An electrochemiluminescent dual-signal immunosensor based on a Ru(bpy)32+-doped silica nanoparticle/copper nanocluster composite for okadaic acid detection.

In this study, a sensitive dual-signal electrochemiluminescence (ECL) immunosensor was developed for okadaic acid (OA) detection utilizing copper nanoclusters (CuNCs) and Ru(bpy)32+-doped silica nanoparticles (RuSiNPs). Interestingly, the CuNCs could simultaneously enhance both cathodic (-0.95 V) and anodic (+1.15 V) ECL signals of RuSiNPs, forming a dual-signal ECL sensing platform. Further, RuSiNPs@CuNCs were used as immunomarkers by covalently conjugating them with an anti-OA monoclonal antibody (mAb) to form probes. Finally, dual ECL signals of the immunosensor were fabricated and showed good linear relationships with OA concentrations in the range of 0.05-70 ng mL-1, having a median inhibitory concentration (IC50) of 1.972 ng mL-1 and a limit of detection of 0.039 ng mL-1. Moreover, the constant ratio of the cathodic and anodic ECL peaks achieved self-calibration of the detection signal and improved the reliability of the results. Finally, we successfully applied the ECL sensor to detect OA in spiked oyster samples.

Multifunctional Metal-Organic Frameworks Driven Three-Dimensional Folded Paper-Based Microfluidic Analysis Device for Chlorpyrifos Detection.

Chlorpyrifos (CPF) residues in food pose a serious threat to ecosystems and human health. Herein, we propose a three-dimensional folded paper-based microfluidic analysis device (3D-µPAD) based on multifunctional metal-organic frameworks, which can achieve rapid quantitative detection of CPF by fluorescence-colorimetric dual-mode readout. Upconversion nanomaterials were first coupled with a bimetal organic framework possessing peroxidase activity to create a fluorescence-quenched nanoprobe. After that, the 3D-µPAD was finished by loading the nanoprobe onto the paper-based detection zone and spraying it with a color-developing solution. With CPF present, the fluorescence intensity of the detection zone gradually recovers, the color changes from colorless to blue. This showed a good linear relationship with the concentration of CPF, and the limits of detection were 0.028 (fluorescence) and 0.043 (colorimetric) ng/mL, respectively. Moreover, the 3D-µPAD was well applied in detecting real samples with no significant difference compared with the high-performance liquid chromatography method. We believe it has huge potential for application in the on-site detection of food hazardous substance residues.

Quantification of antibiotics in food by octahedral gold-silver nanocages-based SERS sensor coupling multivariate calibration.

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu2O template etching method as SERS substrates, which enhanced the plasmonic effect through sharp edges and hollow nanostructures. Afterwards, five chemometric algorithms, like partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), competitive adaptive reweighted sampling-PLS (CARS-PLS), random frog-PLS (RF-PLS), and convolutional neural network (CNN) were applied for TTC and CAP. RF-PLS performed optimally for TTC and CAP (Rc = 0.9686, Rp = 0.9648, RPD = 3.79 for TTC and Rc = 0.9893, Rp = 0.9878, RPD = 5.88 for CAP). Furthermore, the detection limit of 0.0001 µg/mL for both TTC and CAP was obtained. Finally, satisfactory (p > 0.05) results were obtained with the standard HPLC method. Therefore, SERS combined RF-PLS could be applied for fast, nondestructive sensing of TTC and CAP in milk.

Upconversion fluorescence nanosensor based on enzymatic inhibited and copper-triggered o-phenylenediamine oxidation for the detection of dimethoate pesticides.

Pesticide residues in agricultural products pose a significant threat to human health. Herein, a sensitive fluorescence method employing upconversion nanoparticles was developed for detecting organophosphorus pesticides (OPs) based on the principle of enzyme inhibition and copper-triggered o-phenylenediamine (OPD) oxidation. Copper ions (Cu2+) oxidized the colorless OPD to a yellow 2,3-diaminophenazine (oxOPD). The yellow solution oxOPD quenched the fluorescence of upconversion nanoparticles due to the fluorescence resonance energy transfer. The high affinity of Cu2+ for thiocholine reduced the level of oxOPD, resulting in almost no fluorescence quenching. The addition of dimethoate led to the inhibition of acetylcholinesterase activity and thus prevented the formation of thiocholine. Subsequently, Cu2+ oxidized OPD to form oxOPD, which attenuated the fluorescence signal of the system. The detection system has a good linear range of 0.01 ng/mL to 50 ng/mL with a detection limit of 0.008 ng/mL, providing promising applications for rapid detection of dimethoate.

