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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

CRISPR/Cas12a and G-quadruplex DNAzyme-driven multimodal biosensor for visual detection of Aflatoxin B1.

Aflatoxin B1 (AFB1) contamination severely threatens human and animal health, it is thus critical to construct a strategy for its rapid, accurate, and visual detection. Herein, a multimodal biosensor was proposed based on CRISPR/Cas12a cleaved G-quadruplex (G4) for AFB1 detection. Briefly, specific binding of AFB1 to the aptamer occupied the binding site of the complementary DNA (cDNA), and cDNA then activated Cas12a to cleave G4 into fragments. Meanwhile, the intact G4-DNAzyme could catalyze 3, 3', 5, 5'-tetramethylbenzidine (TMB) to form colourimetric/SERS/fluorescent signal-enhanced TMBox, and the yellow solution produced by TMBox under acidic conditions could be integrated with a smartphone application for visual detection. The colourimetric/SERS/fluorescent biosensor yielded detection limits of 0.85, 0.79, and 1.65 pg·mL-1, respectively, and was applied for detecting AFB1 in peanut, maize, and badam samples. The method is suitable for visual detection in naturally contaminated peanut samples and has prospective applications in the food industry.

Analyzing the effects of nonthermal pretreatments on the quality of microwave vacuum dehydrated beef using terahertz time-domain spectroscopy and near-infrared hyperspectral imaging.

Both nonthermal pretreatment and nondestructive analysis are effective technologies in improving drying processes. This study evaluated the effects of different pretreatment methods on the quality of beef dehydrated by microwave vacuum drying (MVD) and compared the MVD process performance comprising real-time moisture content (MC), MC loss, colour content, and shrinkage rate using different optical sensing methods including terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI). Results indicated that osmotic pretreatment improved the drying rate of MVD beef with lower changes in colour and shrinkage rate. Both THz-TDS-based and NIR-HSI-based on-site direct scanning and in-situ in-direct sensing showed accurate prediction results, with best R2p of 0.9646 and 0.9463 for MC and R2p of 0.9817 and 0.9563 for MC loss prediction, respectively. NIR-HSI visualisation of MC results showed that ultrasound pretreatment curbed but osmotic pretreatment promoted nonuniform distribution during MVD. This research should guide improving the industrial MVD drying process.

Inhibition of cell wall pectin metabolism by plasma activated water (PAW) to maintain firmness and quality of postharvest blueberry.

Blueberry is a class of berries with high nutritional and economic value but has short shelf life due to its rapid softening at room temperature. This study investigated the effects of plasma-activated water (PAW) treatment on the softening quality and cell wall pectin metabolism of blueberries stored for 10 d at 25 °C after being immersed in PAW for 10 min. PAW was generated by plasma with different times (1 and 2 min), fixed frequency (10 kHz) and fixed voltage (50 kV). The analysis showed that the firmness of PAW-treated fruit significantly increased (P < 0.05) by 36.4% after 10 d storage. PAW treatment controlled the solubilization of pectin from water-insoluble to water-soluble. The activities of cell wall pectin-degrading enzymes like polygalacturonase (PG), ß-galactosidase (ß-Gal) and pectin methylesterase (PME) in PAW-treated blueberries decreased by 15.7%, 18.3%, and 27.9%, respectively, on day 10. After PAW treatment, blueberries also maintained better postharvest quality (firmness, colour, soluble solid content and anthocyanin content) and intact epidermal waxy and cell wall structure. These results suggested that PAW showed great potential for postharvest fresh-keeping of blueberry.

Insights into inactivation and response mechanisms of sublethal Listeria monocytogenes treated by cold plasma with joint transcriptomics and metabolomics.

