A Visible-Light-Driven Self-Powered Nodularin-R Biosensing Platform Controlled by an Integrated Portable Photoelectrochemical Detection Device.

The presence of nodularin-R (NOD-R) in water has gained considerable attention because of its widespread distribution and high toxicity. In this study, an accurate and rapid visible-light-driven self-powered photoelectrochemical (PEC) biosensor was developed by integrating a portable paper-based electrode with a custom-built miniaturized PEC detection device. The newly designed system successfully achieved on-site detection of NOD-R in real water samples based on PEC technology. First, target recognition triggers the initiation of the hybridization chain reaction to generate double-stranded DNA. The thus-formed double-stranded DNA entrapped methylene blue (MB), and the dye molecules were irradiated with visible light for conversion to leuco-MB in the presence of ascorbic acid. The resulting leuco-MB species significantly amplified the PEC signal output of TiO2-MXene, enabling NOD-R detection. Under optimal conditions, the proposed PEC assay strategy demonstrated NOD-R detection within a concentration range from 20 fg mL-1 to 10 ng mL-1 with a detection limit of 19.6 fg mL-1. In addition, a custom-built miniaturized PEC detection device conveniently integrates the detection component with the light source, enabling the real-time collection of results via a wireless module. This innovative self-powered PEC platform provides significant advancements in smooth and intelligent detection compared to traditional PEC detection devices.

Upconversion Nanoparticles Based Sensing: From Design to Point-of-Care Testing.

Rare earth-doped upconversion nanoparticles (UCNPs) have achieved a wide range of applications in the sensing field due to their unique anti-Stokes luminescence property, minimized background interference, excellent biocompatibility, and stable physicochemical properties. However, UCNPs-based sensing platforms still face several challenges, including inherent limitations from UCNPs such as low quantum yields and narrow absorption cross-sections, as well as constraints related to energy transfer efficiencies in sensing systems. Therefore, the construction of high-performance UCNPs-based sensing platforms is an important cornerstone for conducting relevant research. This work begins by providing a brief overview of the upconversion luminescence mechanism in UCNPs. Subsequently, it offers a comprehensive summary of the sensors' types, design principles, and optimized design strategies for UCNPs sensing platforms. More cost-effective and promising point-of-care testing applications implemented based on UCNPs sensing systems are also summarized. Finally, this work addresses the future challenges and prospects for UCNPs-based sensing platforms.

Functionalized metal-organic frameworks with biomolecules for sensing and detection applications of food contaminants.

The increasing demand for toxin-free food, driven by the rise in fast food consumption and changing dietary habits, necessitates advanced and efficient detection methods to address the potential risks associated with contaminated food. Nanomaterial-based detection methods have shown significant promise, particularly using metal-organic frameworks (MOFs) combined with biomolecules. This review article provides an overview of recent advancements in using functionalized metal-organic frameworks (FMOFs) with biomolecules to detect various food contaminants, including heavy metals, antibiotics, pesticides, bacteria, mycotoxins and other chemical contaminants. We discuss the fundamental principles of detecting food contaminants, evaluate existing analytical techniques, and explore the development of biomacromolecule-functionalized MOF-based sensors encompassing colorimetric, optical, electrochemical, and portable variants. The review also examines sensing mechanisms, uses FMOFs as signal probes and carriers for capture probes, and assesses sensitivity. Additionally, we explore the opportunities and challenges in producing FMOFs with biomacromolecules for food contaminant assessment. Future directions include improving sensor sensitivity and specificity, developing more cost-effective production methods, and integrating these technologies into real-world food safety monitoring systems. This work aims to pave the way for innovative and reliable solutions to ensure the safety of our food supply.

Ratiometric fluorescence method comprising carbon dots and rhodamine 6G encapsulated in metal-organic framework microcubes for curcumin detection.

