is one of the four major ethnic medicines in China and is a component of traditional Chinese medicine. The intrinsic quality of Uyghur medicinal materials will directly affect the clinical efficacy of Uyghur medicinal preparations. However, in recent years, problems such as adulteration of Uyghur medicinal materials and foreign bodies with the same name still exist, so it is necessary to strengthen the quality control of Uyghur medicines to guarantee Uyghur medicinal efficacy. Identifying the components of Uyghur medicines can clarify the types of medicinal materials used, is a crucial step to realizing the quality control of Uyghur medicines, and is also an important step in screening the effective components of Uyghur medicines. Currently, the method of identifying the components of Uyghur medicines relies on manual detection, which has the problems of high toxicity of the unfolding agent, poor stability, high cost, low efficiency, etc. Therefore, this paper proposes a method based on Raman spectroscopy and multi-label deep learning model to construct a model Mix2Com for accurate identification of Uyghur medicine components. The experiments use computer-simulated mixtures as the dataset, introduce the Long Short-Term Memory Model (LSTM) and Attention mechanism to encode the Raman spectral data, use multiple parallel networks for decoding, and ultimately realize the macro parallel prediction of medicine components. The results show that the model is trained to achieve 90.76% accuracy, 99.41% precision, 95.42% recall value and 97.37% F1 score. Compared to the traditional XGBoost model, the method proposed in the experiment improves the accuracy by 49% and the recall value by 18%; compared with the DeepRaman model, the accuracy is improved by 9% and the recall value is improved by 14%. The method proposed in this paper provides a new solution for the accurate identification of Uyghur medicinal components. It helps to improve the quality standard of Uyghur medicinal materials, advance the research on screening of effective chemical components of Uyghur medicines and their action mechanisms, and then promote the modernization and development of Uyghur medicine.

Synergistic photoinduced charge transfer resonance from porous ZIF-67 decorated violet phosphorus array for SERS immunoassay of SARS-CoV-2 spike protein.

As a rapid, highly sensitive, and user-friendly technique, surface-enhanced Raman scattering (SERS) has an extraordinary appeal to home self-test of COVID-19 during the post pandemic era. However, most of the existing SERS substrates have been still criticized in stability, repeatability, and sample enrichment. To address these obstacles, a novel non-metallic SERS substrate with porous surfaces and array geometry was developed by in-situ growing ZIF-67 particles on two-dimensional violet phosphorus (VP) matrix. Chemical enhancement was prominently promoted by the synergistic photoinduced charge transfer resonance in the hybrid band structure of the ZIF-67@VP substrate, facilitating a noble metal-similar enhancement factor of 6.11 × 107. The biocompatible ZIF-67@VP porous array with attractive enhancement capability and high anchoring efficiency was further utilized to monitoring SARS-CoV-2 spike protein in practical saliva samples based on a sandwich immunostructure, achieving a limit of detection of 1.7 ng/mL assisted by black phosphorus nanosheets. This nonmetallic immunoassay strategy with exceptional sensitivity and specificity is predicted to extend the utilization of SERS obstacle in daily infectious disease screening.

Raman spectroscopy study of 7,8-dihydrofolate inhibition on the Wuhan strain SARS-CoV-2 binding to human ACE2 receptor.

Emerging evidence suggests that elevated levels of folic acid in the bloodstream may confer protection against Wuhan-SARS-CoV-2 infection and mitigate its associated symptoms. Notably, two comprehensive studies of COVID-19 patients in Israel and UK uncovered a remarkable trend, wherein individuals with heightened folic acid levels exhibited only mild symptoms and necessitated no ventilatory support. In parallel, research has underscored the potential connection between decreased folic acid levels and the severity of Covid-19 among hospitalized patients. Yet, the underlying mechanisms governing this intriguing inhibition remain elusive. In a quest to elucidate these mechanisms, we conducted a molecular dynamics simulation approach followed by a Raman spectroscopy study to delve into the intricate interplay between the folic acid metabolite, 7,8-dihydrofolate (DHF), and the angiotensin-converting enzyme ACE2 receptor, coupled with its interaction with the receptor-binding domain (RBD) of the Wuhan strain of SARS-CoV-2. Through a meticulous exploration, we scrutinized the transformation of the ACE2 + RBD complex, allowing these reactants to form bonds. This was juxtaposed with a similar investigation where ACE2 was initially permitted to react with DHF, followed by the exposure of the ACE2 + DHF complex to RBD. We find that DHF, when bonded to ACE2, functions as a physical barrier, effectively inhibiting the binding of the Wuhan strain RBD. This physicochemical process offers a cogent explanation for the observed inhibition of host cell infection in subjects receiving supplementary folic acid doses, as epidemiologically substantiated in multiple studies. This study not only sheds light on a potential avenue for mitigating SARS-CoV-2 infection but also underscores the crucial role of folic acid metabolites in host-virus interactions. This research paves the way for novel therapeutic strategies in the battle against COVID-19 and reinforces the significance of investigating the molecular mechanisms underlying the protective effects of folic acid in the context of viral infections.

