Fingerprinting Technique Research Articles

Surface-Enhanced Raman Scattering-Based Multimodal Techniques: Advances and Perspectives.

Surface-enhanced Raman scattering (SERS) spectroscopy is a versatile molecular fingerprinting technique with rapid signal readout, high aqueous compatibility, and portability. To translate SERS for real-world applications, it is pertinent to overcome inherent challenges, including high sample variability and heterogeneity, matrix effects, and nonlinear SERS signal responses of different analytes in complex (bio)chemical matrices with numerous interfering species. In this perspective, we highlight emerging SERS-based multimodal techniques to address the key roadblocks to improving the sensitivity, specificity, and reliability of (bio)chemical detection, bioimaging, theragnosis, and theragnostic. SERS-based multimodal techniques can be broadly categorized into two categories: (1) complementary methods or systems that work together to achieve a common goal where each method compensates for the weaknesses of the other to culminate in a single enhanced outcome or (2) orthogonal techniques that are independent and provide separate but corroborating results simultaneously without interfering with each other. These multimodal techniques maximize information gained from a single experiment to achieve enhanced qualitative or quantitative analysis and broaden the range of detectable analytes from small molecules to tissues. Finally, we discuss emerging directions in multimodal platform design, instrument integration, and data analytics that aim to push the analytical limits of holistic detection.

Defending Against Deep Learning-Based Traffic Fingerprinting Attacks With Adversarial Examples

In an increasingly digital and interconnected world, online anonymity and privacy are paramount issues for Internet users. To address this, tools like The Onion Router (Tor) offer anonymous and private communication by routing traffic through multiple relays with multiple layers of encryption. However, traffic fingerprinting attacks have threatened anonymity and privacy. In response, the community has proposed additional defenses for Tor, but fingerprinting techniques that utilize deep neural networkss (DNNs) have undermined many of these defenses. The latest defenses that are both lightweight and robust against DNNs use adversarial examples, but these defenses require either the full traffic trace beforehand or a database of pre-computed adversarial examples. We propose Prism , a defense against fingerprinting attacks that utilizes adversarial examples with neither prior access to the full traffic trace nor a database. We describe a novel method of adversarial example generation as input is learned over time. Prism injects these adversarial examples into the Tor traffic stream to prevent DNNs from accurately classifying both websites and videos that a user is viewing, even if the DNN is hardened by adversarial training. We also show that the Tor network could implement Prism entirely on relays under certain conditions, extending the defense to users who may run Tor on devices without graphics processing units.

Application of stable isotopes and mineral elements fingerprinting for beef traceability and authenticity in inner mongolia of China

In recent years, the origin and safety of counterfeit meat products have raised significant concerns among consumers. Therefore, there was an urgent need to develop a new method using fingerprinting techniques for meat product traceability. This study aimed to evaluate the traceability and authenticity of beef from Inner Mongolia by measuring the δ13C and δ15N values, as well as 13 mineral elements. In the complex environment of feeding regimes and agricultural types, δ15N, δ13C, and Se were selected as characteristic variables to differentiate feeding regimes, while Fe, K, Tl, Sr, Mn, and δ13C were chosen as characteristic variables for tracing the origin of the beef. Linear discriminant analysis achieved an accuracy rate of up to 100 %. This research confirmed the effectiveness of combining stable isotopes with mineral elements in accurately determining the origin of Inner Mongolian beef and verifying the authenticity of typical feed, providing a valuable strategy for traceability.

AI-DPAPT: a machine learning framework for predicting PROTAC activity.

Proteolysis Targeting Chimeras are part of targeted protein degradation (TPD) techniques, which are significant for pharmacological and therapy development. Small-molecule interaction with the targeted protein is a complicated endeavor and a challenge to predict the proteins accurately. This study used machine learning algorithms and molecular fingerprinting techniques to build an AI-powered PROTAC Activity Prediction Tool that could predict PROTAC activity by examining chemical structures. The chemical structures of a diverse set of PROTAC drugs and their corresponding activities are selected as a dataset for training the tool. The processes used in this study included data preparation, feature extraction, and model training. Further, evaluation was done for the performance of the various classifiers, such as AdaBoost, Support Vector Machine, Random Forest, Gradient Boosting, and Multi-Layer Perceptron. The findings show that the methods selected here depict accurate PROTAC activities. All the models in this study showed an ROC curve better than 0.9, while the random forest on the test set of the AI-DPAPT had an area under the curve score of 0.97, thus showing accurate results. Furthermore, the study revealed significant insights into the molecular features that can influence the functions of the PROTAC. These findings can potentially increase the understanding of the structure-activity correlations involved in the TPD. Overall, the investigation contributes to computational drug development by introducing this platform powered by artificial intelligence that predicts the function of PROTAC. In addition, it sped up the processes of identifying and improving previously unknown medications. The AI-DPAPT platform can be accessed online using a web server at https://ai-protac.streamlit.app/ .

