ObjectiveAstragalus membranaceus, a widely recognized traditional Chinese medicinal herb, is extensively employed for its immunomodulatory and health-enhancing properties. The quality and therapeutic efficacy of Astragalus are profoundly influenced by its geographical origin, underscoring the necessity for reliable methods to authenticate its provenance, ensure product integrity, and mitigate adulteration risks. Conventional identification techniques, encompassing morphological, chemical, and DNA-based approaches, are often constrained by their time-intensive, laborious, and costly nature, thereby limiting their applicability in large-scale industrial contexts. Spectroscopic techniques, such as Laser-Induced Breakdown Spectroscopy (LIBS) and Near-Infrared Spectroscopy (NIR), have emerged as rapid, non-destructive, and efficient analytical tools for quality assessment and geographical origin determination. Nevertheless, the inherent complexity of Astragalus, characterized by its diverse elemental and molecular profiles, often renders single-spectral techniques inadequate for comprehensive characterization. Data fusion methodologies, which integrate complementary information from multiple sources, offer a promising avenue to enhance classification accuracy. By leveraging advanced data fusion strategies to combine LIBS and NIR spectral data, the accuracy of geographical origin discrimination for Astragalus membranaceus can be substantially improved.MethodsAstragalus samples were collected from five different geographical origins: Gansu, Heilongjiang, Inner Mongolia, Shanxi, and Shaanxi (Fig.1). Complementary elemental and compositional information was obtained using LIBS and NIR techniques (Fig.2). Initially, Support Vector Machine (SVM), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) models were developed based on individual LIBS and NIR spectral data, and LDA was selected as the base model for investigating fusion classification outcomes based on the single-spectral classification results (Tab.1). To improve classification performance, lower-level and mid-level data fusion strategies were employed to integrate LIBS and NIR spectral information. Lower-level data fusion involves the direct concatenation of LIBS and NIR spectral data to form a new lower-level fused spectral dataset for model classification (Fig.4). Mid-level data fusion, on the other hand, extracts the most representative features from LIBS and NIR spectra separately and then concatenates these features to form a mid-level fused spectral dataset for model classification (Fig.4). The model's performance was evaluated using various metrics, including classification accuracy (ACC), macro-precision (M-P), macro-recall (M-R), macro-F1 score (M-F1), and the Area Under the Curve (AUC), to assess the effectiveness of spectral fusion strategies compared to single-spectral approaches.Results and DiscussionsIn single-spectrum analysis, the LDA model for LIBS achieved an optimal classification accuracy of 88% on the test set (Tab.1). In comparison, the lower-level fusion LDA model attained an accuracy of 92.00% and an AUC value of 0.9964 on the test set (Tab.3). The most notable enhancement, however, was observed in the mid-level fusion approach, which utilized the Stepwise Projection Algorithm (SPA) for feature selection on both LIBS spectral lines and NIR data. This mid-level fusion LDA model achieved a classification accuracy of 96.00% and an AUC value of 0.9998 on the test set (Tab.3), showing substantial improvements in both precision and reliability. The mid-level fusion approach successfully eliminated redundant data, enabling more efficient and accurate classification. Finally, an importance analysis was conducted on the features in the mid-level fusion (Fig.9), with key features being interpreted. The results indicate that integrating complementary spectral data from LIBS and NIR significantly outperforms single-spectrum analysis in terms of classification accuracy and robustness.ConclusionsThe results demonstrate the efficacy of combining LIBS and NIR spectral data through data fusion for the accurate and efficient identification of the geographical origin of Astragalus membranaceus. The mid-level fusion model, which integrates feature selection techniques, provided the highest classification performance, indicating its potential for non-destructive and rapid origin authentication. The findings not only highlight the advantages of spectral fusion in enhancing classification accuracy but also propose a reliable and scalable solution for the quality control and traceability of medicinal herbs in the pharmaceutical industry. The successful application of LIBS-NIR spectral fusion paves the way for more comprehensive analytical approaches in the quality assessment of traditional Chinese medicinal materials.

