In response to the current problems of low production efficiency, discontinuous batches and difficult quality control in industrialized dish stir-frying equipment, based on the research of the influence of multi-parameter coupling of cooking time, power and speed on cooking quality, a drum-type industrialized continuous stirfrying equipment suitable for Chinese-style meat dish stir-frying is developed and its performance is verified. The stir-frying process is simulated using EDEM, and the stirring effect and stir-frying time corresponding to different scraper angles are compared. The results show that the stirring effect is the optimal when the scraper angle is 22.5 degrees, and the stir-frying time is the shortest when the angle is 45 degrees. Taking pork tenderloin as the research object and the comprehensive quality of stir-frying as the optimization index, orthogonal experiments and response surface analysis are conducted. The experimental results demonstrate that under optimal processing conditions (specifically a scraper angle of 22.5 degrees, electromagnetic power of 29.2 kW, and stirring speed of 29.6 rpm), the comprehensive quality of pork tenderloin slices achieves its maximum value. The verification experiments indicate that the error between the experimental values and the predicted values is less than 5 %. Comparative experiments show that this equipment's stir-fried pork tenderloin comprehensive quality is 54 % of traditional techniques, but its production efficiency is 7 % higher than that of commercial stir-frying equipment of the same specification. It has improved the processing efficiency while maintaining a certain quality. The development of this equipment provides a new idea for the continuous processing of Chinese cuisine.

Accurate segmentation of key parts in the chicken carcass is crucial for intelligent cutting systems in the modern poultry processing industry.However,the false detection,missed detection,and inaccurate segmentation can occur in key parts of chicken carcasses under complex industrial scenarios(e.g.,adhesion between wings and drumsticks,occlusion,and uneven lighting).This study aimed to develop a lightweight,high-precision,and real-time instance segmentation model suitable for deployment on intelligent chicken carcass cutting equipment.An enhanced dataset was constructed for chicken carcasses,with emphasis on Sanhuang and white-feathered chickens.Furthermore,the 109 0 original images were expanded into 545 0 images,thus covering three types of scenarios:ambient lighting,carcass occlusion,and compression-induced deformation.Multi-dimensional data augmentation techniques such as geometric transformation,illumination adjustment,and occlusion simulation were adopted to improve the model's robustness.DEF-YOLO-seg model was developed to improve the YOLOv12n-seg as the baseline:(1)C3k2_DAttention module was designed to fuse the C3k2 module with Deformable Attention(DAttention),which replaced the Area-Attention Enhanced Cross-Feature(A2C2f)module in the lower layer of the backbone network for the feature extraction in the adhered/occluded regions;(2)Efficient Up-Convolution Block(EUCB)was introduced to replace the Upsample module in the neck network,thus reducing computational cost for the feature fusion efficiency;(3)A composite loss function(Focaler-CIoU)with Focaler-IoU and CIoU was constructed for the distribution of easy and difficult samples under complex scenarios.Finally,model training and testing were completed on a hardware platform with an NVIDIA RTX 3090 GPU and an Intel Xeon Platinum 8362 CPU.The DEF-YOLO-seg model achieved a mean Average Precision at an IoU threshold of 0.5(mAP50)of 95.5％and a mean Average Precision at IoU thresholds from 0.5 to 0.95(mAP50-95)of 94.1％,which were 1.3 and 2.8 percentage points higher than those of the baseline YOLOv12n-seg,respectively.With a parameter count of 3.3M and a computational complexity of 11GFLOPs,the model's inference time per image on a local computer was no more than 30 ms.Compared with mainstream models,such as YOLOv9c-seg,YOLOv11n-seg,and YOLOv12n-seg,the improved model maintained lightweight for the superior segmentation accuracy.Furthermore,the parameter sensitivity analysis revealed that the optimal Focaler-CIoU configuration(d=0.22,u=0.73)precisely matched the IoU distribution of chicken carcass data.The task-specific loss function was designed for rather than generic settings.There was the an image-level accuracy of 95.0％.Dice coefficients of the neck,wings,and drumsticks increased from 0.85,0.83 and 0.78 to 0.93,0.92 and 0.90,respectively,in the practical production line.The improved model was effectively reduced the missed detection,false detection,and false segmentation of small parts(e.g.,neck and shank)under adhesion and occlusion.The DEF-YOLO-seg model also achieved the a better balance among segmentation accuracy,real-time performance,and deployment feasibility in intelligent cutting equipment for chicken carcasses.The findings can provide the technical support to the intelligent upgrading for theof food processing.Future research can focus on cutting path planning,as well as the balance between lightweight and detection accuracy.