Rapid qualitative and quantitative analysis of benzo(b)fluoranthene (BbF) in shrimp using SERS-based sensor coupled with chemometric models.

Benzo(b)fluoranthene (BbF), a polycyclic aromatic hydrocarbon (PAH), is a carcinogenic contaminant of concern in seafood. This study developed a simple, rapid, sensitive, and cost-effective surface-enhanced Raman scattering (SERS) sensor (AuNPs) coupled with chemometric models for detecting BbF in shrimp samples. Partial least squares (PLS) regression models were optimized using uninformative variable elimination (UVE), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS). Qualitative analysis was performed using principal component analysis (PCA), linear discriminant analysis (LDA), and k-nearest neighbors (KNN) to differentiate between BbF-contaminated and uncontaminated shrimp samples. The SERS-sensor exhibited excellent sensitivity (LOD = 0.12 ng/mL), repeatability (RSD = 6.21%), and anti-interference performance. CARS-PLS model demonstrated superior predictive ability (R2 = 0.9944), and qualitative analysis discriminated between contaminated and uncontaminated samples. The sensor's accuracy was validated using HPLC, demonstrating the ability of the SERS-sensor coupled with chemometrics to rapidly and reliably detect BbF in shrimp samples.

Generalized ratiometric surface-enhanced Raman scattering biosensor for okadaic acid in food based on Au-triggered signal amplification.

BACKGROUND: Reliability and robustness have been recognized as key challenges for Surface-enhanced Raman scattering (SERS) analytical techniques. Quantifying the concentration of an analyte using a single characteristic peak from SERS has been a controversial topic because the Raman signal is susceptible to highly concentrated electromagnetic hotspots, inhomogeneity of SERS substrate, or non-standardization of measurement conditions. Ratiometric SERS strategies have been demonstrated as a promising solution to effectively balance and compensate for signal fluctuations caused by matrix heterogeneity. However, it is not easy to construct ratiometric SERS sensors with monitoring the ratio of two different signal intensities for target analysis. RESULTS: An attempt has been made to develop a novel ratiometric biosensor that can be applied to detect okadaic acid (OA). Aptamer-anchored magnetic particles were first combined with gold-tagged short complementary DNA (Au-cDNA) to create heterogeneous nanostructures. When the target was present, the Au-cDNA was dissociated from nanostructures, and 4-nitrothiophenol (4-NTP) was initiated to reduce to 4-aminothiophenol (4-ATP) in the presence of hydrogen sources. The SERS ratio change of 4-NTP and 4-ATP was finally detected by AuNPs-coated film. OA was successfully quantified, and the detection limit was as low as 2.4524 ng/mL. The constructed biosensor had good stability and reproducibility with a relative standard deviation of less than 4.47%. The proposed method used gold nanoparticles as an intermediate to achieve catalytic signal amplification and subsequently increased the sensitivity of the biosensor. SIGNIFICANCE AND NOVELTY: Catalytic reaction-based ratiometric SERS biosensors combine the multiple advantages of catalytic signal amplification and signal self-calibration and provide new insights into the development of stable, reproducible, and reliable SERS detection techniques. This ratiometric SERS technique offered a universal method that is anticipated to be applicable for the detection of other targets by substituting the aptamer.

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.

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates.

This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.

Near-infrared spectroscopy based on colorimetric sensor array coupled with convolutional neural network detecting zearalenone in wheat.

Wheat is a vital global cereal crop, but its susceptibility to contamination by mycotoxins can render it unusable. This study explored the integration of two novel non-destructive detection methodologies with convolutional neural network (CNN) for the identification of zearalenone (ZEN) contamination in wheat. Firstly, the colorimetric sensor array composed of six selected porphyrin-based materials was used to capture the olfactory signatures of wheat samples. Subsequently, the colorimetric sensor array, after undergoing a reaction, was characterized by its near-infrared spectral features. Then, the CNN quantitative analysis model was proposed based on the data, alongside the establishment of traditional machine learning models, partial least squares regression (PLSR) and support vector machine regression (SVR), for comparative purposes. The outcomes demonstrated that the CNN model had superior predictive performance, with a root mean square error of prediction (RMSEP) of 40.92 µ g ∙ kg-1 and a coefficient of determination on the prediction (RP2) of 0.91. These results affirmed the potential of integrating colorimetric sensor array with near-infrared spectroscopy in evaluating the safety of wheat and potentially other grains. Moreover, CNN can have the capacity to autonomously learn and distill features from spectral data, enabling further spectral analysis and making it a forward-looking spectroscopic tool.

