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学者姓名：瞿芳芳
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Abstract ：
Background: The residual fluoroquinolone antibiotics (FQs) in the environment and food has raised public concerns over their potential impact on human health. Terahertz metamaterial sensors (TMSs) have garnered significant attention due to their capability to enhance the interaction between terahertz waves and antibiotic molecules, enabling the detection of trace antibiotics. However, conventional quantitative and qualitative methods based on TMSs suffer from low accuracy and cumbersome processes, respectively. Herein, this work proposed a novel approach that reconstructed optimal terahertz response features of different TMSs with machine learning algorithms, which allowed for analysis of three similar trace FQs with enhanced accuracy. Results: The prepared three patterned TMSs exhibited different resonance responses, which varied with changes in FQs types and concentrations. The resonance peak features of the three TMSs were fused to construct the resonance peak feature matrix (W0) and combined with the K-Nearest Neighbor (KNN) algorithm to build the W0-KNN classification model. The interval feature matrix was constructed by optimizing and expanding the resonance peak feature width. The optimal resonance peak interval feature matrix (Wt) was combined with Gaussian process regression (GPR) algorithms with different kernel functions to build the Wt-GPR quantitative model. The results showed that W0-KNN achieved 100 % classification accuracy for the three FQs. Wt-GPR exhibited high quantitative accuracy for all three FQs with the determination coefficient (R2) of 0.94-0.98, and root mean square error (RMSE) of 6.4085-10.6540. The results of Wt-GPR with different kernel functions had small fluctuations, demonstrating high stability in predictive performance. Significance: Reconstructing features from multi-TMSs in combination with machine learning algorithms enables rapid, precise, and reliable qualitative and quantitative analysis of trace FQs. Our research introduces innovative concepts and methodologies to detect trace FQs using TMS-based sensors, paving the way for future applications of TMS in the biomolecular sensing and detection.
Keyword ：
Biomolecular sensing Biomolecular sensing Fluoroquinolone antibiotics Fluoroquinolone antibiotics Machine learning Machine learning Terahertz metamaterials sensor Terahertz metamaterials sensor Trace detection Trace detection
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| GB/T 7714 | Zhang, Lintong , Kong, Xiangzeng , Wang, Shuhui et al. Resonance features integration of multiple terahertz metamaterials sensors for qualification and quantification of trace fluoroquinolone antibiotics [J]. | ANALYTICA CHIMICA ACTA , 2025 , 1345 . |
| MLA | Zhang, Lintong et al. "Resonance features integration of multiple terahertz metamaterials sensors for qualification and quantification of trace fluoroquinolone antibiotics" . | ANALYTICA CHIMICA ACTA 1345 (2025) . |
| APA | Zhang, Lintong , Kong, Xiangzeng , Wang, Shuhui , Zhang, Wenqing , Wu, Libin , Liu, Xinze et al. Resonance features integration of multiple terahertz metamaterials sensors for qualification and quantification of trace fluoroquinolone antibiotics . | ANALYTICA CHIMICA ACTA , 2025 , 1345 . |
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The development of efficient and accurate methods for detecting contamination in agri-foods is critical for ensuring food safety. Terahertz time-domain spectroscopy (THz-TDS), distinguished by its unique spectral characteristics and nondestructive detection capabilities, emerges as a powerful tool for analyzing agri-food safety. This review systematically examines the integration of THz-TDS with frontier technologies (machine learning [ML], metamaterials [MM], microfluidics [MF], and functional nanomaterials [FN]) to enhance detection capabilities. The article delves into the advancements achieved in detecting physical, chemical, and microbial contaminants in agri-food over the past five years (2020-2024) through the integration of THz-TDS with these frontier technologies. Based on the current state of research, this article summarizes the challenges and prospects of THz-TDS with interdisciplinary integration technologies in applications. To advance THz-TDS for agri-food safety monitoring, multidisciplinary integration is required. ML is critical for deciphering complex THz spectral datasets, while MM play a pivotal role in amplifying analyte-specific spectral signatures. FN leverage their potential high-throughput specific adsorption and plasmonic resonance properties to enhance detection sensitivity and specificity. The MF systems can reduce absorption induced by water. This review aims to provide new insights into the multidisciplinary convergence to propel THz-TDS toward transformative agri-food safety applications.