Reduced graphene oxide (rGO) plays a critical role in enhancing the performance of electrochemical sensors through its excellent electrical conductivity, large surface area, ease of functionalization, chemical stability, and synergy with other nanomaterials. In this paper, A novel electrochemical sensor was developed based on rGO and molecularly imprinted polymer (MIP) for detection of trace oxytetracycline (OTC) in water. The MIP/rGO composites are synthesized by in-situ electrochemical modification on a disposable screen-printed electrode (SPE). Due to the large surface area and good electron transport rate of rGO, as well as the specific recognition for TC of MIP, the sensitive determination of trace TC at MIP/rGO/SPE was realized. Linear sweep voltammetry (LSV) was performed on the sensor for the electrochemical analysis of OTC. Under optimized conditions, the peak current of MIP/rGO/SPE exhibited a good linear correlation with OTC concentration in a wide linear range (1-25 µmol/L), possessed a detection limit of 0.34 µmol/L. Spiked detection of OTC was applied for the sensor in actual water samples, with relative standard deviations (RSDs) of 93.2%-108.7%. These results indicate that the proposed MIP/rGO/SPE sensor has the ability to accurately, rapidly and selectively detect OTC in water samples.

The study was designed to investigate the mechanism of Riboflavin (RF)-mediated UVA photosensitive oxidation on beef myofibrillar proteins (MP) oxidized at different storage times. To elucidate the direct relationship between RF and protein oxidation, the mechanism of action was analyzed in terms of amino acid and side chain residues, protein structure, and protein oxidative metabolism. Oxidation of MP resulted in significant changes in the levels of carbonyls, sulfhydryls, Lysine, Arginine, Threonin, and Histidine. The oxidized MP secondary structure was changed, fluorescence intensity decreased, and surface hydrophobicity increased. Metabolomics results revealed that RF-mediated UVA photosensitized oxidation is primarily mediated by Riboflavin metabolism and co-regulated with Phenylalanine metabolism. Moreover, with the increase of frozen storage time, Arginine and proline metabolism was inhibited, and the contents of creatine were significantly reduced, which exacerbated MP oxidative damage. The results provide a theoretical basis for unraveling the mechanism of RF-mediated UVA photosensitive oxidation of MP.

Heavy metals expose great hazards to the ecological environment and public health due to their high toxicity and non-biodegradability. In this work, a low-cost, portable origami electrochemical microfluidic paper-based analytical device (E mu PAD) was developed using nitrogen-doped graphene (NG) for simultaneous detection of Cd(II), Pb(II), and Hg(II) in water. The origami structure functions as a valve between sample introduction and detection, reducing sample volume and enhancing operation convenience. Electrochemical linear scanning voltammetry was employed for the first time in the in-situ synthesis of NG on paper-based electrode for sensing heavy metal ions. With its large specific surface area and numerous active sites, NG-modified electrodes exhibited excellent analytical performance for simultaneous determination of Cd(II), Pb(II), and Hg(II). The E mu PAD sensor exhibited a wide linear response ranging from 5 to 100 mu g/L, and low detection limits (Cd(II): 0.5698 mu g/L, Pb(II): 0.4024 mu g/L, Hg(II): 0.2565 mu g/L,), which were well below the drinking water standards. Additionally, the E mu PAD has been successfully applied to real water samples, achieving recoveries of 96.4 % similar to 106.2 % with RSDs below 7 %.

In the field of intelligent meat processing, the application of 3D laser scanning technology for identifying meat contours is essential for the accurate estimation of meat volume and quantitative slicing. In this study, based on 3D laser scanning technology, a contour adaptive shaping unit was developed to address the issue of scanning irregular meat contours and improve the scanning range and volume estimation accuracy. Moreover, we developed software for contour visualization imaging and volume estimation of raw meat using Halcon and Visual C# to optimize the scanning imaging and volume estimation of pork belly, hind shank, and pork loin. The results showed that the optimal shaping angles for chilled and frozen pork (loin, hind shank, and belly) were 60(degrees), 30(degrees), and 30(degrees), respectively, with a scanning accuracy of >= 90 % and an average increase of 3 %. After shaping, the volume forms of the three types of raw meat remained stable for 12, 9, and 6 s in the chilled and frozen state. The corresponding coefficients of variation (CV) of imaging accuracy were 0.77 %, 1.16 %, and 0.54 %, respectively, with high stability and consistency of the imaging accuracy. In addition, the pork color and the transfer speed did not significantly affect the imaging performance of the adaptive shaping system (p > 0.05). The results demonstrated that the adaptive contour shaping system exhibited superior optimization capabilities in raw meat imaging, which provided the technical basis for the subsequent research and development pertaining to adaptive quantitative slicing devices for raw meat of various specifications.