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.

Alizarin-embedded γ-cyclodextrin-based metal-organic framework in a methylcellulose/polyvinyl alcohol film for maintaining and monitoring grass carp freshness.

Multifunctional packaging films that monitor and maintain fish freshness hold significant potential for use in the food industry. This study introduces a multifunctional intelligent packaging film comprising alizarin (ALI)-embedded cubic γ-cyclodextrin metal-organic frameworks (γ-CD-MOFs) (denoted as γ-CD-MOFs@ALI) in a methylcellulose/polyvinyl alcohol (MP)-based matrix to achieve colorimetric monitoring and enhanced preservation of fish freshness. The MP/γ-CD-MOFs@ALI reveals a rapid color transition in 3 min from yellow color progressively darkens to purple as the pH increases from 2.0 to 10.0. And it is proved that the as-prepared film owns high antibacterial activity against Gram-positive bacteria (S. aureus), impressive ABTS+ radical scavenging rates of 85.54 ± 1.25 %, and effective ALI sustained-release properties. The intelligent packaging film exhibits an excellent colorimetric response to total volatile basic nitrogen and provides exceptional freshness preservation performance, effectively prolonging the shelf life of Ctenopharyngodon idella (grass carp) under 25 °C to 42 h.

Isothermal Reciprocal Catalytic DNA Circuit for Sensitive Analysis of Kanamycin.

Inappropriate use of veterinary drugs can result in the presence of antibiotic residues in animal-derived foods, which is a threat to human health. A simple yet efficient antibiotic-sensing method is highly desirable. Programmable DNA amplification circuits have supplemented robust toolkits for food contaminants monitoring. However, they currently face limitations in terms of their intricate design and low signal gain. Herein, we have engineered a robust reciprocal catalytic DNA (RCD) circuit for highly efficient bioanalysis. The trigger initiates the cascade hybridization reaction (CHR) to yield plenty of repeated initiators for activating the rolling circle amplification (RCA) circuit. Then the RCA-generated numerous reconstituted triggers can reversely stimulate the CHR circuit. This results in a self-sufficient supply of numerous initiators and triggers for the successive cross-invasion of CHR and RCA amplifiers, thus leading to exponential signal amplification for the highly efficient detection of analytes. With its flexible programmability and modular features, the RCD amplifier can serve as a universal toolbox for the high-performance and accurate sensing of kanamycin in buffer and food samples including milk, honey, and fish, highlighting its enormous promise for low-abundance contaminant analysis in foodstuffs.

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.

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.

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.

Metal-organic framework-based multienzyme cascade bioreactor for sensitive detection of methyl parathion.

In this study, a cascade nanobioreactor was developed for the highly sensitive detection of methyl parathion (MP) in food samples. The simultaneous encapsulation of acetylcholinesterase (AChE) and choline oxidase (CHO) in a zeolitic imidazole ester backbone (ZIF-8) effectively improved the stability and cascade catalytic efficiency of the enzymes. In addition, glutathione-stabilized gold nanoclusters (GSH-AuNCs) were encapsulated in ZIF-8 by ligand self-assembly, conferring excellent fluorescence properties. Acetylcholine (ATCh) is catalyzed by a cascade of AChE/CHO@ZIF-8 as well as Fe(II) to generate hydroxyl radicals (·OH) with strong oxidizing properties. The ·OH radicals then oxidize Au(0) in GSH-AuNCs@ZIF-8 to Au(I), resulting in fluorescence quenching. MP, as an inhibitor of AChE, hinders the cascade reaction and thus restores the fluorescence emission, enabling its quantitative detection. The limit of detection of the constructed nanobioreactor for MP was 0.23 µg/L. This MOF-based cascade nanobioreactor has great potential for the detection of trace hazards.