Sediment runoff formation in a small periglacial catchment on the King George (Vaterloo) island, Antarctica

Global climate change has most significantly affected the Polar Regions. The increase in air temperature has stimulated the melting of glaciers in the Arctic and Antarctic, which has contributed to changes in the formation of water and sediment runoff. However, there are very few quantitative estimates of the sediment redistribution in the periglacial catchments of the Polar Regions. Specific features of water and sediment runoff were studied within the catchment area of the Korabelnyj Stream located on the Fildes Peninsula in Antarctica near the Bellingshausen Ice Dome. The main aim of the study was to investigate the conditions for the formation of water and sediment runoff and to identify the proportional contribution of washout and erosion material coming from the periglacial and maritime parts of the catchment area to the sediment runoff of the stream. A set of methods and approaches, including: a) estimates of the sediment flow connectivity index; b) fingerprinting technique; c) hydrometeorological observations; d) large-scale geomorphological survey and others, was ap[1]plied to identify the conditions for the formation of surface runoff and washout, the mechanisms of sediment redistribution in various parts of the fluvial network and to quantify the proportional contribution of two main sediment sources to the sediment runoff of the stream. A fraction with a particle size of ≤63 μm was used for geochemical and spectrometric analyzes of soils and sediments. In total, the content of 34 elements was analyzed, i.e. 6 radioisotopes and 28 stable elements. It has been established that despite the significant differences in the relief of the near-glacial and maritime parts of the catchment area, the indices of sediment connectivity are quite close and amount to –1,79 and –1,35, respectively. A significant part of the material transported by temporary streams from the slopes of the catchment area is redeposited in relief depressions partially occupied by water bodies. The main volume of sediments, which is at least 60–66% of the total sediment runoff in the outlet section of the Korabelnyj Stream, comes from the periglacial part of the catchment area. This is due to the increased water discharge relative to the non-glacial part of the catchment area, which results from the melting of snow and ice accumulated on the ice dome, the high erosion of moraine deposits unprotected by vegetation, and the presence of an ice core in moraines, which prevents water filtration.

An Adaptive Multisource Data Fusion Indoor Positioning Method Based on Collaborative Wi-Fi Fingerprinting and PDR Techniques

An Adaptive Multisource Data Fusion Indoor Positioning Method Based on Collaborative Wi-Fi Fingerprinting and PDR Techniques

2D MoS2 plasmonic nanocavity based SERS platform for bilirubin detection

Although various methods can be used to detect bilirubin at the micromolar level, achieving reliable detection in the sub-nanomolar range remains challenging. Sensitivity at these ultralow levels is vital, as new evidence strongly associates bilirubin with chronic heart, lung, and neurological disorders. This accuracy is also essential for creating diagnostic protocols that can efficiently track bilirubin levels, assisting in managing related conditions. Such advancements will lead to enhanced diagnostic tools and methods, enabling more precise bilirubin monitoring in clinical environments. Here, the aforesaid blind-spot is addressed by utilizing surface enhanced Raman spectroscopy (SERS) as fingerprint technique to detect bilirubin concentrations as low as 0.1 nM. A plasmonically active 2D MoS2 based SERS substrate is formulated and optimized for single step SERS sensing of bilirubin. Plasmonic cavity based approach is utilized to circumvent the problem related to fluorescent background overwhelming the SERS signal. The 2D MoS2 based SERS platform shows threefold enhancement of scattering in comparison to the traditional gold nanoparticle on Si/SiO2 approach. The Raman signature of bilirubin is calibrated by taking citrate as a stable reference analyte and is shown to vary linearly with logarithmic concentration across the picomolar to micromolar range.

