A Sensor Array Based on Molecularly Imprinted Polymers and Machine Learning for the Analysis of Fluoroquinolone Antibiotics

Development of HPLC-DAD method for the separation and simultaneous determination of ten antibiotic residues in food and environmental samples using surface floating organic droplet-based air-assisted liquid-liquid micro-extraction

A high-performance liquid chromatography with diode-array detection (HPLC-DAD) method has been developed for the separation and simultaneous determination of ten multiclass antibiotics residues in various samples, including river water, irrigation dam water, hospital wastewater, cow urine, milk and sediment samples. For each antibiotics, the method exhibited strong linearity, with a coefficient of determination falling between 0.9982 and 0.9999. The ranges for the limits of detection and quantification were 0.07-0.15 and 0.26-0.49 μg/L, respectively. Good precision of the procedure was indicated by the repeatability and reproducibility studies, which ranged in % RSD from 1.03-3.78 and 2.03-5.10, respectively. The method has achieved a good recovery within the range of 87.3-100.8 %, which is acceptable. Additionally, a surface floating organic droplets-air assisted liquid-liquid micro extraction (SFOD-AALLME) has been optimized for the extraction of ten antibiotic residues from the various real samples. For all the multiclass antibiotic residues under study, the developed pre-concentration method (SFOD-AALLME) has proven its linearity, accuracy, and precision. Therefore, multiclass antibiotic residues could be selectively pre-concentrated, extracted, and determined in the cow urine and raw milk, sediment, environmental and wastewater samples, as well as in the samples with similar matrices.

Novel Europium-Grafted 3D Covalent Organic Framework for Selective and Sensitive Fluorescence-Enhanced Detection of Levofloxacin

Levofloxacin (LVFX), a fluoroquinolone antibacterial agent widely used in treating bacterial infections, poses significant risks when overused, necessitating the development of reliable and efficient detection methods. Herein, we introduce Eu@SUZ-103, a novel europium-grafted three-dimensional covalent organic framework (COF) featuring an eight-connected bcu net, for the selective detection of LVFX in serum and urine. Its 3D architecture facilitates rapid LVFX diffusion to luminescent sites, producing notably enhanced fluorescence and high sensitivity. Evaluations in complex biological matrices revealed excellent performance encompassing a broad linear range (5-2000 μM) and a low detection limit. Altogether, Eu@SUZ-103 extends the practical scope of 3D COFs in fluorescence-based sensing, offering a robust platform for accurate, efficient, and selective LVFX monitoring in clinical and environmental applications.

A ratiometric fluorescence sensor with different responsive modes based on carbon dots-embedded Tb-MOFs for the determination of norfloxacin and levofloxacin

Norfloxacin (NOR) and levofloxacin (LEV) are the two most frequently used fluoroquinolones (FQs) in clinic. Their residues seriously endanger the ecosystem and human health. Due to their similarity in structure and properties, it is urgent to develop an efficient and sensitive strategy for detection and differentiation. Herein, we synthesized a novel ratiometric fluorescent sensor for the first time by combining N, S co-doped carbon dots (CDs) and the precursors of Tb-MOFs through a facile one-pot method. The introduction of CDs effectively facilitated the energy transfer between Tb3+ and FQs, overcoming the limitation that single Tb-MOFs could not identify similar antibiotics. Specifically, the presence of NOR resulted in reverse signal response through the inner filter effect and antenna effect. The synergistic effect of these two mechanisms contributed to achieving signal amplification accompanied by a distinguishable color transition. The limit of detection (LOD) was 0.036 μM. Different from NOR, the addition of LEV reduced the electron density of the system, weakened the coordination ability of Tb3+ with LEV, and induced a single signal response with Tb3+ fluorescence intensity as a reference signal (LOD = 0.383 μM). Furthermore, the method proved to be rapid and visual, allowing for the straightforward analysis of FQs residues in water, food matrices, and biological samples with satisfactory precision. By integrating N, S-CDs@Tb-MOFs with flexible substrates, the paper-based sensor facilitated the visual quantitative determination of FQs by reading RGB values. The developed sensor presents a promising strategy for the identification and real-time monitoring of antibiotics.