A dual-mode fluorescence and colorimetric sensing platform for efficient detection of ofloxacin in aquatic products using iron alkoxide nanozyme.

Trace detection of ofloxacin (OFL) with high sensitivity, reliability, and visual clarity is challenging. To address this, a novel dual-modal aptasensor with fluorescence-colorimetric capabilities was designed that exploit the target-induced release of 3,3',5,5'-tetramethylbenzidine (TMB) molecules from aptamer-gated mesoporous silica nanoparticles (MSNs), the oxidase-like activity of iron alkoxide (IA) nanozyme, and the fluorescence attributes of core-shell upconversion nanoparticles. Therefore, the study reports a dual mode detection, with a fluorescence detection range for OFL spanning from 0.1 µg/kg to 1000 µg/kg (and a detection limit of 0.048 µg/kg). Additionally, the colorimetric method offered a linear detection range of 0.3 µg/kg to 1000 µg/kg, with a detection limit of 0.165 µg/kg. The proposed biosensor had been successfully applied to the determination of OFL content in real samples with satisfactory recoveries (78.24-96.14 %).

Enhanced detection of acrylamide using a versatile solid-state upconversion sensor through spectral and visual analysis.

Acrylamide (AM) generally forms in high-temperature processes and has been classified as a potential carcinogen. In this study, we put forward a maneuverable solid-state luminescence sensor using polydimethylsiloxane (PDMS) as the matrix coupled with upconversion nanoparticles as the indicator. The core-shell upconversion nanoparticles emitting cyan light were uniformly encapsulated in PDMS. Then it was further modified with complementary DNA of AM aptamer. The nanocrystalline fluorescein isothiocyanate isomer (FITC), coupled with AM aptamer, was attached to the surface of PDMS. FITC effectively quenched the upconversion luminescence through fluorescence resonance energy transfer (FRET). The introduction of AM resulted in preferentially bound to aptamer caused the separation of the quencher and the donor, and led to luminescence recovery. The developed sensor was applied for both spectral and visual monitoring, demonstrating a detection limit (LOD) of 1.00 nM and 1.07 nM, respectively. Importantly, in the actual foodstuffs detection, there is no obvious difference between the results of this study and the standard method, which indicates the developed method has good accuracy. Therefore, this solid-state sensor has the potential for on-site detection using a smartphone device and an Android application.

Application of colorimetric sensor array coupled with chemometric methods for monitoring the freshness of snakehead fillets.

Total volatile basic nitrogen content (TVB-N) is an important index of freshness for snakehead. This paper attempted the feasibility of determining TVB-N content level in snakehead fillets by a colorimetric sensor array (CSA) composed of twelve porphyrin materials and eight pH indicators. The nine feature variables in RGB, HSV and CIE L*a*b* color spaces were obtained by differentiating the images of the CSA before and after exposure to the headspace-gas of the samples. Competitive adaptive reweighted sampling combined with partial least squares regression (CARS-PLS) was used to build the relationship between the TVB-N content and the feature variables of CSA, and to select meaningful color-sensitive materials. The results showed that CARS-PLS had a correlation coefficient of 0.9325 in the prediction set and selected 13 informative color-sensitive materials. This study demonstrated that the CSA with CARS-PLS algorithm could be used successfully to quantify and monitor the TVB-N in snakehead fillets.

Ag@Au core-shell nanoparticle-based surface-enhanced Raman scattering coupled with chemometrics for rapid determination of chloramphenicol residue in fish.

The alarming increase in drug-resistant bacteria in fish resulting from the misuse of antibiotics poses a significant threat to ecosystems and human health. Therefore, the development of a reliable approach for detecting antibiotic residues in fish is crucial. In this study, a rapid and simple method for detecting chloramphenicol (CAP) residue in tilapia was developed using surface-enhanced Raman scattering (SERS) combined with chemometric algorithms. Silver and gold core-shell nanoparticles (Ag@Au CSNPs) were used as SERS nanosensors to achieve strong signal amplification with an enhancement factor of 2.67 × 106. The results demonstrated that the variable combination population analysis-partial least square (VCPA-PLS) model combined with the standard normal variable transformation pretreatment method exhibited the best predictive performance with a detection limit of 1 × 10-5 µg/mL. Thus, an SERS technique was established based on Ag@Au CSNPs combined with VCPA-PLS to rapidly detect CAP in tilapia.