Authentication of Shallots from Brebes using Gas Chromatography Fingerprinting Technique Combined with Chemometrics

Authentication of Shallots from Brebes using Gas Chromatography Fingerprinting Technique Combined with Chemometrics

Ge-Zhi-Jie-Jiu decoction alleviates alcoholic liver disease through multiple signaling pathways

Ethnopharmacological relevanceAlcoholic liver disease (ALD) is a growing public health concern caused by excessive alcohol consumption, but effective treatments are limited. Ge-Zhi-Jie-Jiu decoction (JJY) is a modified traditional Chinese herbal remedy that aims to alleviate ALD. This formula contains various components such as Ge Hua, Ge Gen, Zhi Ju Zi, and other medicinal-food herbs. However, the specific pharmacotherapeutic compounds of JJY and its pharmacological mechanisms remain unclear. Aim of the studyThis study aimed to elucidate the molecular mechanism and pharmacodynamic basis of JJY in treating ALD. Materials and methodsUPLC-Q-Orbitrap HRMS, HPLC fingerprinting, and LC-MS techniques were used for the composition identification and quality control of JJY. The pharmacological components and molecular mechanisms of JJY in anti-ALD were then predicted using network pharmacology and molecular docking approaches. Ultimately, an acute alcoholic liver injury mouse model was developed, and the potential mechanisms were verified by hematoxylin-eosin (H&E), Oil Red O, and TUNEL staining, real-time fluorescence quantitative PCR (RT-qPCR), Western blot (WB) and molecular docking analysis. ResultsThe results showed that the main components of JJY are organic acids, flavonoids, and isoflavonoids, in which puerarin, daidzein, glycitein, ononin, quercetin, and tectorigenin can be used as the indicator components of JJY. In addition, JJY might ameliorate ALD through several pathways, including potentially promoting alcohol metabolism via alcohol-metabolizing enzymes, and possibly inhibiting oxidative stress, inflammation and apoptosis via the Nrf2/Keap1/HO-1 and MAPK signaling pathways. Furthermore, JJY may also alleviated hepatic lipid accumulation through the PPARα signaling pathway. ConclusionsJJY has significant anti-ALD efficacy with multiple mechanisms. This study offers a solid experimental foundation for JJY's development as a medicine with anti-ALD characteristics and elucidates its probable active components.

Optimized Received Signal Strength-Based Radio Map Interpolation for Indoor Positioning Systems

The most viable strategy to establish a dependable indoor positioning system is by employing the received signal strength (RSS) based fingerprinting technique, which encompasses both the offline and online phases. The offline phase involves constructing a radio map, which can be arduous in vast indoor environments. To tackle this, radio map interpolation is often used to interpolate RSS by utilizing the RSS recorded at a coarser level of known reference points (RPs). This paper proposes a novel RSS-based radio map interpolation to enhance the existing inverse distance weighting (IDW) interpolation technique. The method divides the deployment area into zones and optimizes the density of known RPs in each zone based on the number of access points (APs) with average RSS exceeding the threshold. It allocates higher RP density for the zones with poor AP coverage and reduces it for well-covered zones. Results demonstrates that the proposed method achieves substantial improvements over the baseline IDW scheme in average positioning error of up to 6.58% at the floor level and 3.77% overall.

“Animation” URL in NFT marketplaces considered harmful for privacy

Non-Fungible Tokens (NFTs) are becoming increasingly popular as a way to represent and own digital property. However, the usage of NFTs also prompts questions about privacy. In this work, we show that it is possible to use NFTs to retrieve enough information to fingerprint users. By doing so, we can uniquely associate users with blockchain accounts. This would allow linking several blockchain accounts to the same user. This work focuses on the vulnerabilities presented by some popular NFT marketplaces. Since NFTs may have HTML files embedded, they allow the use of fingerprinting techniques if not handled carefully. Finally, we provide recommendations and countermeasures for the different actors in this ecosystem to avoid these kinds of tracking methods and, in doing so, safeguard user privacy.