Sea hedgehog-inspired surface-enhanced Raman scattering biosensor probe for ultrasensitive determination of Staphylococcus aureus in food supplements.

Staphylococcus aureus contamination in food supplements poses substantial challenges to public health and large-scale production but the sensitive detection in a timely manner remains a bottleneck. Drawing inspiration from the sea hedgehog, gold nanostars (AuNSs) were leveraged to design an ultrasensitive surface-enhanced Raman scattering (SERS) biosensor for the determination of Staphylococcus aureus in food supplements. Besides the surface enhancement furnished by the AuNSs, Raman reporter molecules and specific aptamers sequentially self-assembled onto these AuNSs to construct the "three-in-one" SERS biosensor probe for label-based quantitation of Staphylococcus aureus. Following incubation with contaminated health product samples, the gold nanostars@Raman reporter-aptamer specifically recognize and assemble around Staphylococcus aureus cells, forming a distinctive sea hedgehog structure. This unique configuration results in an amplified Raman signal at 1338 cm-1 and an enhancement factor of up to 6.71 × 107. The entire quantitative detection process can be completed within 30 min, boasting an exceptional limit of detection as low as 1.0 CFU mL-1. The method exhibits a broad working range for the determination of Staphylococcus aureus, with concentrations spanning 2.15 CFU mL-1 to 2.15 × 105 CFU mL-1. Furthermore, it demonstrates outstanding precision, with relative standard deviation values consistently below 5.0%. As a showcase to validate the practicality of the SERS method, we conducted tests on determining Staphylococcus aureus in a herbal food supplement, i.e., Ginkgo Biloba extract (GBE); the results align closely with those obtained through the conventional lysogeny broth agar plate method, pointing to the potential applicability in real-world scenarios.

Quantitative analysis of the quality constituents of Lonicera japonica Thunberg based on Raman spectroscopy.

Quantitative analysis of the quality constituents of Lonicera japonica (Jinyinhua [JYH]) using a feasible method provides important information on its evaluation and applications. Limitations of sample pretreatment, experimental site, and analysis time should be considered when identifying new methods. In response to these considerations, Raman spectroscopy combined with deep learning was used to establish a quantitative analysis model to determine the quality of JYH. Chlorogenic acid and total flavonoids were identified as analysis targets via network pharmacology. High performance liquid chromatograph and ultraviolet spectroscopy were used to construct standard curves for quantitative analysis. Raman spectra of JYH extracts (1200) were collected. Subsequently, models were built using partial least squares regression, Support Vector Machine, Back Propagation Neural Network, and One-dimensional Convolutional Neural Network (1D-CNN). Among these, the 1D-CNN model showed superior prediction capability and had higher accuracy (R2 = 0.971), and lower root mean square error, indicating its suitability for rapid quantitative analysis.

In Operando Analysis of Milk-Based Oral Formulations during Digestion Using Synchrotron Small-Angle X-ray Scattering Coupled to Low-Frequency Raman Spectroscopy.

A low-frequency Raman (LFR) probe was coupled to an in-line small-angle X-ray scattering (SAXS) beamline to test the capabilities of a combinatory approach for the determination of lipid and drug behavior during the enzymatic lipolysis of milk-based oral formulations. Cinnarizine was used as the model drug, and its solubilization dynamics as well as its potential impact on the supramolecular structures formed by the digestion products of bovine milk were evaluated from the perspective of both techniques. The SAXS data were superior in distinguishing various liquid crystalline assemblies formed during the digestion process, with LFR providing complementary information regarding the formation of calcium soaps. On the other hand, studying changes in the LFR domain allowed the differentiation of drug solubilization and precipitation; processes that were less clear from the X-ray scattering data. Given the relative simplicity of the combined experimental setup, these results highlight the advantages that the combination of the two techniques can provide for understanding and developing new lipid-based formulations and will help to translate the results obtained at synchrotron facilities to routine analysis procedures in laboratory/industry-based environments.

A highly effective SERS platform formed by the fabrication of Ag@ZIF-8@Au nanoparticles for rapid detection of acetamiprid in environment.