Electrochemical Sensing of Phenylalanine using Polyaniline-Based Molecularly Imprinted Polymers

Polyaniline (PANI)-based molecularly imprinted polymers were investigated for their efficacy in sensing phenylalanine (Phe) when fabricated on both glassy carbon electrode (GCE) and indium tin oxide (ITO) sheets. This study highlights the superior performance of PANI-MIP/ITO over PANI-MIP/GCE for sensing Phe, with clear and distinct redox responses. Molecular computation helps to understand the interaction mechanism between PANI and Phe, where molecular crowding, aggregated clusters, hydrogen bonding, and π-π stacking facilitate stable interactions. We tested the specificity of Phe sensing by PANI-MIP with different amino acids such as cysteine, tryptophan, and tyrosine as well as organic molecules such as ascorbic acid, allantoin, sucrose, and urea, confirming its remarkable electrochemical efficiency. The oxidation response curve yielded a limit of detection of 4.88 μM and a limit of quantification of 16.3 μM, comparable to or better than earlier reported sensors. This work demonstrates the promise of MIP-based electrochemical sensing. It also lays the groundwork for future investigations into optimizing PANI-MIPs with nanocomposites to develop more selective and stable sensors.

Deep-Learning-Guided Electrochemical Impedance Spectroscopy for Calibration-Free Pharmaceutical Moisture Content Monitoring

The moisture content of pharmaceutical powders can significantly impact the physical and chemical properties of drug formulations, solubility, flowability, and stability. However, current technologies for measuring moisture content in pharmaceutical materials require extensive calibration processes, leading to poor consistency and a lack of speed. To address this challenge, this study explores the feasibility of using impedance spectroscopy to enable accurate, rapid testing of moisture content of pharmaceutical materials with minimal to zero calibration. By utilizing electrochemical impedance spectroscopy (EIS) signals, we identify a strong correlation between the electrical properties of the materials and varying moisture contents in pharmaceutical samples. Equivalent circuit modeling is employed to unravel the underlying mechanism, providing valuable insights into the sensitivity of impedance spectroscopy to moisture content variations. Furthermore, the study incorporates deep learning techniques utilizing a 1D convolutional neural network (1DCNN) model to effectively process the complex spectroscopy data. The proposed model achieved a notable predictive accuracy with an average error of just 0.69% in moisture content estimation. This method serves as a pioneering study in using deep learning to provide a reliable solution for real-time moisture content monitoring, with potential applications extending from pharmaceuticals to the food, energy, environmental, and healthcare sectors.

Wearable, Biocompatible, and Dual-Emission Ocular Multisensor Patch for Continuous Profiling of Fluoroquinolone Antibiotics in Tears

The abuse or misuse of antibiotics in clinical and agricultural settings severely endangers human health and ecosystems, which has raised profound concerns for public health worldwide. Trace detection and reliable discrimination of commonly used fluoroquinolone (FQ) antibiotics and their analogues have consequently become urgent to guide the rational use of antibiotic medicines and deliver efficient treatments for associated diseases. Herein, we report a wearable eye patch integrated with a quadruplex nanosensor chip for noninvasive detection and discrimination of primary FQ antibiotics in tears during routine eyedrop treatment. A set of dual-mode fluorescent nanoprobes of red- or green-emitting CdTe quantum dots integrated with lanthanide ions and a sensitizer, adenosine monophosphate, were constructed to provide an enhanced fluorescence up to 45-fold and nanomolar sensitivity toward major FQs owing to the aggregation-regulated antenna effect. The aggregation-driven, CdTe-Ln(III)-based microfluidic sensor chip is highly specific to FQ antibiotics against other non-FQ counterparts or biomolecular interfering species and is able to accurately discriminate nine types of FQ or non-FQ eyedrop suspensions using linear discriminant analysis. The prototyped wearable sensing detector has proven to be biocompatible and nontoxic to human tissues, which integrates the entire optical imaging modules into a miniaturized, smartphone-based platform for field use and reduces the overall assay time to ∼5 min. The practicability of the wearable eye patch was demonstrated through accurate quantification of antibiotics in a bactericidal event and the continuous profiling of FQ residues in tears after using a typical prescription antibiotic eyedrop. This technology provides a useful supplement to the toolbox for on-site and real-time examination and regulation of inappropriate daily drug use that might potentially lead to long-term antibiotic abuse and has great implications in advancing personal healthcare techniques for the regulation of daily medication therapy.

Pattern Recognition of Alkaloids by Inhibiting the Catalytic Activity of Dopzymes for Dopamine

