High sensitive Ratiometric fluorescent Aptasensor with AIE properties for Deoxynivalenol (DON) detection.

An emerging fluorescent ratiometric aptasensor based on gold nanoclusters (AuNCs) with aggregation-induced emission (AIE) properties was prepared and studied for deoxynivalenol (DON) detection. The ratiometric aptasensor used red fluorescent AuNCs620 labelled with DON aptamer (Apt-AuNCs620) as an indicator and green fluorescent AuNCs519 modified by complementary DNA (cDNA) and magnetic beads (MBs) as internal reference, namely MBs-cDNA-AuNCs519. Under the optimal conditions, the aptasensor exhibited two good linear ranges of 0.1-50 and 50-5000 pg/mL for DON detection with coefficient of determination (R2) of 0.9937 and 0.9928, respectively, and the low detection limit (LOD) of 4.09 pg/mL was achieved. Furthermore, this aptasensor was feasible to detect DON in positive wheat samples, and the results were in line with those from HPLC and ELISA, thus providing a promising route to detect DON with high sensitivity in cereals, even for other mycotoxins by replacing the suitable aptamer and cDNA.

A fluorescent Aptasensor based on magnetic-separation strategy with gold nanoclusters for Deoxynivalenol (DON) detection.

A highly sensitive method based on MBs-cDNA@Apt-AuNCs519 was developed for deoxynivalenol (DON) detection in wheat. The MBs-cDNA@Apt-AuNCs519 was established using green emission gold nanoclusters (AuNCs519) with aggregation-induced emission properties as signal probes and combining amino-modified DON-aptamer (Apt), biotin-modified DNA strand (the partially complementary to Apt (cDNA)), and streptavidin-modified magnetic beads (MBs). The Apt-AuNCs519 were well connected with MBs-cDNA without DON but dissociated from MBs-cDNA@Apt-AuNCs519 with the addition of DON, leading to a noticeable reduction in the fluorescent intensity of the aptasensor. Moreover, this fluorescence aptasensor showed two linear relationships in the concentration range of 0.1-50 ng/mL and 50-5000 ng/mL with a limit of detection of 3.73 pg/mL with good stability, reproducibility and specificity. The results were consistent with high-performance liquid chromatography and enzyme-linked immunosorbent assay methods, further indicating the potential of this method for accurate trace detection of DON in wheat.

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety.

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

In situ investigation of ice fractions and water states during partial freezing of pork loins and shrimps.

Ice fractions and water states in partially frozen muscle foods greatly affect their quality. In the study, a variable temperature nuclear magnetic resonance (VT-NMR) with a liquid nitrogen temperature control system was employed to in situ investigate the relationship between ice fractions and temperatures and changes in water states during partial freezing and thawing of pork and shrimp. Results indicated that changes in ice fractions ranging from -2 âˆ¼ -20 °C could be divided into 3 stages including slow increase, random leap and remarkable leap. More serious damages to the structures related to immobile water occurred in shrimp than in pork, and partial freezing also caused deterioration in muscle fibres related to free water. Additionally, -2 âˆ¼ -3 °C and - 3.5 °C were the appropriate partial freezing temperatures for pork and shrimp, respectively. Therefore, the VT-NMR method possessed great potential for fundamental studies and applications of partial freezing of muscle foods.

Prediction of freezing point and moisture distribution of beef with dual freeze-thaw cycles using hyperspectral imaging.