To address the issues of difficulty in quantifying the skills involved in cooking process of chefs and the unclear formation mechanism for dish quality, this study designed a multi-dimensional information sensing platform using infrared thermal camera (IR) and a nine-axis inertial measurement unit (IMU). This platform can achieve real-time collection of the chef's stir-frying frequency and the degree of uniform heating of ingredients. The stirfrying frequency was analyzed in conjunction with the power spectral density function, while the Composite Uniformity Index (CUI) was proposed based on the temperature field distribution to achieve a digital characterization of the heating uniformity of the ingredients. The results showed that higher stir-frying frequencies improved heating uniformity and accelerated protein denaturation but increased moisture loss, making the meat firmer. Moreover, we found that the stir-frying frequency stabilizes at 2 Hz and maintains the best quality of the dish. Notably, higher stir-frying frequencies correlated with increased production of volatile compounds, including particularly 3-methylbutanal and 3-hydroxy-2-butanone, and enhanced decomposition of esters and degradation of 1-pentanol. This study elucidates the role of heating uniformity in shaping meat texture and flavor by analyzing the stir-frying frequency, providing a theoretical foundation and technical support for the standardization of stir-frying processes.

Tibetan pork is a local Chinese pig breed that is raised in high-altitude environments. This study examined the formation pathways of lipid-derived odour-active volatile compounds in Tibetan pork across three cooking methods: sous-vide (SV), pan-frying (PF), and oven-roasting (OR), by investigating the pro-oxidative roles of free iron and changes of lipid molecules. It was found that lipid-derived volatiles in low-temperature methods were generated in two stages: initially from free fatty acids and lysophosphatidylcholines, and later from the oxidation of phospholipids, with the later stage being affected by free iron, which followed by increasing lipid oxidation properties. Conversely, high-temperature methods such as PF and OR led to rapid lipid oxidation at the beginning of cooking, producing odours from eicosanoids, triglycerides, and phospholipids, while their oxidation was less impacted by free iron. The lipid-derived compounds were probably the intermediate products in the development of Strecker- and Maillard-derived volatiles during high-temperature cooking.

This study aimed to explore the mechanism of cooking intensity on the tenderness of stir-fried pork slices from the perspective of the changes in temperature distribution. Infrared thermal imaging was used to monitor the distribution of temperature. Results showed that the high-level heat (HH) treatment could improve tenderness. When the center temperature increased to 100 C, the shear force of samples from the low-level heat (LH) group increased by around 3-fold, and HH reduced this upward trend. This result was mainly attributed to the shorter heating time undergone by the HH-treated samples compared to the LH treatment, which resulted in less structural shrinkage and faster passing through the protein denaturation interval of the samples. These changes alleviated temperature fluctuations caused by water loss. This explanation could be confirmed by the results of T2 relaxation time and Fourier transform-infrared spectroscopy (FT-IR). However, the LH treatment caused a slower rise in oil temperature due to more moisture migration, which required the samples to undergo longer thermal denaturation, leading to a deterioration in tenderness. Moreover, histological analysis revealed that the greater integrity of endomysium in the HH group inhibited water loss and oil absorption, which contributed to obtain low-fat meat products with higher tenderness. This study provides support for the industrialization of traditional pork cuisines using oil as the heating medium.

