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.

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.

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.

Ti3C2Tx MXene-Based Fluorescent Aptasensor for Detection of Dimethoate Pesticide.

Dimethoate contaminants in food pose a threat to human health. Rapid and sensitive trace detection methods are required to keep food safe. In this study, a novel fluorescent aptasensor was developed for the sensitive detection of dimethoate based on carbon quantum dots labeled with double-stranded DNA (CQDs-apt-cDNA) and Ti3C2Tx flakes. Under optimal conditions, the aptasensor showed a good linear range of 1 × 10-9 to 5 × 10-5 M for dimethoate with a coefficient of determination (R2) of 0.996. Besides, a low detection limit of 2.18 × 10-10 M was obtained. The aptasensor showed high selectivity in interference samples and good reproducibility with an RSD of 3.06% (<5%) for dimethoate detection. Furthermore, the proposed aptasensor was applied to the detection of dimethoate in apple juice and tap water with satisfactory recoveries from 96.2 to 104.4%. Because of these benefits, this aptasensor has the potential and promise for detecting food contaminants in the food industry.

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.

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.

Flexible Au@AgNRs/CMC/qPCR film with enhanced sensitivity, homogeneity and stability for in-situ extraction and SERS detection of thiabendazole on fruits.

In this study, a high-performance, stable and homogeneous Au@AgNRs/CMC/qPCR flexible film surface-enhanced Raman scattering (SERS) substrate was constructed by synergistically stabilizing and protecting bimetallic core-shell Au@Ag nanorods (Au@AgNRs) with carboxymethylcellulose (CMC) and fluorescent-quantitative-polymerase-chain-reaction (qPCR) film. The network structure of CMC immobilized and aligned Au@AgNRs through coordination of carboxyl groups with surface Ag atoms to provide intensive and stable 'hot spots', and the qPCR bilayer film performed as carrier and barrier to protect Au@AgNRs from oxidation, humidity and optical damage and improved the robustness and stability. The Au@AgNRs/CMC/qPCR film was used for in-situ extraction and SERS detection of thiabendazole on nectarine (0.24 ppm) and lemon (0.27 ppm) with low detection of limits. Furthermore, it retained 98.6% SERS performance after storage for 90 days under ambient conditions, revealing the great potential in promoting the commercialization of the SERS technique for sensitive contaminants sensing with simple fabrication procedures, homogeneity, reproducibility and long-term stability.

Preparation of Fe3O4@UiO-66(Zr)@Ag NPs core-shell-satellite structured SERS substrate for trace detection of organophosphorus pesticides residues.

Surface-enhanced Raman spectroscopy (SERS) technology has been revived and developed with the introduction of metal-organic frameworks (MOFs), while more valuable properties of MOFs for SERS substrates remain largely unexplored. This work constructed a new SERS substrate Fe3O4@UiO-66(Zr)@Ag nanoparticles (FUAs) with excellent SERS detection sensitivity, uniformity, reproducibility and stability, exhibiting a high Raman enhancement factor (5.62 × 106), low limit of detection (LOD, 2.11 × 10-11 M) and RSD (12.41 %) for 4-NBT, and maintaining 81 % SERS activity within 60 days. The FUAs took full advantage of the strong affinity of UiO-66(Zr) for organophosphorus pesticides (OPs) to realize trace OPs detection. The LODs of phoxim, triazophos and methyl parathion in apple juice were 0.041, 0.021 and 0.0031 mg/L, respectively, with good linearities ranging from 0.02 or 0.1-50 mg/L, meeting the requirements of the food control standards, indicating that the potentials and prospects of the FUAs SERS substrate for trace detecting OPs in foods.

Multiplex Surface-Enhanced Raman Scattering: An Emerging Tool for Multicomponent Detection of Food Contaminants.