The unreasonable spraying and random migration of acetamiprid may cause pollution of crops, soil and water resources in the environment, resulting in threatening ecosystem and human health. However, the monitoring of acetamiprid using mass spectrum in the environment encounters challenges due to high-cost instruments and complex processing time. Herein, we fabricated a rapid and reliable SERS method based on Ag@ZIF-8@Au platforms for tracing acetamiprid residues in the environment. In this method, a MOF material named ZIF-8 is coated with silver nanoparticles and distributed internally between AgNPs and AuNPs to enhance Raman signal, which can enrich pesticide molecules into the hotspots area provided by noble material and helps avoid the oxidation of silver nanoparticles. High sensitivity (LOD of 9.027 × 10-10 M for acetamiprid, and SERS enhancement factor of 4.3 × 107), excellent reproducibility (6.496% or 7.198% RSD for 30 random points) and superior stability (3.127% RSD for 6 weeks) were achieved using the proposed method. Acetamiprid with concentrations from 10-4 to 10-9 M were successfully detected by SERS method. Furthermore, the linear detection models of acetamiprid in different environment matrices (lake water, tea leaves, tea garden soil, oranges and oranges orchard soil) were established and all the correlation coefficient (R2) were higher than or equal to 95%, indicating the excellent adaptability of Ag@ZIF-8@Au platform in environment. The randomly spiked concentrations of acetamiprid were also tested with good recovery values and low relative error values, further confirming the reliability of the detection method.

Surface-enhanced Raman spectroscopy as a powerful method for the analysis of Chinese herbal medicines.

Chinese herbal medicines (CHMs) derived from nature have received increasing attention and become more popular. Due to their diverse production processes, complex ingredients, and different storage conditions, it is highly desirable to develop simple, rapid, efficient and trace detection methods to ensure the drug quality. Surface-enhanced Raman spectroscopy has the advantages of being time-saving, non-destructive, usable in aqueous environments, and highly compatible with various biomolecular samples, providing a promising analytical method for CHM. In this review, we outline the major advances in the application of SERS to the identification of raw materials, detection of bioactive constituents, characterization of adulterants, and detection of contaminants. This clearly shows that SERS has strong potential in the quality control of CHM, which greatly promotes the modernization of CHM.

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.

Au-Ag OHCs-based SERS sensor coupled with deep learning CNN algorithm to quantify thiram and pymetrozine in tea.

Pesticide residue detection in food has become increasingly important. Herein, surface-enhanced Raman scattering (SERS) coupled with an intelligent algorithm was developed for the rapid and sensitive detection of pesticide residues in tea. By employing octahedral Cu2O templates, Au-Ag octahedral hollow cages (Au-Ag OHCs) were developed, which improved the surface plasma effect via rough edges and hollow inner structure, amplifying the Raman signals of pesticide molecules. Afterward, convolutional neural network (CNN), partial least squares (PLS), and extreme learning machine (ELM) algorithms were applied for the quantitative prediction of thiram and pymetrozine. CNN algorithms performed optimally for thiram and pymetrozine, with correlation values of 0.995 and 0.977 and detection limits (LOD) of 0.286 and 29 ppb, respectively. Accordingly, no significant difference (P greater than 0.05) was observed between the developed approach and HPLC in detecting tea samples. Hence, the proposed Au-Ag OHCs-based SERS technique could be utilized for quantifying thiram and pymetrozine in tea.

Determination of Dicofol in Tea Using Surface-Enhanced Raman Spectroscopy Coupled Chemometrics.

Dicofol is a highly toxic residual pesticide in tea, which seriously endangers human health. A method for detecting dicofol in tea by combining stoichiometry with surface-enhanced Raman spectroscopy (SERS) technology was proposed in this study. AuNPs were prepared, and silver shells were grown on the surface of AuNPs to obtain core-shell Au@AgNPs. Then, the core-shell Au@AgNPs were attached to the surface of a PDMS membrane by physical deposition to obtain a Au@AgNPs/PDMS substrate. The limit of detection (LOD) of this substrate for 4-ATP is as low as 0.28 × 10-11 mol/L, and the LOD of dicofol in tea is 0.32 ng/kg, showing high sensitivity. By comparing the modeling effects of preprocessing and variable selection algorithms, it is concluded that the modeling effect of Savitzky-Golay combined with competitive adaptive reweighted sampling-partial least squares regression is the best (Rp = 0.9964, RPD = 10.6145). SERS technology combined with stoichiometry is expected to rapidly detect dicofol in tea without labels.

Single versus Double Coffee-Ring Effect Patterns in Thin-Layer Chromatography Coupled with Surface-Enhanced Raman Spectroscopic Analysis of Anti-Diabetic Drugs Adulterated in Herbal Products.