Keyword ：
Agri-food safety Agri-food safety interdisciplinary integration technologies interdisciplinary integration technologies nondestructive detection nondestructive detection terahertz spectroscopy terahertz spectroscopy
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| GB/T 7714 | Zhang, Lintong , Wang, Shuhui , Yang, Wangjincheng et al. A Comprehensive Review of Terahertz Time-Domain Spectroscopy for Agri-Food Safety Detection: Enhanced Sensing Performance Through Multidisciplinary Technology Integration [J]. | CRITICAL REVIEWS IN ANALYTICAL CHEMISTRY , 2025 . |
| MLA | Zhang, Lintong et al. "A Comprehensive Review of Terahertz Time-Domain Spectroscopy for Agri-Food Safety Detection: Enhanced Sensing Performance Through Multidisciplinary Technology Integration" . | CRITICAL REVIEWS IN ANALYTICAL CHEMISTRY (2025) . |
| APA | Zhang, Lintong , Wang, Shuhui , Yang, Wangjincheng , Liu, Xinze , Wei, Zenghui , Abdalla, Alwaseela et al. A Comprehensive Review of Terahertz Time-Domain Spectroscopy for Agri-Food Safety Detection: Enhanced Sensing Performance Through Multidisciplinary Technology Integration . | CRITICAL REVIEWS IN ANALYTICAL CHEMISTRY , 2025 . |
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Abstract ：
Ensuring food safety has become a paramount global challenge, impacting both public health and societal stability. Antibiotics are a class of bacteriostatic agents used to treat animal diseases. However, antibiotics accumulate in food and pose a hazard to human health through the food chain. Traditional antibiotic testing requires well-equipped laboratories and professionals, so it is necessary to develop convenient testing methods. Biosensors have the advantages of excellent detection performance, simple operation, and easy design, which provide great potential for on-site detection. In this paper, nanoparticles are categorized into metal nano-particles, carbon nanoparticles, polymer nanoparticles and compound nanoparticles based on their composition. We summarize the suitability of nanoparticles for different biosensors and the reasons for this based on their properties. The development of portable intelligent detection devices in the field of on-site detection is summarized and outlooked. Finally, the main challenges and future opportunities for biosensors are discussed, providing a clear future direction for the detection of antibiotics in food samples.
Keyword ：
Antibiotics Antibiotics Biosensors Biosensors Food safety Food safety Intelligence Intelligence Nanoparticles Nanoparticles Sensitivity Sensitivity
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| GB/T 7714 | Long, Bo , Zhang, Qian , Zhang, Lintong et al. Recent advances in nanomaterial biosensors for the detection of antibiotics to ensure food safety [J]. | CHEMICAL ENGINEERING JOURNAL , 2025 , 521 . |
| MLA | Long, Bo et al. "Recent advances in nanomaterial biosensors for the detection of antibiotics to ensure food safety" . | CHEMICAL ENGINEERING JOURNAL 521 (2025) . |
| APA | Long, Bo , Zhang, Qian , Zhang, Lintong , Xing, Qiongqiong , Liu, Qi , Deng, Liying et al. Recent advances in nanomaterial biosensors for the detection of antibiotics to ensure food safety . | CHEMICAL ENGINEERING JOURNAL , 2025 , 521 . |
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Dissolved Oxygen (DO) is a pivotal indicator for sustaining the vitality and productivity of aquatic ecosystems. To empower sophisticated aquaculture management, a novel approach of feature reconstruction integrated with deep neural networks was proposed to predict the future DO trends within fish pond aquaculture with exceptional precision and reliability. The time series data of water quality factors including pH, water temperature, conductivity, turbidity, air temperature, and humidity were obtained synchronously by sensing devices. The sequence of Spearman correlation analysis (SCA), variational mode decomposition (VMD), and convolutional neural networks (CNN) formed the feature reconstruction method (SCA-VMD-CNN, SVC) for feature optimization, decomposition, and spatiotemporal feature extraction, addressing the nonlinear and temporal features of DO data in aquaculture. The Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks were established based on the SVC features for multi-step predicting of DO. Compared with other state-of-the-art methods, the results showed that SVC effectively improved the accuracy of the DNNs by 16.8 %similar to 19.5 % for multi-step prediction of future DO trends within fish pond aquaculture. The SVC-BiGRU obtained the highest predictive performances with R-2 of 0.962, 0.934, 0.940 for predicting 1-step, 2-step, and 3-step DO content in the next 15, 30, and 45 min. Our proposed methodology paves a pathway toward dynamic monitoring of DO trends, aimed at improving aquaculture efficiency and reducing risks. It may play an essential role in the near future for time-series analysis in precision aquaculture.