Comparative Analysis of Hepatitis C virus Genotype 1a (Isolate 1) using Multiple Regression Algorithms and Fingerprinting Techniques

Approximately 70 million people worldwide have been infected with Hepatitis C virus (HCV), presenting a critical global health challenge. As a member of the Flaviviridae family, HCV can cause severe liver diseases such as cirrhosis, acute hepatitis, and chronic hepatitis. The Hepatitis C virus (HCV) genome encodes a single polyprotein consisting of 3010 amino acids, which when processed contains 10 polypeptides derived from cellular and viral proteases. These include structural proteins such as core protein, E1 and E2 envelope glycoproteins, and nonstructural proteins such as NS1, NS2, NS3, NS4A, NS4B, NS5A, and NS5B. Nonstructural proteins will be released by HCV NS2-3 and NS3-4A proteases, however, structural proteins will be released by host ER signaling peptidases. co-translationally and post-translationally form 10 individual structural proteins: 5'-C-E1-E2-p7-NS2-NS3-NS4A-NS4B-NS5A-NS5B-3'. Despite extensive research, there are significant gaps in predictive and analytical approaches to managing HCV, particularly in understanding the polyprotein structure and its implications for drug discovery. This study addresses these gaps by employing machine learning techniques to analyze HCV polyprotein using various fingerprinting methods and regression algorithms. The data was sourced from the ChEMBL database, and fingerprinting techniques such as PubChem, MACCS, and E-State were utilized. Regression algorithms, including Gradient Boosting Regression (GBR), Random Forest Regression (RFR), AdaBoost Regression (ABR), and Hist Gradient Boosting Regression (HSR), were applied. Model performance was evaluated using R² and Adjusted R² metrics, comparing default models with those enhanced by hyperparameter tuning. Feature importance analysis was conducted to identify key features influencing model performance, aiding in model simplification. The results show that although hyperparameter tuning does not significantly improve the predictive power of a model, it can provide an insight into model optimization. In particular, the default model showed higher R² and Adjusted R² values across different fingerprinting techniques compared to models with hyperparameterized features. Gradient Boosting Regression (GBR) and Random Forest Regression (RFR) consistently performed well, with GBR showing the highest R² values when using PubChem fingerprints. Although there was no significant improvement through hyperparameter tuning, this study was able to find out the features that strongly influenced the model performance by conducting a feature importance analysis. This analysis helped simplify the model and highlighted the potential of machine learning in improving the understanding of HCV polyprotein structure. This research identifies optimal regression models and fingerprinting techniques, providing a strong framework for future drug discovery efforts aimed at improving global health outcomes. The research also shows that it is important to date to advance drug discovery using machine learning.

An Intelligent Fingerprinting Technique for Low-Power Embedded IoT Devices

An Intelligent Fingerprinting Technique for Low-Power Embedded IoT Devices

High-coverage characterization and discovery of molecular markers for quality control of natural fragrant plant extracts using UPLC-HRMS-based untargeted metabolomics.

The chemical components of natural fragrant plant extracts are of high complexity, and the strategies for quality control (QC) and further discovery of fragrance mechanisms still need to be further investigated. This study integrated the strategies and methods of untargeted metabolomics and chemometrics and statistical modeling to attain the goal. The techniques of reversed-phase and HILIC analysis of ultra-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS) were simultaneously used to collect data in both positive and negative ion modes. The pattern analysis of fingerprints and discovery of characteristic molecular markers for QC analysis were comprehensively employed to reach in-depth analysis of the quality variation and discovery of differential molecules among natural fragrant plant extracts. The former uses fingerprint technique to analyze their overall similarities and differences, and the latter comprehensively discovers molecular substances characterizing the chemical characteristics of fragrant extracts with the help of metabolomics and univariate and multivariate methods. The findings are expected to be used as the molecular markers in product manufacturing, sales, and consumption to achieve accurate quality control and recognition of targeted molecules for potential quality monitoring using spectroscopy techniques. In this work, 27 natural fragrant extracts were applied as examples, and their chemical components were comprehensively analyzed with discovery of markers for quality control. After data integration, 1178 molecules were annotated, and 267 differential metabolite molecules with the values of variable importance in the projection (VIP) larger than 1.0 were found. The results show that the method proposed in this work is of great significance for high-coverage analysis, QC marker discovery, and aroma mechanism elucidation, which has potential applications in the areas of food, cosmetics, pharmaceuticals, tobacco, and others.