The freezing point (FP) is an important quality indicator of the superchilled meat. Currently, the potential of hyperspectral imaging (HSI) for predicting beef FP as affected by multiple freeze-thaw (F-T) cycles was explored. Correlation analysis revealed that the FP had a negative correlation with the proportion of bound water (P21) and a positive correlation with the proportion of immobilized water (P22). Moreover, the optimal wavelengths were selected by principal component analysis (PCA). Principal component regression (PCR) and partial least squares regression (PLSR) models were successfully developed based on the optimal wavelengths for predicting FP with determination coefficient in prediction (RP2) of 0.76, 0.76 and root mean square errors in prediction (RMSEP) of 0.12, 0.12, respectively. Additionally, PLSR based on full wavelengths was established for predicting P21 with RP2 of 0.80 and RMSEP of 0.67, and PLSR based on the optimal wavelengths was established for predicting P22 with RP2 of 0.87 and RMSEP of 0.66. The results show the potential of hyperspectral technology to predict the FP and moisture distribution of meat as a nondestructive method.

Tunable thermoresponsive hydrogels for temperature regulation and warning in fruit and vegetables preservation.

Fresh fruit and vegetables usually suffer from quality deterioration when exposed to inappropriate temperatures. Common energy-input temperature regulation is widely applied but there remain challenges of increasing energy consumption. Passive temperature management regulates the heat transfer without energy consumption, showing a sustainable strategy for food preservation. Here, thermoresponsive hydrogels were constructed by incorporating NaCl and sodium dodecyl sulfate (SDS) micelles into a poly(N-isopropylacrylamide-co-acrylamide) (P(NIPAM-co-AM)) network. Due to the excellent mechanical properties and reversible thermochromism at 14 °C and 37 °C, Gel-8 wt%-NaCl could inhibit temperature rise and avoid sunburn damage to peppers under direct sunlight by blocking the input of solar energy and accelerating moisture evaporation. Additionally, hydrogels could act as a feasible sensor by providing real-time visual warnings for inappropriate temperatures during banana storage. Based on the self-adaptive thermoresponsive behaviour, the prepared hydrogels showed effective performance of temperature regulation and quality preservation of fruit and vegetables.

In-situ investigation of supercooling behaviour during high-pressure shift freezing of pure water and sucrose solution.

Supercooling is a main controllable factor for the fundamental understanding the high-pressure shift freezing (HPSF). In the study, a self-developed device based on the diamond anvil cell (DAC) and confocal Raman microscopy was utilized to realize an in-situ investigation of supercooling behaviour during HPSF of the pure water and sucrose solution. The spectra were used to determine the freezing point which is shown as a spectral phase marker (SD). The hydrogen bond strengths of water and sucrose solution under supercooling states were estimated by peak position and peak area ratio of sub-peaks. The results showed that the OH stretching bands had redshift under supercooling states. Moreover, the addition of sucrose molecules could strengthen the hydrogen bonding strength of water molecules under supercooling states. Thus, the DAC combined with Raman spectroscopy could be considered a novel strategy for a deep understanding of the supercooling behaviour during HPSF.

A tailored dual core-shell magnetic SERS substrate with precise shell-thickness control for trace organophosphorus pesticides residues detection.

For addressing the challenges of strong affinity SERS substrate to organophosphorus pesticides (OPs), herein, a rapid water-assisted layer-by-layer heteronuclear growth method was investigated to grow uniform UiO-66 shell with controllable thickness outside the magnetic core and provide abundant defect sites for OPs adsorption. By further assembling the tailored Au@Ag, a highly sensitive SERS substrate Fe3O4-COOH@UiO-66/Au@Ag (FCUAA) was synthesized with a SERS enhancement factor of 2.11 × 107. The substrate's suitability for the actual vegetable samples (cowpeas and peppers) was confirmed under both destructive and non-destructive detection conditions, showing a strong SERS response to fenthion and triazophos, with limits of detection of 1.21 × 10-5 and 2.96 × 10-3 mg/kg in the vegetables under destructive conditions, and 0.13 and 1.39 ng/cm2 for non-destructive detection, respectively. The FCUAA substrate had high SERS performance, effective adsorption capability for OPs, and demonstrated good applicability, thus exhibiting great potential for rapid detection of trace OPs residues in the food industry.

Effects of cold plasma pretreatment combined with sodium periodate on property enhancement of dialdehyde starch prepared using native maize starch.