Keyword ：
Deep neural networks Deep neural networks Dissolved oxygen Dissolved oxygen Fish pond aquaculture Fish pond aquaculture Multi-step prediction Multi-step prediction Time series feature reconstruction Time series feature reconstruction
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| GB/T 7714 | Jiang, Yilun , Zhang, Lintong , Wang, Chuxin et al. Multi-step prediction of dissolved oxygen in fish pond aquaculture using feature reconstruction-based deep neural network [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 232 . |
| MLA | Jiang, Yilun et al. "Multi-step prediction of dissolved oxygen in fish pond aquaculture using feature reconstruction-based deep neural network" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 232 (2025) . |
| APA | Jiang, Yilun , Zhang, Lintong , Wang, Chuxin , Chen, Linjie , Zhang, Wenqing , Weng, Haiyong et al. Multi-step prediction of dissolved oxygen in fish pond aquaculture using feature reconstruction-based deep neural network . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 232 . |
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The advancement of biosensing devices based on field effect transistor (FET) has been rapid, largely due to the simplicity of their operational mechanism, rapid response, ease of miniaturization, and integration. The preparation of field effect transistors using inorganic nanomaterials as channel materials has been extensively employed in biosensing applications, including assessing food quality and safety, environmental monitoring, and diagnosing biological diseases. The detection of disease-causing microorganisms, antibiotics, heavy metals, and harmful gases in modern agricultural breeding environments also necessitates the utilization of sensors that are able to achieving label-free, miniaturized, rapid, and specific detection. Biosensing devices based on field effect transistors are able to rapidly and specifically detect, meeting the needs of modern agricultural breeding environments for low-cost, accurate, miniaturized, and portable devices.
Keyword ：
Agricultural breeding environment Agricultural breeding environment Application prospect Application prospect Field effect transistor Field effect transistor Structure and principle Structure and principle
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| GB/T 7714 | Long, Bo , Xing, Qiongqiong , Zhang, Qian et al. Exploring field effect transistor sensing devices in agricultural breeding environment: application prospects [J]. | ADVANCED COMPOSITES AND HYBRID MATERIALS , 2025 , 8 (1) . |
| MLA | Long, Bo et al. "Exploring field effect transistor sensing devices in agricultural breeding environment: application prospects" . | ADVANCED COMPOSITES AND HYBRID MATERIALS 8 . 1 (2025) . |
| APA | Long, Bo , Xing, Qiongqiong , Zhang, Qian , Deng, Liying , Liu, Qi , Zhang, Lintong et al. Exploring field effect transistor sensing devices in agricultural breeding environment: application prospects . | ADVANCED COMPOSITES AND HYBRID MATERIALS , 2025 , 8 (1) . |
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Background: Pefloxacin (PEF), a widely used broad-spectrum antimicrobial, garners considerable scientific concern due to its inherent biotoxicity and potential risk of antibiotic resistance gene transfer. Current detection methods remain time-and labor-intensive, while exhibiting inadequate sensitivity for precise trace-level quantification. Targeted enhancement of the response signal of the target analyte is a key focus in biochemical molecular sensing detection. Terahertz metamaterials sensors (TMSs) are commonly used to enhance the terahertz (THz) spectral response signals of target analytes, but suffer from inherent specificity limitations, requiring complex surface chemical modifications. Results: This study designed a rotationally symmetric TMS (resonance peaks at 0.763 THz and 1.007 THz) targeting PEF fingerprint peaks (0.778 THz and 0.954 THz), achieving specificity-enhanced detection of PEF through physical resonance characteristics. To