For survival and quality of human life, the search for better ways to ensure food safety is constant. However, food contaminants still threaten human health throughout the food chain. In particular, food systems are often polluted with multiple contaminants simultaneously, which can cause synergistic effects and greatly increase food toxicity. Therefore, the establishment of multiple food contaminant detection methods is significant in food safety control. The surface-enhanced Raman scattering (SERS) technique has emerged as a potent candidate for the detection of multicomponents simultaneously. The current review focuses on the SERS-based strategies in multicomponent detection, including the combination of chromatography methods, chemometrics, and microfluidic engineering with the SERS technique. Furthermore, recent applications of SERS in the detection of multiple foodborne bacteria, pesticides, veterinary drugs, food adulterants, mycotoxins and polycyclic aromatic hydrocarbons are summarized. Finally, challenges and future prospects for the SERS-based detection of multiple food contaminants are discussed to provide research orientation for further.

High-performance homogeneous carboxymethylcellulose-stabilized Au@Ag NRs-CMC surface-enhanced Raman scattering chip for thiram detection in fruits.

Cellulose material holds considerable promise for effective surface-enhanced Raman scattering (SERS) substrate construction due to its extensive availability, chemically modifying capacity, ease of manufacture, high flexibility and low optical activity. A large-area, high-sensitivity, stable and uniform Au@Ag nanorods (NRs)-CMC substrate was successfully developed via electrostatic repulsion by using negatively-charged core-shell Au@Ag NRs as SERS active plasmonic nanomaterial, combined with negatively-charged carboxymethylcellulose (CMC) hydrogel for nanoparticles stabilization, homodisperse and protection. The obtained Au@Ag NRs-CMC substrate showed excellent sensitivity for the detection of thiram residues in fruits containing low and abundant pigment interferents, such as apples and blueberries, with detection limits of 58 and 78 ppb, respectively. Additionally, it retained more than 80% SERS performance after storage for 9 months under ambient conditions, demonstrating its great potential in facilitating the commercialization of cellulose-based SERS technology for cost-effective detection of food contaminants with advantages of facile preparation procedure, uniformity, reproducibility and long-term stability.

A SERS-Fluorescence dual-signal aptasensor for sensitive and robust determination of AFB1 in nut samples based on Apt-Cy5 and MNP@Ag-PEI.

Food aflatoxin B1 (AFB1) contamination greatly threatens human health and its sensitive determination is imperative. In this study, a surface-enhanced Raman scattering (SERS) and fluorescence dual-signal aptasensor was constructed for sensitive AFB1 detection in peanuts, walnuts, and almonds samples. Fluorescent dye cy5 was used as fluorophore and Raman reporter, while polyethyleneimine modified Ag coating magnetic nanoparticles (MNP@Ag-PEI) were utilized to absorb the cy5 modified aptamer (apt-cy5). Results indicated that linear ranges of 0.001-1000 ng/mL and 0.2-20,000 ng/mL with detection limits of 0.45 pg/mL and 0.135 ng/mL for the SERS and fluorescence methods were obtained, respectively, and AFB1 detection in the nut samples using the aptasensor achieved satisfactory recoveries of 95.2%-108.6% for SERS and 94.7%-109.7% for fluorescence. Compared with other mono signal detection, the established aptasensor facilely fused the merits of the two signals and improved the detection accuracy and flexibility.

Synthesis of recyclable SERS platform based on MoS2@TiO2@Au heterojunction for photodegradation and identification of fungicides.

Surface-enhanced Raman spectroscopy (SERS) substrates based on metal/semiconductors have attracted much attention due to their excellent photocatalytic activity and SERS performance. However, they generally exhibit low light utilization and photocatalytic efficiencies. Herein, molybdenum disulfide coated titanium dioxide modified with gold nanoparticles (MoS2@TiO2@Au) as a heterojunction-based recyclable SERS platform was fabricated for the efficient determination of fungicides. Results showed that the MoS2@TiO2@Au platform could rapidly degrade 90.7% crystal violet in 120 min under solar light irradiation and enable reproducible and sensitive SERS analysis of three fungicides (methylene blue, malachite green, and crystal violet) and in-situ monitor of the photodegradation process. The platform could also be reused five times due to the unique integrated merits of the MoS2@TiO2@Au heterojunction. Meanwhile, experiments in determining methylene blue in prawn protein solution achieved a limit of detection of 1.509 µg/L. Therefore, it is hoped that this work could expand detection applications of photocatalytic materials.