Quantitative Analysis of Aeolian Sand Provenance: A Comprehensive Analysis in the Otindag Dune Field, Central Inner Mongolia, China

The identification and quantification of aeolian sand contributions are essential for understanding the formation of dune fields and mechanisms of modern surface processes. In the present study, we take aeolian sand in the Otindag dune field (hereafter, often referred to as, simply, Otindag) as the research object. The dune field’s immediate source is quantitatively identified based on heavy minerals and the Conservativeness Index (CI), Consensus Ranking (CR), and the Consistent Tracer Selection (CTS) method. The primary source area of the aeolian sand was found to be from the northwestern, upwind area of the Otindag (59 ± 14%), followed by the Yinshan Mountain (17 ± 10%) and the lake basin (23 ± 12%). The proposed sediment transport model elucidates that sediments from the upwind of the Otindag are directly transported from the northwest to the Otindag, where they are deposited. Materials from the southern Yinshan Mountains are carried by rivers to the southern edge of the Otindag, where they are subsequently transported by wind and ultimately deposited. The lake deposits within the Otindag also contribute to the aeolian sand supply under the influence of wind. This study demonstrates that the fingerprinting techniques of CI, CR, and CTS serve as successful strategies for conducting quantitative provenance research in dune fields.

Antimicrobial resistance profile and prevalence of Mycobacterium tuberculosis complex in Western Iran using spoligotyping method

Tuberculosis (TB) is a chronic infectious disease with multiple manifestations and gradual progression that remains a major health problem and a leading cause of death worldwide. In recent years, a number of DNA fingerprinting techniques have been developed to identify strains of the Mycobacterium tuberculosis (MTB) complex. Spoligotyping is one of the first PCR-based genotyping methods. Information on the number and identification of common strains among MTB complex samples in clinical samples from Kermanshah city is needed to develop more effective therapeutic strategies.This is a descriptive cross-sectional study of 41 sample patients with TB referred to Kermanshah Tuberculosis Centre between December 2021 and June 2022, including sputum, aspiration, urine, etc. First, the susceptibility of the developed bacteria to culture media was compared with that of isoniazid using the proportional method, and rifampin was determined according to the standard protocol. Demographic data of patients referred to the Centre for the Control of Lung Diseases were also recorded.In the next step, spoligotyping was carried out using the standard method and each strain pattern was recorded as an OCTAL code and compared with the information available at the World Bank on spoligotyping and its strains. Forty-one patients with pulmonary TB were tested using spoligotyping. Four MTB strains were identified, including H4, CAS, T1 and H1.The H4 strain also had the highest frequency with 16 samples (39%) among the MTB complex strains isolated using spoligotyping.The highest frequency of strains isolated using spoligotyping was associated with the H4 strain. It can be concluded that spoligotyping is very cost effective, simple, repeatable and highly sensitive.

Exploring the interspecific relationships among Dendrobium species via electrochemical fingerprinting techniques

This study investigated the interspecific relationships among 29 Dendrobium species using electrochemical fingerprinting techniques. Differential pulse voltammetry was employed to record species-specific electrochemical profiles in both phosphate buffer solution (PBS) and acetic acid buffer solution (ABS). Distinct voltammetric patterns were observed for each species, with ABS yielding higher peak currents compared to PBS. On average, peak currents in ABS were 37.2 % ± 5.8 % higher than in PBS across all species. For example, D. nobile showed a peak current of 12.3 μA in ABS compared to 8.9 μA in PBS, a 38.2 % increase. Normalized two-dimensional plots of the electrochemical fingerprints served as unique signatures for species differentiation. Cluster analysis of the electrochemical data revealed phylogenetic relationships largely consistent with previous molecular studies, while also highlighting novel groupings. For instance, D. parishii and D. pulchellum formed separate branches, indicating distant relationships with other species. Common species such as D. officinale, D. nobile, D. primulinum, and D. loddigesii clustered together, confirming their close relationships. Interestingly, species from Sect. Dendrobium showed mixed clustering with species from four other sections, reflecting the genus's complex evolutionary history. While some inconsistencies with previous molecular phylogenetic studies were noted, such as the distant relationship between D. parishii and D. anosmum, the overall congruence between electrochemical and molecular data validates the potential of this approach. This study demonstrates that electrochemical fingerprinting, combined with appropriate data analysis techniques, can provide valuable insights into the taxonomic relationships and chemical diversity of Dendrobium species, offering a complementary tool to traditional morphological and molecular methods for plant systematics and conservation efforts.