A ratiometric fluorescence method comprising carbon dots (CDs) and rhodamine 6G (Rh-6G) encapsulated in the microcubes of metal-organic framework (MOF-5) is introduced for the sensitive detection of curcumin (Cur) in condiments. CDs@MOF-5@Rh-6G, synthesized by the adsorption of Rh-6G on MOF-5 embedded with CDs, showed two distinct emission peaks at 435 and 560 nm under excitation at 335 nm, and could be used for Cur detection by ratiometric fluorescence. In the presence of Cur, the fluorescence of the CDs at 435 nm (F435) was quenched by Cur owing to internal filtering and dynamic quenching effects, whereas the emission of Rh-6G at 560 nm (F560) remained unchanged (335 nm is the excitation wavelength, 435 and 560 nm are the emission wavelengths, in which F435/F560 values are used as the output results). Under optimal conditions, a linear relationship was observed between the Cur concentration (in the range 0.1-5 µmol/L) and F435/F560 value for CDs@MOF-5@Rh-6G, with a detection limit of 15 nmol/L. Notably, the proposed method could accurately detect Cur in mustard, curry, and red pepper powders. Therefore, this study could improve the quality control of food and facilitate the development of sensitive ratiometric fluorescence probes.

Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning.

BACKGROUND: It is important to monitor and control the moisture content throughout the Tencha drying processing procedure so that its quality is ensured. Workers often rely on their senses to perceive the moisture content, leading to relative subjectivity and low reproducibility. Traditional drying methods, which are used for measuring moisture content, are destructive to samples. This research was conducted using computer vision combined with deep learning to detect moisture content during the Tencha drying process. Different color space components of Tencha drying sample images were first extracted by computer vision. The color components were preprocessed using MinMax and Z score. Subsequently, one-dimensional convolutional neural networks (1D-CNN), partial least squares, and backpropagation artificial neural networks models were built and compared. RESULTS: The 1D-CNN model and Z score preprocessing achieved superior predictive accuracy, with correlation coefficient of prediction (Rp) = 0.9548 for moisture content. The migration of moisture content during the Tencha drying process was eventually visualized by mapping its spatial and temporal distributions. CONCLUSION: The results indicated that computer vision combined with 1D-CNN was feasible for moisture prediction during the Tencha drying process. This study provides technical support for the industrial and intelligent production of Tencha. © 2024 Society of Chemical Industry.

Time series prediction of insect pests in tea gardens.

BACKGROUND: Tea-garden pest control is crucial to ensure tea quality. In this context, the time-series prediction of insect pests in tea gardens is very important. Deep-learning-based time-series prediction techniques are advancing rapidly but research into their use in tea-garden pest prediction is limited. The current study investigates the time-series prediction of whitefly populations in the Tea Expo Garden, Jurong City, Jiangsu Province, China, employing three deep-learning algorithms, namely Informer, the Long Short-Term Memory (LSTM) network, and LSTM-Attention. RESULTS: The comparative analysis of the three deep-learning algorithms revealed optimal results for LSTM-Attention, with an average root mean square error (RMSE) of 2.84 and average mean absolute error (MAE) of 2.52 for 7 days' prediction length, respectively. For a prediction length of 3 days, LSTM achieved the best performance, with an average RMSE of 2.60 and an average MAE of 2.24. CONCLUSION: These findings suggest that different prediction lengths influence model performance in tea garden pest time series prediction. Deep learning could be applied satisfactorily to predict time series of insect pests in tea gardens based on LSTM-Attention. Thus, this study provides a theoretical basis for the research on the time series of pest and disease infestations in tea plants. © 2024 Society of Chemical Industry.

ZnO@Ag-Functionalized Paper-Based Microarray Chip for SERS Detection of Bacteria and Antibacterial and Photocatalytic Inactivation.