A novel fluorescence biosensor based on CRISPR/Cas12a integrated MXenes for detecting Aflatoxin B1.

Aflatoxin B1 (AFB1) contamination in food threatens global food safety, and rapid quantitative detection of AFB1 remains a challenge. Herein, a novel fluorescence biosensor was developed for AFB1 detection based on CRISPR/Cas12a and MXenes. Specifically, the well-designed activator was locked by dual-AFB1 aptamers, Cas12a was directly linked to crRNA to form inactivated complexes, and MXenes efficiently adsorbed FAM fluorophore-modified single-stranded DNA (ssDNA-FAM), quenching its fluorescence. In the presence of AFB1, the activator was released due to the preferential binding of the aptamer to AFB1, and the released activator then activated the trans-cleavage activity of Cas12a to indiscriminately cleave ssDNA on MXenes, leading to the recovery of the fluorescence signal. The fluorescent biosensor had a wide detection range from 0.001 to 80 ng mL-1, a detection limit of 0.92 pg mL-1, and the ability to detect within 80 min. More importantly, the platform demonstrates excellent detection performance in real peanut samples.

Mesoporous silica coated core-shell nanoparticles substrate for size-selective SERS detection of chloramphenicol.

With the growing popularity of the non-destructive technique, surface-enhanced Raman spectroscopy (SERS) demands a highly sensitive and reproducible plasmonic nanoparticles substrate. In this study, a novel bimetallic core-shell nanoparticles (Au@Ag@mSiO2NP) substrate consisting of a gold core, silver shell, and a mesoporous silica coating was synthesized. The mesoporous coating structure was created by employing template molecules such as surfactant and their subsequent removal allowing selective screening based on the size of analyte molecules. Results showed that the plasmonic substrate could selectively enhance small molecules by preventing large macromolecules to reach the exciting zone of the substrate core, achieving the detection of chloramphenicol in milk samples with a detection limit of 6.68 × 10-8 M. Moreover, the mesoporous coating provided additional stability to the Au@Ag nanoparticles, leading to the reusability of the substrate. Thus, this work offered a simple and smart Au@Ag@mSiO2NP substrate for effective SERS detection of analytes.

Discrimination of Pericarpium Citri Reticulatae in different years using Terahertz Time-Domain spectroscopy combined with convolutional neural network.

Pericarpium Citri Reticulatae (PCR) in longer storage years possess higher medicinal values, but their differentiation is difficult due to similar morphological characteristics. Therefore, this study investigated the feasibility of using terahertz time-domain spectroscopy (THz-TDS) combined with a convolutional neural network (CNN) to identify PCR samples stored from 1 to 20 years. The absorption coefficient and refractive index spectra in the range of 0.2-1.5 THz were acquired. Partial least squares discriminant analysis, random forest, least squares support vector machines, and CNN were used to establish discriminant models, showing better performance of the CNN model than the others. In addition, the output data points of the CNN intermediate layer were visualized, illustrating gradual changes in these points from overlapping to clear separation. Overall, THz-TDS combined with CNN models could realize rapid identification of different year PCRs, thus providing an efficient alternative method for PCR quality inspection.

Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications.

As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.

TEM Investigation of Asymmetric Deposition-Driven Crystalline-to-Amorphous Transition in Silicon Nanowires.

Controlling the shape and internal strain of nanowires (NWs) is critical for their safe and reliable use and for the exploration of novel functionalities of nanodevices. In this work, transmission electron microscopy was employed to examine bent Si NWs prepared by asymmetric electron-beam evaporation. The asymmetric deposition of Cr caused the formation of nanosized amorphous-Si domains; the non-crystallinity of the Si NWs was controlled by the bending radius. No other intermediate crystalline phase was present during the crystalline-to-amorphous transition, indicating a direct phase transition from the original crystalline phase to the amorphous phase. Moreover, amorphous microstructures caused by compressive stress, such as amorphous Cr domains and boxes, were also observed in the asymmetric Cr layer used to induce bending, and the local non-crystallinity of Cr was lower than that of Si under the same bending radius.