In thin-layer chromatography coupled with surface-enhanced Raman spectroscopy (TLC-SERS), the coffee ring effect (CRE) describes the formation of a ring-shape spot (blank in the middle and darker on the edge) caused by the aggregation of silver nanoparticles (Ag NPs), alone (single CRE) or with the analytes (double CRE). In this work, the SCRE and DCRE were investigated in two anti-diabetic drugs, hydrophobic glibenclamide (GLB) and more hydrophilic metformin (MET). The SCRE occurred in GLB analysis, as opposed to the DCRE that occurred in MET. It was proven that for optimization of the TLC-SERS analytical procedure, it is necessary to distinguish the CRE patterns of analytes. Additionally, MET and GLB were analyzed with the developed TLC-SERS method and confirmed by another validated method using high-performance liquid chromatography. Four herbal products collected on the market were found to be adulterated with GLB or/and MET; among those, one product was adulterated with both MET and GLB, and two products were adulterated with GLB at a higher concentration than the usual GLB prescription dose. The TLC-SERS method provided a useful tool for the simultaneous detection of adulterated anti-diabetic herbal products, and the comparison of the SCRE and DCRE provided more evidence to predict CRE patterns in TLC-SERS.

Ratiometric SERS aptasensing for simultaneous quantitative detection of histamine and tyramine in fishes.

Herein, a SiO2@Ag NPs core/shell nanoparticles were synthesized to fabricate a surface-enhanced Raman spectroscopy (SERS) sensor for the simultaneous determination of histamine (HIS) and tyramine (TYR) based on specific aptamer recognition and ratiometric strategy. SiO2@Ag NPs with 4-thiosaminophenol (4-ATP) and Nile blue A (NBA) molecules were used as an internal standard (IS) and labeled with aptamers corresponding to HIS and TYR, respectively. Raman reporter molecules ROX and Cy5 labeled complementary DNA (cDNA) were then hybridized with aptamers to form rigid double-stranded DNA. After the HIS and TYR were captured by their aptamers, resulting in the dissociation of cDNA and separated from the SERS substrate. Therefore, the SERS signal intensity at 1503 cm-1 of ROX and 1358 cm-1 of Cy5 tagged on the terminal of cDNA decreased with the concentration of HIS and TYR increasing, while the SERS signal intensity at 1079 cm-1 of 4-APT and 592 cm-1 of NBA on the substrate remain stable. Thus, the concentrations of HIS and TYR can be determined by the I1503/I1079 and I1358/I592 values, respectively. This sensing strategy achieves a lower detection limit of 0.2 ng/mL for HIS and 0.05 ng/mL for TYR, respectively, demonstrating promising applications in sensitive detection of BAs in animal-derived foodstuff.

Ultraviolet Resonance Raman Spectra of Serum Albumins.

The ultraviolet resonance Raman (UVRR) spectra of the two proteins bovine serum albumin (BSA) and human serum albumin (HSA) in an aqueous solution are compared with the aim to distinguish between them based on their very similar amino acid composition and structure and to obtain signals from tryptophan that has only very few residues. Comparison of the protein spectra with solutions of tryptophan, tyrosine, and phenylalanine in comparative ratios as in the two proteins shows that at an excitation wavelength of 220 nm, the spectra are dominated by the strong resonant contribution from these three amino acids. While the strong enhancement of two and one single tryptophan residue in BSA and HSA, respectively, results in pronounced bands assigned to fundamental vibrations of tryptophan, its weaker overtones and combination bands do not play a major role in the spectral range above 1800 cm-1. There, the protein spectra clearly reveal the signals of overtones and combination bands of phenylalanine and tyrosine. Assignments of spectral features in the range of Raman shifts from 3800 to 5100 cm-1 to combinations comprising fundamentals and overtones of tyrosine were supported by spectra of amino acid mixtures that contain deuterated tyrosine. The information in the high-frequency region of the UVRR spectra could provide information that is complementary to near-infrared absorption spectroscopy of the proteins.

Rapid detection of nicotine and benzoic acid in e-liquids with surface-enhanced Raman scattering and artificial intelligence-assisted spectrum interpretation.