address low accuracy in conventional univariate regression models, an improved recursive feature elimination-multivariate linear regression (IRFE-MLR) algorithm framework was proposed. The designed TMS exhibited high resonance matching (S value = 98.5 % and 94.7 %) with PEF, and the IRFE-MLR model significantly enhanced trace detection performance (R2 = 0.95, RMSE = 9.36) compared to the univariate model (R2 = 0.82, RMSE = 19.31), achieving a detection limit of 5 ng/L and a mean recovery rate of 115.13 % (RSD = 5.91 %). Comparative tests of PEF, enrofloxacin (ENR), and nadifloxacin (NAD) revealed that the TMS exhibited superior sensitivity to PEF at 0.763 THz, attributed to its high Q-factor (sharp resonance) and S value. The former enhanced dielectric response sensitivity, while the latter strengthened near-field coupling for analyte-TMS interaction. Significance: This study provides a high-sensitivity and high-reliable PEF residue sensing strategy without complex surface modification and provides an extensible design paradigm for specificity-enhanced TMS. The proposed method can be extended to ultra-trace detection of other critical biochemical hazards, holding significant implications for environmental pollution control and food safety assurance.
Keyword ：
Pefloxacin Pefloxacin Resonance matching degree Resonance matching degree Specificity-enhanced Specificity-enhanced Terahertz metamaterials sensor Terahertz metamaterials sensor Trace detection Trace detection
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| GB/T 7714 | Zhang, Lintong , Yang, Jingsen , Zhang, Jiachen et al. Resonance-matched terahertz metamaterials integrated with IRFE-MLR framework enabling specificity-enhanced detection of trace pefloxacin [J]. | ANALYTICA CHIMICA ACTA , 2025 , 1374 . |
| MLA | Zhang, Lintong et al. "Resonance-matched terahertz metamaterials integrated with IRFE-MLR framework enabling specificity-enhanced detection of trace pefloxacin" . | ANALYTICA CHIMICA ACTA 1374 (2025) . |
| APA | Zhang, Lintong , Yang, Jingsen , Zhang, Jiachen , Yang, Wangjincheng , Liu, Xinze , Wang, Shuhui et al. Resonance-matched terahertz metamaterials integrated with IRFE-MLR framework enabling specificity-enhanced detection of trace pefloxacin . | ANALYTICA CHIMICA ACTA , 2025 , 1374 . |
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The residues of fluoroquinolone antibiotics (FQs) have attracted widespread attention due to their potential health risks. Given the high structural similarity among FQs, traditional methods for analyzing their mixed residues are often complex and inaccurate. This study proposed a method combining terahertz (THz) spectroscopy and machine learning for qualitative and quantitative analysis of multicomponent mixtures containing four FQs (nadifloxacin [NAD], pefloxacin [PEF], ofloxacin [OFL], and enrofloxacin [ENR]) at low mass ratios (0.067-0.333). For qualitative analysis, multi-step preprocessing (MP) and support vector machine (SVM) were integrated to develop the MP-SVM model. The MP-SVM achieved an average classification accuracy of 0.967 for the four FQs in test sets. While MP enabled high-resolution qualitative discrimination, it was insufficient for accurate quantification of complex FQ mixtures. Consequently, further optimizations of feature data and model parameters were conducted for quantitative analysis. Specifically, a high-quality feature matrix (T) was constructed by merging fingerprint features of each FQ. The sparrow search algorithm (SSA) was employed to optimize support vector regression (SVR) parameters, forming the MP-T-SSA-SVR model. This model significantly improved quantitative performance, with a coefficient of determination (R2) of 0.971-0.972, a root mean square error (RMSE) of 3.405-3.514, and a mean absolute error (MAE) of 2.090-2.400 in test sets. Compared to similar studies, this work involves more diverse FQs with higher structural similarity, providing a new reference for advancing qualitative and quantitative analysis of practical multicomponent mixtures.