Bacterial infections caused by pathogenic microorganisms have become a serious, widespread health concern. Thus, it is essential and required to develop a multifunctional platform that can rapidly and accurately determine bacteria and effectively inhibit or inactivate pathogens. Herein, a microarray SERS chip was successfully synthesized using novel metal/semiconductor composites (ZnO@Ag)-ZnO nanoflowers (ZnO NFs) decorated with Ag nanoparticles (Ag NPs) arrayed on a paper-based chip as a supporting substrate for in situ monitoring and photocatalytic inactivation of pathogenic bacteria. Typical Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli and Vibrio parahemolyticus were selected as models. Partial least-squares discriminant analysis (PLS-DA) was performed to minimize the dimensionality of SERS spectra data sets and to develop a cost-effective identification model. The classification accuracy was 100, 97.2, and 100% for S. aureus, E. coli, and V. parahemolyticus, respectively. The antimicrobial activity of ZnO@Ag was proved by the microbroth dilution method, and the minimum inhibitory concentrations (MICs) of S. aureus, E. coli, and V. parahemolyticus were 40, 50, and 55 µg/mL, respectively. Meanwhile, it demonstrated remarkable photocatalytic performance under natural sunlight for the inactivation of pathogenic bacteria, and the inactivation rates for S. aureus, E. coli, and V. parahemolyticus were 100, 97.03 and 97.56%, respectively. As a result, the microarray chip not only detected the bacteria with high sensitivity but also confirmed the antibacterial and photocatalytic sterilization properties. Consequently, it offers highly prospective strategies for foodborne diseases caused by pathogenic bacteria.

Insight into a Target-Induced Photocurrent-Polarity-Switching Photoelectrochemical Immunoassay for Ultrasensitive Detection of Escherichia coli O157:H7.

Sensitive, portable methods of detection for foodborne pathogens hold great significance for the early warning and prevention of foodborne diseases and environmental pollution. Restricted by a complicated matrix and limited signaling strategies, developing a ready-to-use sensing platform with ultrahigh sensitivity remains challenging. In this work, near-infrared (NIR) light-responsive AgBiS2 nanoflowers (NFs) and Cu2O nanocubes (NCs) were introduced to construct a novel target-induced photocurrent-polarity-switchable system and verified for the development of an all-in-one, ready-to-use photoelectrochemical (PEC) immunosensor. NIR-responsive n-type AgBiS2 NFs and p-type Cu2O NCs producing anodic and cathodic photocurrents were conjugated with monoclonal (MAb1) and polyclonal antibodies (PAb2), respectively. Using a sandwich-type immunocomplex bridged by Escherichia coli O157:H7, an efficient photocurrent-polarity-switching PEC system was formed on a paper-based working electrode (PWE). Owing to the spatial separation of the photogenerated carriers and the elimination of false-positive/negative signals by the polarity-switchable photocurrent, the proposed NIR PEC immunoassay for E. coli O157:H7 exhibits a considerably low detection limit of 8 colony-forming units/milliliter (CFU/mL) with a linear range from 25 to 5 × 107 CFU/mL. The platform includes a PWE with an automatic cleaning function and a portable PEC analyzer with smartphone-compatible Bluetooth capability, thus achieving point-of-care testing of E. coli O157:H7. The sensor was applied to the analysis of pork samples artificially contaminated with E. coli O157:H7, and the detection results were in good agreement with the plate counting method, a gold standard in the field. This work aimed to investigate the photoelectric activity of the NIR-responsive p/n-type composites and to provide a new signal-reversal route for the construction of an all-in-one ready-to-use PEC immunosensor for the detection of low-concentration biomolecules.

A Collection of RPA-Based Photoelectrochemical Assays for the Portable Detection of Multiple Pathogens.

Portable, ultrasensitive, and simultaneously quantitative detection of the nucleic acids of multiple foodborne pathogens is critical to public health. However, the current testing methods depend on costly equipment and tedious amplification steps. In this study, we propose a photoelectrochemical (PEC) biosensor combined with recombinase polymerase amplification (RPA) technology (RPA-PEC) for the rapid detection of multiple foodborne pathogens under irradiation of 980 nm light. In particular, two working surfaces were designed on homemade three-dimensional screen-printed paper-based electrodes. The genomic DNAs of Escherichia coli O157:H7 and Staphylococcus aureus was initiated by RPA on the corresponding electrode surfaces, thus forming a lab-on-paper platform. Using the formed DNA-PEC signaler, photocurrents were achieved at 37 °C after only 20 min of RPA. The detection performance was superior to that of conventional agarose gel electrophoresis, with detection limits of 3.0 and 7.0 copies/µL for E. coli O157:H7 and S. aureus, respectively. Our study pioneers a new RPA-PEC method for foodborne pathogens and provides directions for the construction of lab-on-paper platforms for the portable detection of multiple nucleic acids.