Significance Terahertz (THz) technology, due to its unique non-contact measurement and non-destructive testing capabilities, has shown remarkable potential in a wide range of marine applications. The ability to detect pollutants, assess the condition of marine infrastructure, and monitor ecological health in real time offers significant advantages over traditional methods. As such, THz technology holds great promise for advancing marine environmental protection and resource management.Progress Firstly, the application of terahertz (THz) waves in marine environment monitoring is first introduced. In the THz waveband, the normalized radar cross section (NRCS) is used to measure variations in reflection intensity at different incident angles, which helps evaluate the characteristics of the oil film. NRCS reflects the interaction of THz waves with the oil film surface, providing insights into its thickness, distribution, and surface properties. As the propagation properties of THz waves differ between the oil film and the water surface, factors such as the thickness, composition, and surface conditions of the oil film directly influence the reflection characteristics. By measuring the reflection intensity at different incident angles, detailed information about the oil film can be obtained, allowing for precise assessment of its thickness and distribution (Fig.5). For water quality classification, THz waves are incident at a specific angle onto an ATR prism, generating evanescent waves that penetrate the sample through total reflection. Fourier transform is applied to extract the reflection coefficient and calculate the complex permittivity. Optical parameters are modeled and classified to achieve water sample classification (Fig.6-Fig.7). Next, the application of THz technology in marine non-destructive testing is introduced. This part is divided into three sections: non-destructive testing of ship hull fiberglass materials, protective coatings and paint layers, and PE pipes. Non-destructive testing of ship hull fiberglass materials (Fig.8, Fig.10-Fig.11) and PE materials (Fig.16-Fig.17) is carried out using THz time-domain spectroscopy (THz-TDS). The principle involves exciting THz pulses to pass through the sample, collecting transmitted and reflected signals. By sampling these signals in the time domain and applying Fourier transform, the data is converted into the frequency domain, creating imaging for internal structure and defect visualization. This process enables the non-destructive evaluation of internal features and defects. For the non-destructive testing of ship protective coatings and paint layers (Fig.12-Fig.14), time-domain THz technology is used to record the time delay and amplitude variations of reflected signals. Deconvolution techniques are applied to calculate the coating thickness, and stationary wavelet transform (SWT) is utilized to extract characteristic signals for internal defect identification. Finally, the application of THz technology in marine ecosystem monitoring is discussed. This includes the detection of microalgal and microbial metabolites (Fig.18-Fig.20) to assess the potential for ecological issues, such as red tide phenomena, and the detection of radioactive cesium ions in seawater (Fig.22). The use of THz waves in marine ecosystem monitoring offers a promising approach for early detection of ecological disruptions and contamination in aquatic environments. The results shown in Fig.18 and Fig.22 highlight the potential of THz technology in enhancing marine environmental monitoring, ensuring a safer and more sustainable marine ecosystem.Conclusions and Prospects THz technology has demonstrated substantial potential in various marine applications, particularly in pollution detection, material integrity assessment, and ecological monitoring. Its non-contact and non-destructive characteristics make it an ideal tool for safeguarding marine infrastructure and ecosystems. As THz technology continues to evolve, its applications in the marine field are expected to expand, offering more efficient and accurate methods for real-time monitoring and early warning systems. In the future, THz technology is poised to play a crucial role in marine resource protection, contributing to sustainable marine management and environmental conservation.

ObjectiveThe main objective of this study is to unravel the microscopic mechanisms underlying the adsorption-desorption processes of volatile organic compounds (VOCs), particularly ethanol, using graphene-based materials and metal-organic frameworks (MOFs) as adsorbents. Adsorption technologies have been widely adopted in view of their high efficiency and cost-effectiveness in the treatment of VOCs, but the underlying mechanisms surrounding these processes are not well understood, which poses a major obstacle to further improvement of adsorption rates. To address this challenge, we aim to utilize the unique capabilities of terahertz time-domain spectroscopy (THz-TDS) as an emerging tool to gain insight into the dynamic interactions between adsorbents and adsorbates.MethodsIn this study, THz-TDS was employed to monitor and track the real-time adsorption-desorption dynamics of ethanol on four different graphene-based materials (rGO, FG, GO, and GO-COOH) and three MOFs (ZIF-8, ZIF-67, and MOF-177). By adjusting the experimental temperature, the variation of spectral peaks (EP) in the THz-TDS signal was systematically recorded as an indicator of adsorption-desorption behavior. In this way, we analyzed the effect of temperature on the equilibrium state of the adsorption process and deepened our understanding of the mechanism of warming-triggered desorption. In addition,the effect of temperature on the equilibrium state of the adsorption process was analyzed and the understanding of the mechanism of warming-triggered desorption was deepened. In addition, the adsorption capacity of each material was quantitatively characterized using THz-TDS and the correctness of the characterization results was explained by the physicochemical properties of the adsorbents.Results and DiscussionsThe adsorption of graphene-based materials was in the order of QGO-COOH>QFG>QGO>QrGO, while the adsorption of MOFs was in the order of QZIF-8>QZIF-67>QMOF-177. In addition, the analysis of THz-TDS signals at different temperatures (Fig.5) confirmed the existence of a warming-triggered desorption mechanism, thus revealing the dynamic equilibrium of the adsorption process. The quantitative characterization of adsorption achieved by THz-TDS provides a solid foundation for comparing the performance of different adsorbents and exploring the underlying microscopic mechanism of their interaction with ethanol.ConclusionsIn this study, the effect of temperature on the adsorption-desorption process of ethanol by four graphene-based materials and three MOFs was analyzed using THz-TDS. During the adsorption process, the EP values of all seven adsorbent materials increased with the increase of temperature, which was attributed to the transformation of the ethanol molecules from the adsorbed state to the gaseous state, with the consequent breakage of the hydrogen bonds and the corresponding weakening of the absorption of terahertz waves. Meanwhile, the adsorption (26 ℃) and desorption processes (46 ℃, 66 ℃, and 76 ℃) of four graphene-based materials and three MOFs were tracked using the THz-TDS technique, and the parameter of adsorption capacity was indirectly assessed by calculating the difference in EP between 26 ℃ and 76 ℃. The correlation between adsorption capacity and adsorption capacity is shown in the following table. The correlation between the adsorbed amounts was QGO-COOH>QFG>QGO>QrGO and QZIF-8>QZIF-67>QMOF-177, respectively, which was verified by the information of polarity, SSA, pore size distribution, and surface functional groups. This study confirms the feasibility of using THz-TDS to track the adsorption-desorption process and to indirectly characterize the adsorption amount by monitoring the hydrogen bond breakage, which provides a valuable reference to achieve efficient treatment of VOCs.