Keyword ：
Fluoroquinolone antibiotics Fluoroquinolone antibiotics Multicomponent mixtures Multicomponent mixtures Qualitative and quantitative analysis Qualitative and quantitative analysis Similar molecular structures Similar molecular structures Terahertz spectroscopy Terahertz spectroscopy
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| GB/T 7714 | Zhang, Lintong , Liu, Xinze , Yang, Jingsen et al. Qualitative and Quantitative Analysis of Multivariate Mixed Fluoroquinolone Antibiotics Based on Terahertz Spectroscopy [J]. | JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES , 2025 , 46 (10) . |
| MLA | Zhang, Lintong et al. "Qualitative and Quantitative Analysis of Multivariate Mixed Fluoroquinolone Antibiotics Based on Terahertz Spectroscopy" . | JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES 46 . 10 (2025) . |
| APA | Zhang, Lintong , Liu, Xinze , Yang, Jingsen , Wang, Shuhui , Zhang, Jiachen , Yang, Wangjincheng et al. Qualitative and Quantitative Analysis of Multivariate Mixed Fluoroquinolone Antibiotics Based on Terahertz Spectroscopy . | JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES , 2025 , 46 (10) . |
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Trace amounts of antibiotics in water can accumulate in the human body through the food chain, posing significant health risks. Therefore, there is an urgent need to develop simple and effective methods for detecting antibiotics in water. In this study, we prepared electrochemical aptamer sensors based on carbon nanotubes@polystyrene sulfonate-gold nanoparticles/reduced graphene oxide (CNT@PSS-AuNPs/rGO) layered thin films for real-time, on-site detection of ciprofloxacin (CIP) in aquaculture environments, utilizing a portable sensing detection device. The CNT@PSS-AuNPs/rGO layered film offers an excellent specific surface area, providing ample binding sites for the aptamer. The functionalized CNT@PSS-AuNPs enhance the dispersibility and conductivity of the substrate material and increase the surface area of the electrode when loaded with rGO. Under optimal experimental conditions, the developed sensor exhibits a dynamic range from 4 ng/mL to 1.0 x 103 ng/mL and a limit of detection of 4 ng/mL (S/N = 3), demonstrating satisfactory sensitivity. The sensor also shows good stability, with a relative standard deviation of less than 1% after 100 repeated measurements. Moreover, when combined with a portable detection platform, CIP levels in aqueous environments can be analyzed intelligently, rapidly, and timely. Our study aims to promote simple and effective detection strategies, potentially extending their practical applications.
Keyword ：
CIP CIP Electrochemical sensor Electrochemical sensor Layered film Layered film Portability Portability Reduced graphene oxide Reduced graphene oxide
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| GB/T 7714 | Long, Bo , Zhang, Qian , Zhang, Lintong et al. Sensors based on CNT@PSS-AuNPs/rGO layered films for portable detection of ciprofloxacin [J]. | ADVANCED COMPOSITES AND HYBRID MATERIALS , 2025 , 8 (1) . |
| MLA | Long, Bo et al. "Sensors based on CNT@PSS-AuNPs/rGO layered films for portable detection of ciprofloxacin" . | ADVANCED COMPOSITES AND HYBRID MATERIALS 8 . 1 (2025) . |
| APA | Long, Bo , Zhang, Qian , Zhang, Lintong , Liu, Qi , Xing, Qiongqiong , Qu, Fangfang et al. Sensors based on CNT@PSS-AuNPs/rGO layered films for portable detection of ciprofloxacin . | ADVANCED COMPOSITES AND HYBRID MATERIALS , 2025 , 8 (1) . |