Recent advances of nanomaterial-based optical sensor for the detection of benzimidazole fungicides in food: a review.

The abuse of pesticides in agricultural land during pre- and post-harvest causes an increase of residue in agricultural products and pollution in the environment, which ultimately affects human health. Hence, it is crucially important to develop an effective detection method to quantify the trace amount of residue in food and water. However, with the rapid development of nanotechnology and considering the exclusive properties of nanomaterials, optical, and their integrated system have gained exclusive interest for accurately sensing of pesticides in food and agricultural samples to ensure food safety thanks to their unique benefit of high sensitivity, low detection limit, good selectivity and so on and making them a trending hotspot. This review focuses on recent progress in the past five years on nanomaterial-based optical, such as colorimetric, fluorescence, surface-enhanced Raman scattering (SERS), and their integrated system for the monitoring of benzimidazole fungicide (including, carbendazim, thiabendazole, and thiophanate-methyl) residue in food and water samples. This review firstly provides a brief introduction to mentioned techniques, detection mechanism, applied nanomaterials, label-free detection, target-specific detection, etc. then their specific application. Finally, challenges and perspectives in the respective field are discussed.

Recent advancement in nano-optical strategies for detection of pathogenic bacteria and their metabolites in food safety.

Pathogenic bacteria and their metabolites are the leading risk factor in food safety and are one of the major threats to human health because of the capability of triggering diseases with high morbidity and mortality. Nano-optical sensors for bacteria sensing have been greatly explored with the emergence of nanotechnology and artificial intelligence. In addition, with the rapid development of cross fusion technology, other technologies integrated nano-optical sensors show great potential in bacterial and their metabolites sensing. This review focus on nano-optical strategies for bacteria and their metabolites sensing in the field of food safety; based on surface-enhanced Raman scattering (SERS), fluorescence, and colorimetric biosensors, and their integration with the microfluidic platform, electrochemical platform, and nucleic acid amplification platform in the recent three years. Compared with the traditional techniques, nano optical-based sensors have greatly improved the sensitivity with reduced detection time and cost. However, challenges remain for the simple fabrication of biosensors and their practical application in complex matrices. Thus, bringing out improvements or novelty in the pretreatment methods will be a trend in the upcoming future.

Overview of advanced technologies for volatile organic compounds measurement in food quality and safety.

Food quality and nutrition have received much attention in recent decades, thanks to changes in consumer behavior and gradual increases in food consumption. The demand for high-quality food necessitates stringent quality assurance and process control measures. As a result, appropriate analytical tools are required to assess the quality of food and food products. VOCs analysis techniques may meet these needs because they are nondestructive, convenient to use, require little or no sample preparation, and are environmentally friendly. In this article, the main VOCs released from various foods during transportation, storage, and processing were reviewed. The principles of the most common VOCs analysis techniques, such as electronic nose, colorimetric sensor array, migration spectrum, infrared and laser spectroscopy, were discussed, as well as the most recent research in the field of food quality and safety evaluation. In particular, we described data processing algorithms and data analysis captured by these techniques in detail. Finally, the challenges and opportunities of these VOCs analysis techniques in food quality analysis were discussed, as well as future development trends and prospects of this field.

A review of biomolecules conjugated lanthanide up-conversion nanoparticles-based fluorescence probes in food safety and quality monitoring applications.

Upconversion nanoparticles (UCNPs) are known to possess unique characteristics, which allow them to overcome a number of issues that plague traditional fluorescence probes. UCNPs have been employed in a variety of applications, but it is arguably in the realm of optical sensors where they have shown the most promise. Biomolecule conjugated UCNPs-based fluorescence probes have been developed to detect and quantify a wide range of analytes, from metal ions to biomolecules, with great specificity and sensitivity. In this review, we have given much emphasis on the recent trends and progress in the preparation strategies of bioconjugated UCNPs and their potential application as fluorescence sensors in the trace level detection of food industry-based toxicants and adulterants. The paper discusses the preparation and functionalisation strategies of commonly used biomolecules over the surface of UCNPs. The use of different sensing strategies namely heterogenous and homogenous assays, underlying fluorescence mechanisms in the detection process of food adulterants are summarized in detail. This review might set a precedent for future multidisciplinary research including the development of novel biomolecules conjugated UCNPs for potential applications in food science and technology.