AIM: The aim of the current study is to elucidate the inactivation and molecular response pattern of sublethal Listeria monocytogenes to cold plasma-mediated two-pronged oxidative microenvironments from a high-throughput multi-omics perspective. METHODS AND RESULTS: First joint transcriptomics and metabolomics analyses revealed that significantly expressed genes and metabolites were mainly involved in enhanced transmembrane transport and Fe2+/Cu+ efflux, amino acid limitation, cytoplasmic pH homeostasis, reconfiguration of central carbon metabolism flux, and energy conservation strategy, which triggered the surge of intracellular endogenous oxidative stress and finally mediated bacterial ferroptosis and pathogenicity attenuation. Typical antioxidant systems such as the TrxR-Trx system and common antioxidant genes (e.g. sodA, katA, ahpC, trxA, spxA) were inhibited, and the more prominent antioxidant pathways include methionine metabolism, the pentose phosphate pathway, and glutathione metabolism, as well as the DNA repair systems. CONCLUSIONS: Therefore, our work confirmed from the transcriptional and metabolic as well as physiological levels that cold plasma-mediated intracellular oxidative stress induced big perturbations in pathways as a driving force for the inactivation and pathogenicity attenuation of L. monocytogenes. SIGNIFICANCE AND IMPACT OF STUDY: This study provided new insights for the construction of multi-dimensional mechanisms of bacterial inactivation and pathogenicity attenuation for the precise control and inactivation of microorganisms in plasma non-thermal processing.

Distinguishing pericarpium citri reticulatae of different origins using terahertz time-domain spectroscopy combined with convolutional neural networks.

The geographical indication of pericarpium citri reticulatae (PCR) is very important in grading the quality and price of PCRs. Therefore, terahertz time-domain spectroscopy (THz-TDS) technology combined with convolutional neural networks (CNN) was proposed to distinguish PCRs of different origins without damage in this study. The one-dimensional CNN (1D-CNN) model with an accuracy of 82.99% based on spectral data processed with SNV was established. The two-dimensional image features were transformed from unprocessed spectral data using the gramian angular field (GAF), the Markov transition field (MTF) and the recurrence plot (RP), which were used to build a two-dimensional CNN (2D-CNN) model with an accuracy of 78.33%. Further, the CNN models with different fusion methods were developed for fusing spectra data and image data. In addition, the adding spectra and images based on the CNN (Add-CNN) model with an accuracy of 86.17% performed better. Eventually, the Add-CNN model based on ten frequencies extracted using permutation importance (PI) achieved the identification of PCRs from different origins. Overall, the current study would provide a new method for identifying PCRs of different origins, which was expected to be used for the traceability of PCRs products.

Aggregates-based fluorescence sensing technology for food hazard detection: Principles, improvement strategies, and applications.

Aggregates often exhibit modified or completely new properties compared with their molecular elements, making them an extraordinarily advantageous form of materials. The fluorescence signal change characteristics resulting from molecular aggregation endow aggregates with high sensitivity and broad applicability. In molecular aggregates, the photoluminescence properties at the molecular level can be annihilated or elevated, leading to aggregation-causing quenching (ACQ) or aggregation-induced emission (AIE) effects. This change in photoluminescence properties can be intelligently introduced in food hazard detection. Recognition units can combine with the aggregate-based sensor by joining the aggregation process, endowing the sensor with the high specificity of analytes (such as mycotoxins, pathogens, and complex organic molecules). In this review, aggregation mechanisms, structural characteristics of fluorescent materials (including ACQ/AIE-activated), and their applications in food hazard detection (with/without recognition units) are summarized. Because the design of aggregate-based sensors may be influenced by the properties of their components, the sensing mechanisms of different fluorescent materials were described separately. Details of fluorescent materials, including conventional organic dyes, carbon nanomaterials, quantum dots, polymers and polymer-based nanostructures and metal nanoclusters, and recognition units, such as aptamer, antibody, molecular imprinting, and host-guest recognition, are discussed. In addition, future trends of developing aggregate-based fluorescence sensing technology in monitoring food hazards are also proposed.