Source differentiation of BTEX compounds in groundwater contaminated due to refinery activities

This study aims to identify sources of groundwater contamination in a refinery area using integrated compound-specific stable isotope analysis (CSIA), oil fingerprinting techniques, hydrogeological data, and distillation analysis. The investigations focused on determination of the origin of benzene, toluene, ethylbenzene, and xylenes (BTEX), and aliphatic hydrocarbons as well. Groundwater and floating oil samples were collected from extraction wells for analysis. Results indicate presence of active leaks in both the northern and southern zones. In the northern zone, toluene was found to primarily originate from oil products like aviation turbine kerosene (ATK or aviation fuel), kerosene, regular gasoline, and diesel fuel. Additionally, stable isotope ratios of carbon and hydrogen for ethylbenzene, o-xylene (ortho xylene) and p-xylene (para xylene) in zone A suggested the pollution originated from gasoline within the northern zone. The origin of super gasoline (with higher octane) identified in southern zone using δ13C and δ2H values of toluene in the floating oil and groundwater samples. Further, biodegradation of toluene likely occurred in southern zone according to δ13C and δ2H. The findings underscore the critical importance of integrating CSIA and fingerprinting techniques to effectively address the challenges of source identification and relying solely on each method independently is insufficient. Accordingly, comparing the GC-MS results of floating oil samples with ATK and jet fuel (JP4) standards can be effectively utilized for source differentiation. However, this method showed no practical application to distinguish different types of diesel or gasoline. The accuracy and reliability of source identification of BTEX compounds may significantly improve when hydrogeological data incorporates with stable isotopes analysis. Additionally, the results of this study will elevate the procedures for fuel-related contaminants source identification of the polluted groundwater that is crucial to develop effective remediation strategies.

A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification.

LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities.

A comprehensive study of Tianma Toutong Tablets from the dual dimensions of quality and efficacy using fingerprint techniques and network pharmacology

BackgroundThe quality of traditional Chinese medicine (TCM) is the prerequisite for ensuring its safe and effective clinical application. With the increasing popularity of TCM worldwide, the quality control of TCM products has become increasingly crucial. Tianma toutong tablet (TMTTT) is mainly used for migraine caused by external wind and cold, blood stasis, or deficiency of blood and nourishment. However, the mechanism of action of TMTTT is still unclear, and there has been a lack of in vitro antioxidant activity research and migraine treatment mechanism research. Therefore, it is urgent to establish a set of comprehensive and effective evaluation methods. Resultsthree fingerprint profiles were established using HPLC, UV, and DSC analysis methods, and established three digital parameters simple complexity index (SX), simple clarity index (SY), simple complexity clarity ratio (Sω), 22 batches of samples were evaluated using a comprehensive linear quantitative fingerprint method (CLQFM). In addition, the antioxidant activity of the samples was determined using the DPPH method, and the relationship between fingerprint peaks in different fingerprints and antioxidant capacity was explored using Pearson correlation coefficients. Finally, network pharmacological research was conducted to investigate the potential targets, compounds, and pathways involved in the treatment of migraines with TMTTT. The results showed that the 22 batches of samples were classified into different quality grades. TMTTT exhibited good antioxidant activity, the fingerprint-efficacy relationship showed that gastrodin, chlorogenic acid, ferulic acid, imperatorin and isoimperatorin had strong antioxidant capacity, providing directions for the identification of active compounds. A total of 36 core targets were identified and screened by network pharmacology, which AKT serine/threonine kinase 1 (AKT1), albumin (ALB), insulin (INS), tumor necrosis factor (TNF), prostaglandin-endoperoxide synthase 2 (PTGS2) and interleukin-6 (IL-6), and compounds such as β-sitosterol, chrysophanol, vanillin are the key to the treatment of migraine, providing references for subsequent clinical research and new drug development. Significance: This study examined the consistency of the quality of TMTTT and the mechanism of action in treating migraines from both quality and efficacy perspectives, providing a favorable direction for further research on TMTTT and offering new ideas for the quality control of TCM compound formulations.