Ultrasensitive detection of Staphylococcus aureus using a non-fluorescent cDNA-grafted dark BBQ®-650 chromophore integrated hydrophilic upconversion nanoparticles/aptamer system.

A highly structured fluorometric bioassay has been proposed for screening Staphylococcus aureus (S. aureus). The study exploits (i) the spectral attributes of the hexagonal NaYF4:Yb,Er upconversion nanoparticle (UCNP)-coated 3-aminopropyl)triethoxysilane; (ii) the intrinsic non-fluorescent quenching features of the highly stable dark blackberry (BBQ®-650) receptor; (iii) the aptamer (Apt-) biorecognition and binding affinity, and (iv) the complementary DNA hybridizer-linkage efficacy. The principle relied on the excited state energy transfer between the donor Apt-labeled NH2-UCNPs at the 3' end, and cDNA-grafted BBQ®-650 at the 5' end, as the effective receptors. The donor moieties in proximity (< 10.0 nm) trigger hybridization with the cDNA-grafted dark BBQ®-650, as the receptors of energy from the 2F5/2 level of Yb3+ ions to initiate the Förster resonance energy transfer pathway. This was confirmed by the decline in the excited-state lifetimes from 223.52 µs (τ1) to 179.26 µs (τ2). The existence of the target S. aureus in the bioassay attracts the Apt- resulting in the detachment of the acceptor, and disintegration of the complex configuration via conformation reversal. The re-activated fluorescence monitored at λex/em = 980/652 nm, as a function of the logarithmic concentration of S. aureus (42 to 4.2 × 108 CFU mL-1), yielded an ultra-low detection response of 2.0 CFU mL-1. The bioassay screening of S. aureus in real samples revealed satisfactory recoveries (92.44-107.82%) and validation results (p > 0.05). Hence, the comprehensive Apt-labeled NH2-UCNPs-cDNA-grafted dark BBQ®-650 bioassay offered fast and precise S. aureus screening in food and environmental settings.

Highly Sensitive ß-Lactoglobulin Fluorescent Aptamer Biosensors Based on Tungsten Disulfide Nanosheets and DNase I-Assisted Signal Amplification.

ß-lactoglobulin (ß-Lg) is a protein found in milk that can cause severe allergic reactions, including rash, vomiting, and diarrhea. Thus, it is crucial to develop a sensitive ß-Lg detection method to protect people who are susceptible to allergies. Here, we introduce a novel and highly sensitive fluorescent aptamer biosensor for detecting ß-Lg. First, a fluorescein-based dye (FAM)-labeled ß-lactoglobulin aptamer (ß-Lg aptamer) is adsorbed on the surface of tungsten disulfide (WS2) nanosheets via van der Waals forces, resulting in fluorescence quenching. When ß-Lg is present, the ß-Lg aptamer selectively binds to ß-Lg, causing a conformational change in the ß-Lg aptamer and releasing it from the surface of WS2 nanosheets, which restores the fluorescence signal. Simultaneously, DNase I in the system cleaves the aptamer bound to the target, producing a short oligonucleotide fragment and releasing ß-Lg. The released ß-Lg then binds to another ß-Lg aptamer adsorbed on WS2, initiating the next round of cleavage, resulting in significant amplification of the fluorescence signal. This method has a linear detection range of 1-100 ng mL-1, and the limit of detection is 0.344 ng mL-1. Furthermore, this approach has been successfully used for detecting ß-Lg in milk samples with satisfactory results, providing new opportunities for food analysis and quality control.

Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system.

BACKGROUND: The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables. RESULTS: The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59. CONCLUSION: The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

Application of colorimetric sensor array coupled with machine-learning approaches for the discrimination of grains based on freshness.

