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.

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.

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.

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.

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.

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.

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.

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.

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.

Highly sensitive technique for detection of adulterants in centella herbal samples using surface enhanced Raman spectroscopy (SERS).

The trace level detection of adulterants in food, nutritional supplements and medicinal herbs is highly challenging in the field of food processing and herbal industries. In addition, laborious sample processing procedures and well trained personnel are required to analyse the samples using conventional analytical equipments. In this study, a highly sensitive technique with minimal sampling processes and human intervention is proposed for the trace amount detection of pesticidal residues in centella powder. Herein, graphene oxide gold (GO-Au) nanocomposite coated parafilm is developed as substrate by simple dropcasting technique to facilitate dual surface enhanced Raman signal. The dual SERS enhancement involving chemical enhancement from graphene and electromagnetic signal enhancement from gold nanoparticles is utilized for detection of chlorpyrifos in the ppm level concentration. The flexible polymeric surfaces could be the better choice for SERS substrates due to their inherent properties such as flexibility, transparency, roughness and hydrophobicity. Among the various types of flexible substrates explored, GO-Au nanocomposites coated parafilm substrates showed better Raman signal enhancement. Parafilm coated with GO-Au nanocomposites is successful in achieving detection limits down to 0.1 ppm of chlorpyrifos in centella herbal powder sample. Thus, the fabricated parafilm based GO-Au SERS substrates could be used as a screening tool at quality control of herbal product manufacturing sectors for trace level detection of adulterants in herbal samples from their unique chemical and structural information.

Spectroscopic biofeedback on cutaneous carotenoids: A powerful tool for primary prevention in advanced age.

Antioxidants exhibit a powerful defense mechanism against aging and chronic disease. The human skin reflects the overall antioxidant status of the body. The cutaneous carotenoid concentration is a biomarker for individual nutritional intake of antioxidants, as it correlates with the overall antioxidant status. The cutaneous carotenoid concentrations of 44 adults were measured using a multiple spatially resolved reflection spectroscopy. During the first phase of the study, measurements of carotenoid concentrations were performed without revealing the antioxidant status, followed by an intervention phase during which the volunteers were informed about their individual values by biofeedback. During the third phase, biofeedback was combined with an additional intake of fruit juices. Across time points, participants showed increasing levels of carotenoid status. Thus, biofeedback leads to an improvement of the carotenoid value of the skin. Providing a biofeedback measurement to monitor the individual antioxidative status may be an easy and cost-effective way of primary prevention.

Label-free detection of trace level zearalenone in corn oil by surface-enhanced Raman spectroscopy (SERS) coupled with deep learning models.

Surface-enhanced Raman spectroscopy (SERS) and deep learning models were adopted for detecting zearalenone (ZEN) in corn oil. First, gold nanorods were synthesized as a SERS substrate. Second, the collected SERS spectra were augmented to improve the generalization ability of regression models. Third, five regression models, including partial least squares regression (PLSR), random forest regression (RFR), Gaussian progress regression (GPR), one-dimensional convolutional neural networks (1D CNN), and two-dimensional convolutional neural networks (2D CNN), were developed. The results showed that 1D CNN and 2D CNN models possessed the best prediction performance, i.e., determination of prediction set (RP2) = 0.9863 and 0.9872, root mean squared error of prediction set (RMSEP) = 0.2267 and 0.2341, ratio of performance to deviation (RPD) = 6.548 and 6.827, limit of detection (LOD) = 6.81 × 10-4 and 7.24 × 10-4 µg/mL. Therefore, the proposed method offers an ultrasensitive and effective strategy for detecting ZEN in corn oil.

CRISPR Cas12a-Powered Silicon Surface-Enhanced Raman Spectroscopy Ratiometric Chip for Sensitive and Reliable Quantification.

Sensitive and reliable clustered regularly interspaced short palindromic repeats (CRISPR) quantification without preamplification of the sample remains a challenge. Herein, we report a CRISPR Cas12a-powered silicon surface-enhanced Raman spectroscopy (SERS) ratiometric chip for sensitive and reliable quantification. As a proof-of-concept application, we select the platelet-derived growth factor-BB (PDGF-BB) as the target. We first develop a microfluidic synthetic strategy to prepare homogeneous silicon SERS substrates, in which uniform silver nanoparticles (AgNPs) are in situ grown on a silicon wafer (AgNPs@Si) by microfluidic galvanic deposition reactions. Next, one 5'-SH-3'-ROX-labeled single-stranded DNA (ssDNA) is modified on AgNPs via Ag-S bonds. In our design, such ssDNA has two fragments: one fragment hybridizes to its complementary DNA (5'-Cy3-labeled ssDNA) to form double-stranded DNA (dsDNA) and the other fragment labeled with 6'-carboxy-X-rhodmine (ROX) extends out as a substrate for Cas12a. The cleavage of the ROX-tagged fragment by Cas12a is controlled by the presence or not of PDGF-BB. Meanwhile, Cy3 molecules serving as internal standard molecules still stay at the end of the rigid dsDNA, and their signals remain constant. Thereby, the ratio of ROX signal intensity to Cy3 intensity can be employed for the reliable quantification of PDGF-BB concentration. The developed chip features an ultrahigh sensitivity (e.g., the limit of detection is as low as 3.2 pM, approximately 50 times more sensitive than the fluorescence counterpart) and good reproducibility (e.g., the relative standard deviation is less than 5%) in the detection of PDGF-BB.

