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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

G-Quadruplex/Hemin-Mediated Polarity-Switchable and Photocurrent-Amplified System for Escherichia coli O157:H7 Detection.

The contamination of food by pathogens is a serious problem in global food safety, and current methods of detection are costly, time-consuming, and cumbersome. Therefore, it is necessary to develop rapid, portable, and sensitive assays for foodborne pathogens. In addition, assays for foodborne pathogens must be resistant to interference resulting from the complex food matrix to prevent false positives and negatives. In this study, hemin and reduced graphene oxide-MoS2 sheets (GMS) were used to design a near-infrared (NIR)-responsive photoelectrochemical (PEC) aptasensor with target-induced photocurrent polarity switching based on a hairpin aptamer (Hp) with a G-quadruplex motif. A ready-to-use analytical device was developed by immobilizing GMS on the surface of a commercial screen-printed electrode, followed by the attachment of the aptamer. In the presence of Escherichia coli O157:H7, the binding sites of Hp with the G-quadruplex motif were opened and exposed to hemin, leading to the formation of a G-quadruplex/hemin DNAzyme. Crucially, after binding to hemin, the charge transfer pathway of GMS changes, resulting in a switch of the photocurrent polarity. Further, G-quadruplex/hemin DNAzyme enhanced the cathodic photocurrent, and the proposed sensor exhibited a wide linear range ((25.0-1.0) × 107 CFU/mL), a low limit of detection (2.0 CFU/mL), and good anti-interference performance. These findings expand the applications of NIR-responsive PEC materials and provide versatile PEC methods for detecting biological analytes, especially for food safety testing.

Widely targeted metabolomics reveals the effect of different raw materials and drying methods on the quality of instant tea.

Introduction: Instant teas are particularly rich in tea polyphenols and caffeine and have great potential as food ingredients or additives to improve the quality of food and enhance their nutritional and commercial value. Methods: To determine the relationships between raw material, drying method, and sensory and other quality attributes, instant teas were prepared from three tea varieties, namely black, green and jasmine tea, using two drying methods, namely spray-drying (SD) and freeze-drying (FD). Results: Both the raw tea material and drying method influenced the quality of the finished instant teas. Black tea was quality stable under two drying, while green tea taste deteriorated much after SD. Jasmine tea must be produced from FD due to huge aroma deterioration after SD. FD produced instant tea with higher sensory quality, which was attributed to the lower processing temperature. Chemical compositional analysis and widely targeted metabolomics revealed that SD caused greater degradation of tea biochemical components. The flavonoids content changed markedly after drying, and metabolomics, combined with OPLS-DA, was able to differentiate the three varieties of tea. Instant tea preparations via SD often lost a large proportion of the original tea aroma compounds, but FD minimized the loss of floral and fruity aroma compounds. Changes in the tea flavonoids composition, especially during drying, contributed to the flavor development of instant tea. Discussion: These results will provide an practicle method for high-quality instant tea production through choosing proper raw tea material and lowering down drying temperature with non-thermal technologies like FD.

Determination of procymidone residues in rapeseed oil based on olfactory visualization technology.

A new means about olfactory visualization technique for the quantitative analysis of procymidone residues in rapeseed oil has been proposed. First, an olfactory visualization system was set up to collect volatile odor information from rapeseed oil samples containing different concentrations of procymidone residues. Then, we utilized four intelligent optimization algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO) and simulated annealing (SA), to optimize the characteristics of the sensors. Finally, support vector machine regression (SVR) models employing optimized features were constructed for the quantitative detection of procymidone residues in rapeseed oil. The study demonstrated that the SA-SVR model demonstrated superior prediction results, achieving a high determination coefficient of prediction (RP2) at 0.9894. As indicated by the results, it is possible to successfully conduct non-destructive detection of procymidone residues in edible oil by the olfactory visualization technology.

Fe-MOF-based fluorescent sensor with on/off capabilities for the highly sensitive detection of tert-butylhydroquinone in edible oils.

In this work, a "turn off-on" fluorescent sensor was developed for highly sensitive determination of tert-butylhydroquinone (TBHQ) based on an Fe(III)-based metal-organic framework (Fe-MOF). An Fe-MOF with an octahedral structure was synthesized via a simple hydrothermal method using ferric chloride hexahydrate and 2-aminoterephthalic acid (NH2-BDC) as raw materials. The fluorescence of Fe-MOF is extremely weak owing to ligand-to-metal charge transfer (LMCT) and internal filtration effect (IFE). When the system contained TBHQ, the binding of TBHQ to Fe(III) inhibited the LMCT of the fluorescent ligand NH2-BDC to Fe(III), releasing the fluorescence of NH2-BDC and thus restoring the fluorescence. With this as the basis, a rapid, sensitive, and selective fluorescence sensor is developed for the detection of TBHQ. Under the optimal conditions, TBHQ showed good linearity with fluorescence intensity in the range of 0-1.5 × 102 µmol L-1 and a detection limit of 0.0030 µmol L-1 (S/N = 3). The selectivity, reproducibility, and stability of the developed Fe-MOF-based sensors are comprehensively studied. Finally, the practicality of the method is verified by examining the detection of TBHQ in soybean oil; the results are consistent with those obtained using conventional high-performance liquid chromatography.

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.

Ratiometric surface-enhanced Raman scattering strategy using gold nanoparticles confined on an ultrathin polydimethylsiloxane grafted gold mirror film substrate for ferbam screening in fruit juice.

BACKGROUND: In surface-enhanced Raman scattering (SERS) detection methods, the intricacies in the synthesis and recognition processes, along with non-uniform substrate morphologies, induce spectral irreproducibility. Metal (gold) nanoparticles (AuNPs) on gold (Au) mirror film configuration along with a ratiometric approach, constitute a potential system to resolve this issue. RESULTS: To acquire a reproducible and stable SERS response, an ultrathin polydimethylsiloxane (PDMS) spacer layer was grafted onto the Au mirror film via a contact heating step. The AuNPs-supported ultrathin PDMS grafted Au mirror film system was extended for ratiometric sensing of ferbam residue in real fruit juice samples. The hydrophobic PDMS localizes the AuNPs, 4-nitrophenol probe, and ferbam to a smaller region on the PDMS-grafted Au mirror film and prevents their spreading and diffusion. The ratiometric SERS response for ferbam target and probe ratio at I1376/I1326 cm-1 was monitored on the AuNPs@PDMS grafted Au mirror film substrate with good linear fitting. A detection limit of 1.09 × 10-8 mol L-1 and a relative standard deviation of 11.90% were obtained. In addition, ferbam residues in grape and orange juice samples were successfully recovered (96.86%-99.76%). SIGNIFICANCE: The AuNPs@PDMS grafted Au mirror film substrate, coupled with ratiometric analysis, showed excellent SERS activity with high sensitivity and reproducibility. The proposed platform can be adequately extended to detect other pesticide types in complex food settings.