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【目的】通过基于信号序列优化机器听觉模型的研究，为蜂群健康与活动状态的监测提供依据。【方法】在蜂箱内设置音频传感器，以非侵入性和无干扰性的方式持续记录6类蜂群音频，针对传统的音频分类方法中未考虑时序信息和分类准确度不高等问题，提出一种基于双向长短期记忆(bidirectional long short-term memory, BiLSTM)网络优化的多分类模型。基于梅尔频率倒谱系数提取音频特征，并构建以BiLSTM为基准的蜂群状态分类模型；引入卷积神经网络(convolutional neural network, CNN)和自注意力机制(self-attention mechanism, SA)对BiLSTM的输入和输出进行优化；构建优化的CNN-BiLSTM-SA模型用于6类蜂群状态的精准识别。【结果】与CNN和BiLSTM模型相比，CNN-BiLSTM-SA模型的分类准确率最高，训练集和验证集准确率均大于0.990 0,测试集准确率为0.988 6,交叉验证平均准确率为0.981 5。【结论】CNN-BiLSTM-SA模型为蜂箱内蜂群状态精准识别提供了有效技术支持，有助于未来智能养蜂和音频传感监控的发展。
Keyword ：
卷积神经网络 卷积神经网络 双向长短期记忆 双向长短期记忆 机器听觉 机器听觉 自注意力机制 自注意力机制 蜂群状态 蜂群状态
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| GB/T 7714 | 叶大鹏 , 陈林杰 , 张林通 et al. 基于信号序列优化的蜂群状态精准识别机器听觉模型 [J]. | 福建农林大学学报(自然科学版) , 2025 , 54 (02) : 268-278 . |
| MLA | 叶大鹏 et al. "基于信号序列优化的蜂群状态精准识别机器听觉模型" . | 福建农林大学学报(自然科学版) 54 . 02 (2025) : 268-278 . |
| APA | 叶大鹏 , 陈林杰 , 张林通 , 张雯清 , 魏增辉 , 黄少康 et al. 基于信号序列优化的蜂群状态精准识别机器听觉模型 . | 福建农林大学学报(自然科学版) , 2025 , 54 (02) , 268-278 . |
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Water is essential for life on Earth, but increased human activity has led to water pollution. The international community has been highly concerned about this issue, making it crucial to develop methods for detecting water pollutants. Recently, electrochemical sensors have emerged as effective tools for detecting environmental pollutants in water. Covalent organic frameworks (COFs), a quickly developing type of crystalline linked organic polymers, possess highly structured frameworks, significant specific surface areas, durable chemical properties, and customizable pore microenvironments, which confer great versatility. Notably, electrochemical sensors based on COFs have garnered significant attention due to their outstanding analytical performance. Herein, a comprehensive overview of the basic characteristics is provided and synthesis techniques of COFs in the field of electrochemical detection are widely used, emphasizing their role in the development of electrochemical biosensors and high response detection devices. The design principles, preparation methods, and detection mechanisms of COF-based electrochemical sensors are also detailed. Recent scientific advancements have been examined, highlighting the application of COF as a functional material in electrochemical sensors for detecting various water pollutants, such as antibiotics, heavy metals, insecticides, bacteria, and fungi. Additionally, the challenges and future development prospects of COF-based electrochemical detection technology have been outlined.
Keyword ：
antibiotics antibiotics covalent organic frameworks covalent organic frameworks electrochemical sensors electrochemical sensors heavy metal ions heavy metal ions pesticides pesticides
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| GB/T 7714 | Long, Bo , Liu, Qi , Zhang, Qian et al. Analysis and Study of Covalent Organic Frameworks in Electrochemical Sensors for Water Environment Pollutant Detection [J]. | SMALL STRUCTURES , 2025 , 6 (9) . |
| MLA | Long, Bo et al. "Analysis and Study of Covalent Organic Frameworks in Electrochemical Sensors for Water Environment Pollutant Detection" . | SMALL STRUCTURES 6 . 9 (2025) . |
| APA | Long, Bo , Liu, Qi , Zhang, Qian , Xing, Qiongqiong , Deng, Liying , Qu, Fangfang et al. Analysis and Study of Covalent Organic Frameworks in Electrochemical Sensors for Water Environment Pollutant Detection . | SMALL STRUCTURES , 2025 , 6 (9) . |
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