This study represents the inaugural investigation into the effect of cold plasma (CP) pretreatment combined with sodium periodate on the preparation of dialdehyde starch (DAS) from native maize starch and its consequent effects on the properties of DAS. The findings indicate that the maize starch underwent etching by the plasma, leading to an increase in the particle size of the starch, which in turn weakened the rigid structure of the starch and reduced its crystallinity. Concurrently, the plasma treatment induced cleavage of the starch molecular chain, resulting in a decrease in the viscosity of the starch and an enhancement of its fluidity. These alterations facilitated an increased contact area between the starch and the oxidising agent sodium periodate, thereby augmenting the efficiency of the DAS preparation reaction. Consequently, the aldehyde group content was elevated by 9.98 % compared to the conventional DAS preparation methodology. Therefore, CP could be an efficient and environmentally friendly non-thermal processing method to assist starch oxidation for DAS preparation and property enhancement.

Fast real-time monitoring of meat freshness based on fluorescent sensing array and deep learning: From development to deployment.

A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having a good ability in the quantitative and qualitative detection of ammonia, dimethylamine, and trimethylamine gases with a low limit of detection (as low as 131.56 ppb) in range of 5 âˆ¼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C. Moreover, SqueezeNet was applied to automatically identify the fresh level of meat based on FSA images with high accuracy (98.17 %) and further deployed in various production environments such as personal computers, mobile devices, and websites by using open neural network exchange (ONNX) technique. The entire meat freshness recognition process only takes 5 âˆ¼ 7 s. Furthermore, gradient-weighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) explanatory algorithms were used to improve the interpretability and transparency of SqueezeNet. Thus, this study shows a new idea for FSA assisted with DL in meat freshness intelligent monitoring from development to deployment.

Developing a magnetic SERS nanosensor utilizing aminated Fe-Based MOF for ultrasensitive trace detection of organophosphorus pesticides in apple juice.

The unreasonable use of organophosphorus pesticides leads to excessive pesticide residues in food, seriously threatening public health, and the potential of surface-enhanced Raman spectroscopy (SERS) technology, incorporating a metal-organic framework, is substantial for the rapid detection of trace pesticide residues. Here, a novel Fe3O4@NH2-MIL-101(Fe)@Ag (FNMA) SERS nanosensor was developed. Results indicated that the FNMA had a high enhancement factor of 1.53 × 108, a low limit of detection (LOD) of 4.55 × 10-12 M, and a relative standard deviation of 7.73 % for 4-nitrothiophenol, demonstrating its good SERS sensitivity and uniformity, and also possessed good storage stability for one month. In quantifying fenthion and methyl parathion in standard solutions and apple juice in the range of 0.05/0.02-20 mg/L, it showed LODs of 3.02 × 10-3 mg/L and 1.43 × 10-3 mg/L, and 0.0407 and 0.0075 mg/L, respectively, demonstrating potentials in ultrasensitive trace detection of pesticides in food.

Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages.

Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.

Regulating bacterial biofilms in food and biomedicine: unraveling mechanisms and Innovating strategies.

Bacterial biofilm has brought a lot of intractable problems in food and biomedicine areas. Conventional biofilm control mainly focuses on inactivation and removal of biofilm. However, with robust construction and enhanced resistance, the established biofilm is extremely difficult to eradicate. According to the mechanism of biofilm development, biofilm formation can be modulated by intervening in the key factors and regulatory systems. Therefore, regulation of biofilm formation has been proposed as an alternative way for effective biofilm control. This review aims to provide insights into the regulation of biofilm formation in food and biomedicine. The underlying mechanisms for early-stage biofilm establishment are summarized based on the key factors and correlated regulatory networks. Recent developments and applications of novel regulatory strategies such as anti/pro-biofilm agents, nanomaterials, functionalized surface materials and physical strategies are also discussed. The current review indicates that these innovative methods have contributed to effective biofilm control in a smart, safe and eco-friendly way. However, standard methodology for regulating biofilm formation in practical use is still missing. As biofilm formation in real-world systems could be far more complicated, further studies and interdisciplinary collaboration are still needed for simulation and experiments in the industry and other open systems.

