Investigation of heat-induced pork batter quality detection and change mechanisms using Raman spectroscopy coupled with deep learning algorithms.

Pork batter quality significantly affects its product. Herein, this study explored the use of Raman spectroscopy combined with deep learning algorithms for rapidly detecting pork batter quality and revealing the mechanisms of quality changes during heating. Results showed that heating increased ß-sheet content (from 26.38 to 41.42%) and exposed hidden hydrophobic groups, which formed aggregates through chemical bonds. Dominant hydrophobic interactions further cross-linked these aggregates, establishing a more homogeneous and denser network at 80 °C. Subsequently, convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and CNN-LSTM were comparatively used to predict gel strength and whiteness in batters based on the Raman spectrum. Thereinto, CNN-LSTM provided the optimal results for gel strength (Rp = 0.9515, RPD = 3.1513) and whiteness (Rp = 0.9383, RPD = 3.0152). Therefore, this study demonstrated the potential of Raman spectroscopy combined with deep learning algorithms as non-destructive tools for predicting pork batter quality and elucidating quality change mechanisms.

Multiple DNA cycle amplification for highly efficient detection of mercury pollution in food.

Mercury ion (Hg2+), a highly toxic metal pollutant, is widely found in the environment and can enter the human body through the food chain, causing various health issues. Sensitive and accurate methods for monitoring Hg2+ are highly desirable for ensuring food safety. Herein, we propose a self-sustainable multiple amplification system (MAS) for Hg2+ determination through the reciprocal activation between catalytic hairpin assembly (CHA) and rolling circle amplification (RCA). The thymine-encoded recognition element specifically recognizes Hg2+, triggering the exposure of the initiator. The initiator then motivates the mutual activation of CHA and RCA to accelerate the production of an exponentially amplified signal. The MAS method achieved a low detection limit of 11 pM. Due to its reliable target recognition and robust amplification efficiency, the MAS circuit facilitated the highly efficient and accurate analysis of low-abundance Hg2+ in milk and snakehead samples, thus providing a potentially new tool for food safety control.

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.

Quantitative and qualitative detection of target heavy metals using anti-interference colorimetric sensor Array combined with near-infrared spectroscopy.

An anti-interference colorimetric sensor array (CSA) technique was developed for the qualitative and quantitative detection of target heavy metals in corn oil. This method involves a binding mechanism that triggers changes in atomic energy levels and visible color changes. A custom-built olfactory visualization device was employed to gather spectral data, revealing distinct CSA color difference patterns. Subsequently, three pattern recognition algorithms were used to create an identification model for the target heavy metals. The results showed that the ACO-KNN (Ant Colony Optimization-K-Nearest Neighbor) model outperformed the other models, achieving accuracy rates of 90.28% and 89.58% for the calibration and prediction sets, respectively. The ACO-PLS (Partial Least Square) model was more stable with the lowest root mean square error of prediction (RMSEP), which were 0.1730 and 0.1180, respectively. The limit of detection (LOD) and quantification (LOQ) of Pb and Hg were (0.3, 0.6, 1.1 and 2.2) x 10-3 mg/L, respectively.

A three-channel biosensor based on stimuli-responsive catalytic activity of the Fe3O4@Cu for on-site detection of tetrodotoxin in fish.

Tetrodotoxin (TTX), a lethal neurotoxin, poses a grave threat to human health. The available spectroscopic methods suffer from limitations such as complex procedures and inadequate on-site capabilities. In this study, we proposed a method using Fe3O4@Cu as a catalytic biosensor combined with SERS, colorimetry and image processing for TTX detection. Integrating the aptamer amplifies the specificity of the system and masks the catalytic activity of Fe3O4@Cu. The catalytic efficiency of Fe3O4@Cu in the H2O2-TMB reaction can quantify the concentration of TTX in the system. Consequently, oxidation of TMB (oxTMB) led to the generation and change of signals for SERS, colorimetry and image processing, enabling a three-channel quantitative detection of TTX. Under the optimal conditions, the detection limit of established SERS, colorimetry and image processing were 0.055, 2.127 and 0.243 ng/mL, respectively. This three-channel biosensor was applied to real samples, providing an accurate, stable and adaptable alternative for on-site TTX detection.

Quantitative determination of zearalenone in wheat by the CSA-NIR technique combined with chemometrics algorithms.

In the current study, a colorimetric sensor array combined with near-infrared (NIR) spectroscopy was used to quantitatively analyze zearalenone in wheat. The portable NIR spectrometer was used to scan the porphyrin reaction points of the wheat colorimetric sensor and collect spectral data. Subsequently, based on all the NIR spectral data, the two models and three feature selection algorithms are compared, and the best performance model and the best feature variable input are selected. Concurrently, the Kernel-based Extreme Learning Machine (KELM) model optimized by the two parameter optimization algorithms was compared, and the best parameter optimization algorithm was selected. Among all evaluation models, the KELM model optimized by the Competitive Adaptive Reweighted Sampling algorithm combined with the rime optimization algorithm has the best prediction effect. The predicted RP2 is 0.9900, and the root mean square error of prediction (RMSEP) is 18.4610 µg∙kg-1.

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.

Recent advances review in tea waste: High-value applications, processing technology, and value-added products.

Tea waste (TW) includes pruned tea tree branches, discarded summer and fall teas, buds and wastes from the tea making process, as well as residues remaining after tea preparation. Effective utilization and proper management of TW is essential to increase the economic value of the tea industry. Through effective utilization of tea waste, products such as activated carbon, biochar, composite membranes, and metal nanoparticle composites can be produced and successfully applied in the fields of fuel production, composting, preservation, and heavy metal adsorption. Comprehensive utilization of tea waste is an effective and sustainable strategy to improve the economic efficiency of the tea industry and can be applied in various fields such as energy production, energy storage and pharmaceuticals. This study reviews recent advances in the strategic utilization of TW, including its processing, conversion technologies and high value products obtained, provides insights into the potential applications of tea waste in the plant, animal and environmental sectors, summarizes the effective applications of tea waste for energy and environmental sustainability, and discusses the effectiveness, variability, advantages and disadvantages of different processing and thermochemical conversion technologies. In addition, the advantages and disadvantages of producing new products from tea wastes and their derivatives are analyzed, and recommendations for future development of high-value products to improve the efficiency and economic value of tea by-products are presented.

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.

Sulfadiazine detection in aquatic products using upconversion nanosensor based on photo-induced electron transfer with imidazole ligands and copper ions.

Contamination of aquatic products with sulfonamide antibiotics poses a threat to consumer health and can lead to the emergence of drug-resistant bacteria. Common methods to detect such compounds are slow and require expensive instruments. We developed a sensitive sulfadiazine (SDZ) detection method based on the photoinduced electron transfer between UCNPs and Cu2+. The surface-modified upconversion nanoparticles bind to Cu2+ by electrostatic adsorption, causing fluorescence quenching. The quenched fluorescence was subsequently recovered by the addition of imidazole and SDZ to the detection system, which formed a complex with Cu2+. The sensor showed excellent linearity over a wide concentration range (0.05-1000 ng/mL), had a low limit of detection (0.04 ng/mL), was selective, and was not affected by common substances present in aquatic media. This indicates that the sensor has great potential for application in the detection of SDZ residues in aquatic products.

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.

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.

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.

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.

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.

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.

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.