Exploiting the specific recognition probe for all of the biomolecules is difficult in "lock-and-key" biosensors. The cross-reaction or the semispecific probe in pattern recognition mode is an alternative strategy through extracting a multidimensional signal array from recognition elements. Here, we design a pattern recognition sensor array based on the alkaloid-inhibited catalytic activity of dopzymes for the discrimination and determination of six alkaloids. In this sensor array, three different G-rich sequences, i.e., G-triplex (G3), G-quadruplex (GQ1), and the G-quadruplex dimer (2GQ1) possessing various peroxidase activities, conjugated with a dopamine aptamer and the dopzymes (G3-d-apt, GQ1-d-apt, and 2GQ1-d-apt) were obtained with an enhanced catalytic performance for the substrate. Through the interactions between six target alkaloids and G3, GQ1, and 2GQ1 regions, the pattern signal (6 alkaloids × 3 dopzymes × 5 replicates) was obtained from the diverse inhibited effect for the dopzyme activity. In virtue of the statistical method principal component analysis (PCA), the data array was projected into a new dimensional space to acquire the three-dimensional (3D) canonical scores and grouped into their respective clusters. The sensor array exhibited an outstanding discrimination and classification capability for six alkaloids with different concentrations with 100% accuracy. In addition, the nonspecific recognition elements of the sensor array showed high selectivity even though other alkaloids with similar structures to targets existed in the samples. Importantly, the levels of the six targets can be analyzed by the most influential discrimination factor, which represented the vector with the highest variance, evidencing that the sensor array has potential in drug screening and clinical treatment.

Integrating target-responsive microfluidic-based biosensing chip with smartphone for simultaneous quantification of multiple fluoroquinolones

The presence of fluoroquinolone (FQs) antibiotic residues in the food and environment has become a significant concern for human health and ecosystems. In this study, the background-free properties of upconversion nanoparticles (UCNPs), the high specificity of the target aptamer (Apt), and the high quenching properties of graphene oxide (GO) were integrated into a microfluidic-based fluorescence biosensing chip (MFBC). Interestingly, the microfluidic channels of the MFBC were prepared by laser-printing technology without the need for complex preparation processes and additional specialized equipment. The target-responsive fluorescence biosensing probes loaded on the MFBC were prepared by self-assembly of the UCNPs-Apt complex with GO based on π-π stacking interactions, which can be used for the detection of the two FQs on a large scale without the need for multi-step manipulations and reactions, resulting in excellent multiplexed, automated and simultaneous sensing capabilities with detection limits as low as 1.84 ng/mL (enrofloxacin) and 2.22 ng/mL (ciprofloxacin). In addition, the MFBC was integrated with a smartphone into a portable device to enable the analysis of a wide range of FQs in the field. This research provides a simple-to-prepare biosensing chip with great potential for field applications and large-scale screening of FQs residues in the food and environment.

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning

Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors.

The Effect of Polymerization Techniques on the Creation of Molecularly Imprinted Polymer Sensors and Their Application on Pharmaceutical Compounds

Molecularly imprinted polymers (MIPs) have become more prevalent in fabricating sensor applications, particularly in medicine, pharmaceuticals, food quality monitoring, and the environment. The ease of their preparation, adaptability of templates, superior affinity and specificity, improved stability, and the possibility for downsizing are only a few benefits of these sensors. Moreover, from a medical perspective, monitoring therapeutic medications and determining pharmaceutical compounds in their pharmaceutical forms and biological systems is very important. Additionally, because medications are hazardous to the environment, effective, quick, and affordable determination in the surrounding environment is of major importance. Concerning a variety of performance criteria, including sensitivity, specificity, low detection limits, and affordability, MIP sensors outperform other published technologies for analyzing pharmaceutical drugs. MIP sensors have, therefore, been widely used as one of the most crucial techniques for analyzing pharmaceuticals. The first part of this review provides a detailed explanation of the many polymerization techniques that were employed to create high-performing MIP sensors. In the subsequent section of the review, the utilization of MIP-based sensors for quantifying the drugs in their pharmaceutical preparation, biological specimens, and environmental samples are covered in depth. Finally, a critical evaluation of the potential future research paths for MIP-based sensors clarifies the use of MIP in pharmaceutical fields.

Exploring the potential of molecularly imprinted polymers and metal/metal oxide nanoparticles in sensors: recent advancements and prospects

Metal/metal oxide nanoparticles have gained increasing attention in recent years due to their outstanding features, including optical and catalytic properties, as well as their excellent conductivity. The implementation of metal/metal oxide nanoparticles, combined with molecularly imprinted polymers (MIPs) has paved the way for a new generation of building blocks to engineer and enhance the fascinating features of advanced sensors. This review critically evaluates the impact of combining metal/metal oxide nanoparticles with MIPs in sensors. It covers synthesis strategies, advantages of coupling these materials with MIPs, and addresses questions about the selectivity of these hybrid materials. In the end, the current challenges and future perspectives of this field are discussed, with a particular focus on the potential applications of these hybrid composites in the sensor field. This review highlights the exciting opportunities of using metal/metal oxide nanoparticles along with MIPs for the development of next-generation sensors.