Agricultural products are frequently contaminated by mycotoxins. Multiplex, ultrasensitive, and rapid deter-mination of mycotoxins is still a challenging problem, which is of great significance to food safety and public health. Herein, a surface-enhanced Raman scattering (SERS) based lateral flow immunoassay (LFA) for the simultaneous on-site determination of aflatoxin B1 (AFB1) and ochratoxin A (OTA) on the same test line (T line) was developed, in this study. In practice, two kinds of Raman reporters 4-mercaptobenzoic acid (4-MBA), and 5,5 '-dithiobis-(2-nitrobenzoic acid) (DTNB) encoded silica-encapsulated gold nanotags (Au4-MBA@SiO2 and AuDNTB@SiO2) were used as detection markers to identify the two different mycotoxins. Through systematic optimization of the experimental conditions, this biosensor has high sensitivity and multiplexing with the limits of detection (LODs) at 0.24 pg mL-1 for AFB1 and 0.37 pg mL-1 for OTA. These are far below the regulatory limits set by the European Commission, in which the minimum LODs for AFB1 and OTA are 2.0 and 3.0 mu g kg -1. In the spiked experiment, the food matrix are corn, rice, and wheat, and the mean recoveries of the two my-cotoxins ranged from 91.0% +/- 6.3%-104.8% +/- 5.6% for AFB1 and 87.0% +/- 4.2%-112.0% +/- 3.3% for OTA. These results demonstrate that the developed immunoassay has good stability, selectivity, and reliability, which can be used for routine monitoring of mycotoxin contamination.

Three-dimensional (3D) porous graphene-based materials have displayed attractive electrochemical catalysis and sensing performances, benefiting from their high porosity, large surface area, and excellent electrical conductivity. In this work, a novel electrochemical sensor based on 3D porous reduced graphene (3DPrGO) and ion-imprinted polymer (IIP) was developed for trace cadmium ion (Cd(II)) detection in water. The 3DPrGO was synthesized in situ at a glassy carbon electrode (GCE) surface using a polystyrene (PS) colloidal crystal template and the electrodeposition method. Then, IIP film was further modified on the 3DPrGO by electropolymerization to make it suitable for detecting Cd(II). Attributable to the abundant nanopores and good electron transport of the 3DPrGO, as well as the specific recognition for Cd(II) of IIP, a sensitive determination of trace Cd(II) at PoPD-IIP/3DPrGO/GCE was achieved. The proposed sensor exhibited comprehensive linear Cd(II) responses ranging from 1 to 100 μg/L (R2 = 99.7%). The limit of detection (LOD) was 0.11 μg/L, about 30 times lower than the drinking water standard set by the World Health Organization (WHO). Moreover, PoPD-IIP/3DPrGO/GCE was applied for the detection of Cd(II) in actual water samples. The satisfying recoveries (97–99.6%) and relative standard deviations (RSD, 3.5–5.7%) make the proposed sensor a promising candidate for rapid and on-site water monitoring.

The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation.

Understanding and predicting the storage stability of sweetcorn seeds is critical for effective supply chain management, however, prediction ability relies heavily on accelerated ageing (AA) studies and this is not always directly applicable to natural ageing (NA). In this study, hyperspectral imaging (HSI) and non-targeted metabolomics (LC-MS/MS) were integrated using PLS-R, SVM-R and OPLS-DA to predict loss of seed vigour in NA seeds, using data based on AA seeds. The inconsistencies in the pattern of spectral variation between seeds undergoing AA and NA were first identified. AA-based vigour prediction models were then built using all wavelengths and effective wavelengths (EWs) selected by regression coefficients. These models were externally validated by independent AA and NA seed datasets, respectively. The results yielded satisfactory predictions for AA seeds (R2 & GE; 0.814), but low precision for NA seeds (R2 & LE; 0.696). Metabolome analysis identified 54 differential metabolites, containing a large proportion of amino acids, dipeptides and their derivatives, which were important substances reflecting discrepancies between the ageing mechanisms of AA and NA seeds. Subsequently, N-H bond-related wavebands were deemed to be a possible interference factor in the models' practicability. After removing the N-H bond-related EWs, the AA-based models achieved better performance on NA seeds, with R2v-2 value increasing from 0.696 to 0.720 for Lvsechaoren and from 0.668 to 0.727 for Zhongtian 300. In summary, coupling HSI, LC-MS/MS and machine learning was shown as an appropriate approach for nondestructive monitoring and predicting the vigour of stored sweetcorn seeds.