Assembly-Induced Emission of Copper Nanoclusters: Revealing the Sensing Mechanism for Detection of Volatile Basic Nitrogen in Seafood Freshness On-Site Monitoring.

Total volatile basic nitrogen (TVB-N) is a vital indicator for assessing seafood freshness and edibility. Rapid on-site detection of volatile basic nitrogen (VBN) is of significant importance for food safety monitoring. In this study, highly luminescent self-assembled copper nanoclusters (Cu NCs@p-MBA), synthesized using p-mercaptobenzoic acid (p-MBA) as the ligand, were utilized for the sensitive detection of VBNs. Under acidic conditions, Cu NCs@p-MBA formed compact and well-organized nanosheets through noncovalent interactions, accompanied by intense orange fluorescence emission (651 nm). The benzene carboxylic acid part of Cu NCs@p-MBA provided the driving force for supramolecular assembly and exhibited a strong affinity for amines, particularly low-molecular-weight amines such as ammonia (NH3) and trimethylamine (TMA). The quantitative determination of NH3 and TMA showed the detection limits as low as 0.33 and 0.81 ppm, respectively. Cu NCs@p-MBA also demonstrated good responsiveness to putrescine and histamine. Through density functional theory (DFT) calculations and molecular dynamics (MD) simulations, the precise atomic structure, assembly structure, luminescent properties, and reaction processes of Cu NCs@p-MBA were studied, revealing the sensing mechanism of Cu NCs@p-MBA for highly sensitive detection of VBNs. Based on the self-assembled Cu NCs@p-MBA nanosheets, portable fluorescent labels were developed for semiquantitative, visual, and real-time monitoring of seafood freshness. Therefore, this study exemplified the high sensitivity of self-assembly induced emission (SAIE)-type Cu NCs@p-MBA for VBNs sensing, offering an efficient solution for on-site monitoring of seafood freshness.

Unveiling microwave and Roasting-Steam heating mechanisms in regulating fat changes in pork using cell membrane simulation.

Fat reduction due to heating or cooking is an important issue in a healthy diet. In the current study, pork subcutaneous back fat was treated via microwave heating (MH) within 10-90 s and roasting - steam heating (RSH) within 2-30 min and their dynamic changes of individual adipocytes were explored by using vesicles as a bio-membrane model. The result showed that MH and RSH significantly increased fat loss (P < 0.05), with the maximum losses being 74.1 % and 65.6 %, respectively. The mechanical strength of connective tissue decreased and then increased slightly. The microstructure demonstrated that MH and RSH treatments facilitated a large outflow of fat, showing that the particle size of the vesicle and individual adipocytes increased and then decreased. It is thus feasible to study the dynamic changes of individual adipocytes in regulating fat reduction using cell membrane simulation.

Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep learning algorithms.

This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t-distributed stochastic neighbour embedding (t-SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness.

Fabrication and characterization of biomimetic plant cuticles from pullulan - graphene oxide (PU-GO) and beeswax - stearic acid (BW-SA) for Citrus Limon Rosso preservation.

Inspired by the natural plant cuticles, a novel strategy was proposed for the fabrication of biomimetic plant cuticles from pullulan-graphene oxide (PU-GO) and beeswax-stearic acid (BW-SA), which could serve as hydrophilic polysaccharides and hydrophobic waxes, respectively. PU-GO and PU-GO/BW-SA in different GO concentrations (0, 10, 30 and 50 µg/mL) were prepared, and their structural characteristics and basic properties were investigated. Results showed that PU-GO/BW-SA possessed a hydrophilic layer and a hydrophobic structure similar to the structure of natural plant cuticles. The incorporation of GO enhanced the barrier properties of the films and PU-GO/BW-SA showed a higher contact angle, lower tensile strength and higher barrier properties compared with PU-GO. In addition, PU-GO/BW-SA in 10 µg/mL GO concentration (PU-GO10/BW-SA) possessed the lowest WVP (7.2 × 10-7 g/(m h Pa)) and a contact angle (93.78°) similar to natural plant cuticles. Applications in Citrus Limon Rosso further proved the potential of PU-GO10/BW-SA as a biomimetic plant cuticle in fruit preservation.