Molecularly Imprinted Polymer Interface on Screen-Printed ZnO Nanorod Field Effect Transistors for Serotonin Detection in Clinical Samples

Ultrasensitive detection of serotonin is crucial for the early diagnosis of several diseases like Parkinson's and Alzheimer's. Most of the existing detection strategies are still not suitable for sensitive point-of-care applications. This study presents direct molecular imprinting of serotonin on the surface of three-dimensional zinc oxide (ZnO) nanorod devices connected in a field effect transistor (FET) configuration to achieve ultrasensitive, real-time, and rapid detection with a convenient and affordable approach, which has significant potential for translation to clinical settings. This strategy has enabled pushing the detection limit to 0.1 fM in a physiological analyte in real time with screen-printed electrodes, thereby resulting in the convenient batch fabrication of sensors for clinical validation. The response of the sensor with the clinical sample has been correlated with that of the gold standard and has been observed to be statistically similar.

Surface imprinted-covalent organic frameworks for efficient solid-phase extraction of fluoroquinolones in food samples

Molecularly imprinting on covalent organic frameworks (MI-COF) is a promising way to prepare selective adsorbents for effective extraction of fluoroquinolones (FQs). However, the unstable framework structure and complex imprinting process are challenging for the construction of MI-COF. Here, we report a facile surface imprinting approach with dopamine to generate imprinted cavities on the surface of irreversible COF for highly efficient extraction of FQs in food samples. The irreversible-linked COF was fabricated from hexahydroxytriphenylene and tetrafluorophthalonitrile to ensure COF stability. Moreover, the introduction of dopamine surface imprinted polymer into COF provides abundant imprinted sites and endows excellent selectivity for FQs recognition against other antibiotics. Taking enrofloxacin as a template molecule, the prepared MI-COF gave an exceptional adsorption capacity of 581 mg g-1, a 2.2-fold enhancement of adsorption capacity compared with nonimprinted COF. The MI-COF was further explored as adsorbent to develop a novel solid-phase extraction method coupled with high-performance liquid chromatography for the simultaneous determination of enrofloxacin, norfloxacin and ciprofloxacin. The developed method gave the low limits of detection at 0.003-0.05 ng mL-1, high precision with relative standard deviations less than 3.5%. The recoveries of spiked FQs in food samples ranged from 80.4% to 110.7%.

One Stone Two Birds: An Upconversion Nanosensor for Sensitive Detection of Fluoroquinolones in Aquatic Products Based on Chelation Recognition

Excessive residues of fluoroquinolones (FQs) in aquatic products have become a growing issue in recent years. Herein, we demonstrate an upconversion fluorescence nanosensor constructed by a one-stone-two-birds strategy, where Fe3+ not only quenches upconversion fluorescence with high efficiency but also specifically recognizes the bidentate ligand structure of FQs. Compared to existing methods, the proposed sensor is simpler to synthesize and cheap and has more storage stability due to the unification of the quencher and recognition molecule. Enrofloxacin (ENR) was chosen as a representative veterinary drug for FQs to verify the effectiveness of the nanosensor. Under optimal conditions, the range of detection for ENR was 2.0 × 10-2 to 2.0 × 102 μg/mL, with a limit of detection of 1.08 × 10-3 μg/mL. The developed nanosensor was further validated by high-performance liquid chromatography-ultraviolet (HPLC-UV) without significant differences in practical detection. Hence, this study offers a potential strategy for the detection of FQs.

Machine-learning-based predictions of imprinting quality using ensemble and non-linear regression algorithms

The molecularly imprinted polymers are artificial polymers that, during the synthesis, create specific sites for a definite purpose. These polymers due to their characteristics such as stability, easy of synthesis, reproducibility, reusability, high accuracy, and selectivity have many applications. However, the variety of the functional monomers, templates, solvents, and synthesis conditions like pH, temperature, the rate of stirring, and time, limit the selectivity of imprinting. The Practical optimization of the synthetic conditions has many drawbacks, including chemical compound usage, equipment requirements, and time costs. The use of machine learning (ML) for the prediction of the imprinting factor (IF), which indicates the quality of imprinting is a very interesting idea to overcome these problems. The ML has many advantages, for example a lack of human error, high accuracy, high repeatability, and prediction of a large amount of data in the minimum time. In this research, ML was used to predict the IF using non-linear regression algorithms, including classification and regression tree, support vector regression, and k-nearest neighbors, and ensemble algorithms, like gradient boosting (GB), random forest, and extra trees. The data sets were obtained practically in the laboratory, and inputs, included pH, the type of the template, the type of the monomer, solvent, the distribution coefficient of the MIP (K_MIP), and the distribution coefficient of the non-imprinted polymer (K_NIP). The mutual information feature selection method was used to select the important features affecting the IF. The results showed that the GB algorithm had the best performance in predicting the IF, and using this algorithm, the maximum R² value (R² = 0.871), and the minimum mean absolute error (MAE = - 0.982), and mean square error were obtained (MSE = - 2.303).