BACKGROUND: Volatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible-near-infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine-learning-based models - for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm - were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies. RESULTS: Compared with the pattern recognition results of image processing, visible-near-infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples. CONCLUSION: The method developed could be used for non-destructive detection of grain freshness. © 2023 Society of Chemical Industry.

Advances in surface-enhanced Raman spectroscopy technology for detection of foodborne pathogens.

Rapid control and prevention of diseases caused by foodborne pathogens is one of the existing food safety regulatory issues faced by various countries and has received wide attention from all sectors of society. The development of rapid and reliable detection methods for foodborne pathogens remains a hot research area for food safety and public health because of the limitations of complex steps, time-consuming, low sensitivity, or poor selectivity of commonly used methods. Surface-enhanced Raman spectroscopy (SERS), as a novel spectroscopic technique, has the advantages of high sensitivity, selectivity, rapid and nondestructive detection and has exhibited broad application prospects in the determination of pathogenic bacteria. In this study, the enhancement mechanisms of SERS are briefly introduced, then the characteristics and properties of liquid-phase, rigid solid-phase, and flexible solid-phase are categorized. Furthermore, a comprehensive review of the advances in label-free or label-based SERS strategies and SERS-compatible techniques for the detection of foodborne pathogens is provided, and the advantages and disadvantages of these methods are reviewed. Finally, the current challenges of SERS technology applied in practical applications are listed, and the possible development trends of SERS in the field of foodborne pathogens detection in the future are discussed.

Recent advances in rare earth ion-doped upconversion nanomaterials: From design to their applications in food safety analysis.

The misuse of chemicals in agricultural systems and food production leads to an increase in contaminants in food, which ultimately has adverse effects on human health. This situation has prompted a demand for sophisticated detection technologies with rapid and sensitive features, as concerns over food safety and quality have grown around the globe. The rare earth ion-doped upconversion nanoparticle (UCNP)-based sensor has emerged as an innovative and promising approach for detecting and analyzing food contaminants due to its superior photophysical properties, including low autofluorescence background, deep penetration of light, low toxicity, and minimal photodamage to the biological samples. The aim of this review was to discuss an outline of the applications of UCNPs to detect contaminants in food matrices, with particular attention on the determination of heavy metals, pesticides, pathogenic bacteria, mycotoxins, and antibiotics. The review briefly discusses the mechanism of upconversion (UC) luminescence, the synthesis, modification, functionality of UCNPs, as well as the detection principles for the design of UC biosensors. Furthermore, because current UCNP research on food safety detection is still at an early stage, this review identifies several bottlenecks that must be overcome in UCNPs and discusses the future prospects for its application in the field of food analysis.

Development of cobalt oxyhydroxide-aptamer-based upconversion sensing nano-system for the rapid detection of Staphylococcus aureus.

Staphylococcus aureus (S. aureus) is a common pathogen that is dangerous to humans' health. Herein, a novel upconversion fluorescent biosensor based on fluorescence resonance energy transfer from aptamer-labeled upconversion nanoparticles (UCNPs-apt) as donor and cobalt oxyhydroxide (CoOOH) nanosheets as acceptor was designed to detect S. aureus in complex matrices. The principle of the work relies on fluorescence resonance energy transfer as UCNPs-apt can self-assemble on CoOOH nanosheet surfaces by van der Waals forces to effectively quench the fluorescence. When S. aureus was added, the aptamer was able to preferentially capture the target, resulting in the dissociation of donor and acceptor and the recovery of fluorescence. The structure and morphology of the nanostructures were assigned in detail by a series of characterizations, and the energy transfer mechanism was evaluated by time-resolved lifetime measurements. Under the optimal conditions, a linear calibration plot was obtained in a concentration range of 45-4.5 × 106 CFU/mL with a limit of detection of 15 CFU/mL. In addition, the proposed biosensor was used for S. aureus detection in real samples (e.g., pork, beef), and the detection result showed no significant difference (p > 0.05) compared with the conventional plate count approach. Hence, the fabricated biosensor holds a potential application for S. aureus in food analysis and public health.