Deep eutectic solvents (DESs) films based on gelatin as active packaging for moisture regulation in fruit preservation.

To develop a novel active packaging for fruit preservation, two different deep eutectic solvents (DESs) comprising choline chloride, betaine and glycerol [ChCl:Gly (1:2) and Be:Gly (1:2)] were prepared and the corresponding DESs-based films (DES@Gel) using gelatin as polymer matrix were fabricated. DES@Gel showed smoother morphologies and better optical and mechanical properties as compared with Gel. Moisture sorption isotherm curves, the enhancement of water vapour permeability (WVP) and the excellent moisture absorption-desorption cyclist performance illustrated the moisture regulation hypothesis mechanism of DES@Gel. Furthermore, cherry tomato preservation experiment was carried out and the groups treated with DES@Gel showed better performances. The moisture regulation property of DES@Gel could broaden new avenues for active packaging.

Comparative elucidation of bioactive and antioxidant properties of red dragon fruit peel as affected by electromagnetic and conventional drying approaches.

The effects of near-infrared (NIRD), mid-infrared (MIRD), far-infrared (FIRD), microwave (MWD), and hot air drying (HAD) on drying kinetic, colour, phytochemical composition, and antioxidant activity of red dragon fruit peel (RDFP) was evaluated. Results indicated that drying methods induced varying microstructural and chemical changes on RDFP, significantly influencing moisture removal rates and phytochemical retention. The lowest drying time was observed for MWD, while MIRD presented the highest drying time. FIRD drying was more favourable for retaining TPC, TFC, betacyanin and betaxanthin, while the ascorbic acid content was better retained during MIRD and NIRD. Enhancements in ABTS, CUPRAC and reducing power were associated with FIRD, and NIRD and MIRD enhanced DPPH and HRSA. Overall, chemical modifications induced by drying improved the phytochemical and antioxidant properties but presented adversative effects on ascorbic acid and DPPH. The study presented an essential background for the optimal drying of RDFP.

Developments in food neonicotinoids detection: novel recognition strategies, advanced chemical sensing techniques, and recent applications.

Neonicotinoid insecticides (NEOs) are a new class of neurotoxic pesticides primarily used for pest control on fruits and vegetables, cereals, and other crops after organophosphorus pesticides (OPPs), carbamate pesticides (CBPs), and pyrethroid pesticides. However, chronic abuse and illegal use have led to the contamination of food and water sources as well as damage to ecological and environmental systems. Long-term exposure to NEOs may pose potential risks to animals (especially bees) and even human health. Consequently, it is necessary to develop effective, robust, and rapid methods for NEOs detection. Specific recognition-based chemical sensing has been regarded as one of the most promising detection tools for NEOs due to their excellent selectivity, sensitivity, and robust interference resistance. In this review, we introduce the novel recognition strategies-enabled chemical sensing in food neonicotinoids detection in the past years (2017-2023). The properties and advantages of molecular imprinting recognition (MIR), host-guest recognition (HGR), electron-catalyzed recognition (ECR), immune recognition (IR), aptamer recognition (AR), and enzyme inhibition recognition (EIR) in the development of NEOs sensing platforms are discussed in detail. Recent applications of chemical sensing platforms in various food products, including fruits and vegetables, cereals, teas, honey, aquatic products, and others are highlighted. In addition, the future trends of applying chemical sensing with specific recognition strategies for NEOs analysis are discussed.