Surface-enhanced Raman scattering integrating with machine learning for green tea storage time identification.

The rapid and accurate identification of complex samples still remains a great challenge, especially for those with similar compositions. In this work, we report an integration strategy consisting of surface-enhanced Raman scattering (SERS) and machine learning to discriminate complex and similar analytes, in this case green tea products with different storage times. Surface-functionalized Ag nanoparticles (NPs) were used as a SERS substrate to reveal the changes in the sensory components of green tea with variable storage time. Principal components analysis (PCA)-based support vector machine (SVM) classification was used to extract the key spectral features and identify green tea with different storage times. The results showed that such an integration strategy achieved high predictive accuracy on time tag discrimination for green tea. The multiclass SVM classifier successfully recognized green tea with different storage times at a prediction accuracy of 95.9%, sensitivity of 96.6%, and specificity of 98.8%. Therefore, this work illustrates that the SERS-based PCA-SVM platform might be a facile and reliable tool for the identification of complex matrices with subtle differentiations.

Development of in-situ Raman diagnosis technique of eosinophil esophagitis.

The spectra of the live tissue with blood flow measured with 785 nm-excitation light showed a very weak signal due to hemoglobin (Hb). It suggested the possibility to detect eosinophil accumulation in the tissue with the 785 nm-excitation light. The excitation wavelength of 633 nm induced strong fluorescence of sapphire glass that is a material of the ball lens of BHRP (Ball lens top hollow optical fiber Raman probe). On the other hand, the previous study suggested that eosinophil including eosinophil peroxidase (EPO) that showed a strong resonance Raman effect with 633 nm-excitation light. The purpose of the present study is to collect basic information and to evaluate the viability of Raman spectroscopic analysis for the detection of eosinophil accumulation in the live esophagus. BHRP with a sapphire ball lens with 500 µm diameter was applied for measurement of live esophagus tissue of a mouse. In this study, Raman spectra of eosinophil were measured with 633 and 785 nm-excitation. The Raman spectra of eosinophil showed a strong contribution of EPO, suggested that a heme chromophore in EPO had pre-resonance enhancement via Q band with the 785 nm-excitation light. Principal component analysis (PCA) is applied for the analysis of Raman spectra of eosinophil, erythrocyte and other granulocytes. Eosinophil was successfully discriminated from other blood cells in the PCA score plots built for the datasets of the spectra measured with 633 and 785 nm-excitation wavelengths. Consequently, our study demonstrates that Raman spectroscopy with 785 nm-excitation had high viability for in situ analysis of eosinophilic esophagitis (EoE).

Surface-enhanced Raman spectral characterization of antifungal activity of selenium and zinc based organometallic compounds.

Surface-enhanced Raman spectroscopy (SERS) is used to identify the biochemical changes associated with the antifungal activities of selenium and zinc organometallic complexes against Aspergillus niger fungus. These biochemical changes identified in the form of SERS peaks can help to understand the mechanism of action of these antifungal agents which is important for development of new antifungal drugs. The SERS spectral changes indicate the denaturation and conformational changes of proteins and fungal cell wall decomposition in complex exposed fungal samples. The SERS spectra of these organometallic complexes exposed fungi are analyzed by using statistical tools like principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). PCA is employed to differentiate the SERS spectra of fungal samples exposed to ligands and complexes. The PLS-DA discriminated different groups of spectra with 99.8% sensitivity, 100% specificity, 98% accuracy and 86 % area under receiver operating characteristic (AUROC) curve.

Rapid fabrication of the Au hexagonal cone arrays for SERS applications.

This study performed trace detection using surface-enhanced Raman scattering (SERS) on Au hexagonal cone arrays (Au-HCAs). Uniform porous anodized aluminum oxide (AAO) templates were used, and an Ag film with a cone cavity was prepared using a thermal deposition technique. Next, a series of homogeneous Au-HCAs were prepared controllably via electrodeposition growth technology. The prepared Au-HCAs were used as SERS substrates, and according to the experimental results, the optimal electrodeposition time is 600 s. At this time, Au-HCAs had the highest SERS activity. The detection limit of R6G was 10-9 M, exhibiting high reproducibility and high uniformity at 10-6 M, indicating that Au-HCAs had good stability. Moreover, a good linear correlation between the Raman intensity and the molecular concentration endowed Au-HCAs with good quantitative analysis ability. Therefore, the Au-HCAs exhibited great potential for qualitative and quantitative detection.