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The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures R c 2 = 0.898 R p 2 = 0.844 and freezing rates R c 2 = 0.879 R p 2 = 0.829 . The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods.

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.

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.

Surface-enhanced Raman spectroscopy (SERS) with cysteamine modified silver-coated gold nanoparticles (Au@Ag-CysNPs) was presented for fast screening of oxamyl and thiacloprid in liquid samples of milk. Concentrations at different levels were detected ranging from 0.5 to 10 ppm by choosing 500–1600 cm −1 spectral range. A strongest Raman peak at 679 cm −1 was ascribed to oxamyl, whereas a band at 1095 cm −1 was associated with thiacloprid in milk samples. The morphology and average size of core-shell nanoparticles (Au@AgNPs) and Au@Ag-CysNPs were acquired by High-resolution transition electron microscopy images and dynamic light scattering. Images showed that Au@AgNPs which had a core size of 28 nm and a shell of 6 nm thickness was modified with 6 nm cysteamine hydrochloride successfully. High coefficient of determination (R 2 ) values of 0.9968 and 0.9875 was calculated for oxamyl and thiacloprid with the limits of detection of 0.031 ppm and 0.023 ppm, respectively. The current novel nanoparticle was easy to prepare and was suitable to serve as a sensitive substrate for SERS detection applications, which could be employed for assessing other accidental contaminants in other food matrices in future studies.

The impact-ionization-enhanced mechanism is introduced into a SiC light-triggered thyristor (LTT) to improve its switching speed under weak UV illumination. The effects of impact ionization on photogenerated carrier multiplication and the dynamic switching performance of the SiC LTT are investigated through TCAD simulation. The relationships between bias voltage, UV light intensity, and key dynamic parameters are analyzed. Simulation results indicate that when the bias voltage exceeds 14 kV, the device enters the avalanche multiplication regime, leading to a significant increase in photocurrent under a given UV intensity. As the bias voltage increases, the turn-on time of the thyristor first decreases, then saturates, and finally drops rapidly. Under UV illumination of 100 mW/cm2, the turn-on time decreases from 10.1 μs at 1 kV to 0.85 μs at 18 kV, while the switching energy dissipation at 18 kV is only 1292.3 mJ/cm2. These results demonstrate that the impact-ionization-enhanced effect substantially improves the switching performance of SiC LTTs.

In this study, wavelet textural analysis was applied to hyperspectral images in the visible and near-infrared (VIS/NIR) region (400–1,000 nm) for differentiation between fresh and frozen–thawed pork. The spectral data of acquired hyperspectral images were analyzed using partial least squares (PLS) regression and five wavelengths (462, 488, 611, 629, and 678 nm) were selected as the feature wavelengths by the regression coefficients from the PLS model. The fourth-order daubechies wavelet (“db4”) was used to serve as the wavelet mother function for wavelet textural extraction of the feature images at the above selected feature wavelengths with the wavelet decomposition level from 1 to 4. Four textural features were calculated in the horizontal, vertical, and diagonal orientations at each level. Forty-eight textural features were extracted from each feature image and used to differentiate between fresh and frozen–thawed pork samples by least-squares support vector machine (LS-SVM) model. Wavelet texture extracted from all five feature images at first decomposition level was identified as optimal wavelet texture combination, resulting in the highest classification accuracy for the LS-SVM models (98.48 % for the training set and 93.18 % for the testing set). Based on the texture combination, the quality attributes of pork meat could be predicted with correlation coefficients of calibration (r c ) of 0.982 and 0.913, and correlation coefficients of prediction (r ₚ ) of 0.845 and 0.711 for pH and thawing loss, respectively. The results showed the possibility of developing a fast and reliable hyperspectral system for discrimination between fresh and frozen–thawed pork samples based on wavelet texture in the VIS/NIR wavelength range.

Efficient detection and degradation of fungicides are greatly concerned with aquatic food safety. Herein, a multifunction CoFe2O4/MoS2@Au (ACMS) composite was synthesized for crystal violet (CV) and malachite green (MG) photocatalytic degradation and SERS determination. As the construction of the Z-scheme heterostructure of ACMS, which enhanced the light absorption capability and the separation efficiency of photoexcited carrier significantly, ACMS possessed an excellent photocatalytic performance with a degradation rate of 94.76% for CV under simulated solar light irradiation. Furthermore, the multifunction ACMS exhibited superior SERS capability with a detection limit (LOD) of 4.309 × 10−2 μg L−1 for MG residues in water. And the ACMS substrates could be utilized to determine the MG residues in crucian carp extract, resulting in a recovery rate of 96.00~116.00%. In addition, such multifunction heterojunctions were performed for in situ monitoring of the photodegradation process. This research opened up a novel perspective on the applications of heterojunction-based multifunction materials for food safety control.

A quick, accurate, and reliable method for the evaluation of meat quality during salting stages is essential for quality control and management. This study was carried out to investigate the utility of hyperspectral imaging (HSI) techniques (400–1,000 nm) for predicting the color and pH of salted meat. Specifically, partial least squares regression (PLSR) was applied to the spectral data extracted from the images of the meat to develop statistical models for predicting color and pH. A subset of information-rich wavelengths was identified by principal component analysis (PCA) and used in a regression model. The results from the model with the reduced number of wavelengths generated L*, a*, and pH values with coefficients of determination (R ² cᵥ) of 0.723, 0.726, and 0.86 and root mean square errors estimated by cross-validation (RMSECV) of 2.898, 1.408, and 0.073, respectively. These values compared favorably with values generated by a PLSR model using all of the wavelengths investigated, illustrating the reasonable accuracy and robustness of the method. The overall results of this study demonstrate the potential of HSI to serve as an objective and nondestructive method for rapid determination of color and pH of porcine meat during the salting process.

For the first time, β-Ga2O3 were prepared on 4H-SiC (0001) substrates using a low-pressure chemical vapor deposition (LPCVD) technique. The obtained β-Ga2O3/4H-SiC heterostructures display strongly preferential growth orientation along the of β-Ga2O3. Combining the experimental results, interfacial properties, such as the work of adhesion (Wad), electronic properties and bonding characteristics of β-Ga2O3(201)/4H-SiC(0001) heterointerface were systemically studied using first principles. Four different β-Ga2O3(201)/4H-SiC(0001) interface models composed of different atom stacking sequences were established. It was found that the interface consisting of silicon terminated of 4H-SiC (0001), and oxygen terminated of β-Ga2O3(201) (Si-O) has the lowest relaxation energy and the highest stability. Results indicated that the binding of interface Si and C to the O atoms is stronger than that to the Ga atoms. The results of the difference charge density and electron localization function reveals that the Si and C atoms at interface are bonded with O atoms of β-Ga2O3 by covalent bonds, in which Si-O and C-O covalent bonds play a favorable role in the final stable configurations formation. This work will provide a further understanding of the various electronic behaviors of the β-Ga2O3(201)/4H-SiC(0001) heterointerface.

Numerical investigation on hole-injection characteristics of NiO/SiC heterojunction is carried out in this paper. Theory analysis and numerical simulation both indicate the excellent hole-injection characteristic of p-NiO/n-SiC heterojunction. The pn junction diode and pnp phototransistor are constructed and simulated to evaluate hole-injection characteristics p-NiO/n-SiC heterojunction. The results indicate that the p-NiO/n-SiC heterojunction shows great potential advantage in enhancing current gain of pnp phototransistor. By using NiO/SiC heterojunction as the emitter junction, the current gain of SiC based pnp phototransistor can be increased by about 96.3 times.