Electronic cigarettes have rapidly gained acceptance recently. Nicotine-containing electronic cigarette liquids (e-liquids) are prohibited in some countries, but are permitted and simply available online in others. A rapid detection method is therefore required for on-site inspection or screening of a large amount of samples. Our previous study demonstrated a surface-enhanced Raman scattering (SERS)-based approach to identify nicotine-containing e-liquids; without any pre-treatment, e-liquid can be directly tested on our solid-phase SERS substrates, made of silver nanoparticle arrays embedded in anodic aluminium oxide nanochannels (Ag/AAO). However, this approach required manual determination of spectral signatures and negative samples should be validated in the second round detection. Here, after examining 406 commercial e-liquids, we refined this approach by developing artificial intelligence (AI)-assisted spectrum interpretations. We also found that nicotine and benzoic acid can be simultaneously detected in our platform. This increased test sensitivity because benzoic acid is usually used in nicotine salts. Around 64% of nicotine-positive samples in this study showed both signatures. Using either cutoffs of nicotine and benzoic acid peak intensities or a machine learning model based on the CatBoost algorithm, over 90% of tested samples can be correctly discriminated with only one round of SERS measurement. False negative and false positive rates were 2.5-4.4% and 4.4-8.9%, respectively, depending on the interpretation method and thresholds applied. The new approach takes only 1 microliter of sample and can be performed in 1-2 min, suitable for on-site inspection with portable Raman detectors. It could also be a complementary platform to reduce samples that need to be analyzed in the central labs and has the potential to identify other prohibited additives.

Self-limiting stoichiometry in SnSe thin films.

Unique functionalities can arise when 2D materials are scaled down near the monolayer limit. However, in 2D materials with strong van der Waals bonds between layers, such as SnSe, maintaining stoichiometry while limiting vertical growth is difficult. Here, we describe how self-limiting stoichiometry can promote the growth of SnSe thin films deposited by molecular beam epitaxy. The Pnma phase of SnSe was stabilized over a broad range of Sn : Se flux ratios from 1 : 1 to 1 : 5. Changing the flux ratio does not affect the film stoichiometry, but influences the predominant crystallographic orientation. ReaxFF molecular dynamics (MD) simulation demonstrates that, while a mixture of Sn/Se stoichiometries forms initially, SnSe stabilizes as the cluster size evolves. The MD results further show that the excess selenium coalesces into Se clusters that weakly interact with the surface of the SnSe particles, leading to the limited stoichiometric change. Raman spectroscopy corroborates this model showing the initial formation of SnSe2 transitioning into SnSe as experimental film growth progresses. Transmission electron microscopy measurements taken on films deposited with growth rates above 0.25 Å s-1 show a thin layer of SnSe2 that disrupts the crystallographic orientation of the SnSe films. Therefore, using the conditions for self-limiting SnSe growth while avoiding the formation of SnSe2 was found to increase the lateral scale of the SnSe layers. Overall, self-limiting stoichiometry provides a promising avenue for maintaining growth of large lateral-scale SnSe for device fabrication.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine.

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Rapid identification and detection of aristolochic acids in the herbal extracts by Raman spectroscopy.

Herbs containing aristolochic acids (AAs) have already been proven to be highly carcinogenic and nephrotoxic. In this study, a novel surface-enhanced Raman scattering (SERS) identification method was developed. Ag-APS nanoparticles with a particle size of 3.53 ± 0.92 nm were produced by combining silver nitrate and 3-aminopropylsilatrane. The reaction between the carboxylic acid group of aristolochic acid I (AAI) and amine group of Ag-APS NPs was used to form amide bonds, and thus, concentrate AAI, rendering it easy to detect via SERS and amplified to obtain the best SERS enhancement effect. Detection limit was calculated to be approximately 40 nM. Using the SERS method, AAI was successfully detected in the samples of four Chinese herbal medicines containing AAI. Therefore, this method has a high potential to be applied in the future development of AAI analysis and rapid qualitative and quantitative analysis of AAI in dietary supplements and edible herbs.

A facile heat-treatment solid phase microextraction method for SERS detection of isocarbophos in tea using a hand-held Raman spectrometer.

A facile sensor system based on heat-treatment solid phase microextraction and Surface-Enhanced Raman Scattering (HT-SPME-SERS) was established for in-situ detection of isocarbophos in complex tea matrix. Starting from the action optimization of temperature control unit and air flow control unit, pesticide molecules volatilizing from solution are efficiently captured by substrate and generate real-time SERS signals by a hand-held Raman spectrometer, and the sensor system based on HT-SPME-SERS was finally established. A novel SERS substrate of Cu@rGO@Ag was developed as HT-SPME-SERS material, where reduced graphene oxide (rGO) enriched pesticide molecules by π-π stacking. A superior detection sensitivity brought by the ultra-high enhancement effect of Cu@rGO@Ag substrate was obtained. A good linear relationship between Raman intensity and isocarbophos concentration was obtained and the limit of detection (LOD) was as low as 0.00451 ppm. The detection results obtained from the sensor system have been verified by gas chromatography-mass spectrometer (GC-MS), showing its great application potential for the safety of agricultural products.