The total viable count (TVC) of bacteria is an important index to evaluate the freshness and safety of dishes. To improve the accuracy and robustness of spectroscopic detection of total viable bacteria count in a complex system, a new method based on a near-infrared (NIR) hyperspectral hybrid model and Support Vector Machine (SVM) algorithms was developed to directly determine the total viable count in intact beef dish samples in this study. Diffuse reflectance data of intact and crushed samples were tested by NIR hyperspectral and processed using Multiplicative Scattering Correction (MSC) and Competitive Adaptive Reweighted Sampling (CARS). Kennard–Stone (KS) and Samples Set Partitioning Based on Joint X-Y Distance (SPXY) algorithms were used to select the optimal number of standard samples transferred by the model combined with root mean square error. The crushed samples were transferred into the complete samples prediction model through the Direct Standardization (DS) algorithm. The spectral hybrid model of crushed samples and full samples was established. The results showed that the Determination Coefficient of Calibration (RP2) value of the total samples prediction set increased from 0.5088 to 0.8068, and the value of the Root Mean Square Error of Prediction (RMSEP) decreased from 0.2454 to 0.1691 log10 CFU/g. After establishing the hybrid model, the RMSEP value decreased by 9.23% more than before, and the values of Relative Percent Deviation (RPD) and Reaction Error Relation (RER) increased by 12.12% and 10.09, respectively. The results of this study showed that TVC instewed beef samples can be non-destructively determined based on the DS model transfer method combined with the hybrid model strategy. This study provided a reference for solving the problem of poor accuracy and reliability of prediction models in heterogeneous samples.

近年来自热食品产业发展迅猛,但其质量安全问题一直备受社会关注.通过概述自热食品产业发展的现状,从产业实践的角度系统分析了自热食品中各组件存在的质量安全风险问题,阐述了目前出台的自热食品监管政策及现行标准,同时对自热食品标准发展趋势进行了探讨,并提出相应的对策和建议,以期为行业企业开展安全生产,监管部门强化监督,保障消费者的食品安全提供参考.

Interleukin-6 (IL-6) is generally used as a biomarker for the evaluation of inflammatory infection in humans and animals. However, there is no approach for the on-site and rapid detection of IL-6 for the monitoring of mastitis in dairy farm scenarios. A rapid and highly sensitive surface enhanced Raman scattering (SERS) immunofiltration assay (IFA) for IL-6 detection was developed in the present study. In this assay, a high sensitivity gold core silver shell SERS nanotag with Raman molecule 4-mercaptobenzoic acid (4-MBA) embedded into the gap was fabricated for labelling. Through the immuno-specific combination of the antigen and antibody, antibody conjugated SERS nanotags were captured on the test zone, which facilitated the SERS measurement. The quantitation of IL-6 was performed by the readout Raman signal in the test region. The results showed that the detection limit (LOD) of IL-6 in milk was 0.35 pg mL−1, which was far below the threshold value of 254.32 pg mL−1. The recovery of the spiking experiment was 87.0–102.7%, with coefficients of variation below 9.0% demonstrating high assay accuracy and precision. We believe the immunosensor developed in the current study could be a promising tool for the rapid assessment of mastitis by detecting milk IL-6 in dairy cows. Moreover, this versatile immunosensor could also be applied for the detection of a wide range of analytes in dairy cow healthy monitoring.

为解决光谱数据差异导致模型不稳定的问题,研究了不同批次中式菜肴营养素含量预测模型的传递方法.以间隔3个月制作的番茄炒蛋样本为例,采集光谱数据并利用理化方法测定蛋白质含量(每批次120个样本);选择预测效果较好的第二批次模型作为主模型,将分段直接标准化(Piecewise Direct Standardization,PDS)算法、模型更新(Model Updating,MP)和斜率/截距(Slope/Bias,S/B)修正法联合(PDS-MP-S/B)用于菜肴类模型传递,分析不同PDS窗口数和标准集数对预测结果的影响.当PDS窗口数为11且标准集数为100时,PDS-MP-S/B算法对蛋白质含量的预测结果明显优于无模型传递和单独使用3种算法时,预测模型的预测集决定系数R2(Pred)为0.9628,相对预测偏差(Rela-tive Prediction Deviation,RPD)为 5.6731,预测均方根误差(Root Mean Square Er-ror of Prediction,RMSEP)为0.3157.从光谱、模型、结果三个方面实现了模型传递,提高了模型的通用性,减少了建模成本,为中式菜肴的快检提供了理论支持.

有机酸是一种重要的酸味剂,在肉类保鲜、抑菌、风味改善等方面发挥积极作用,有机酸的适当添加有助于稳定及提升肉类品质.在肉类加工过程中加入有机酸,具有改善肉类颜色,嫩化肉类,降低蒸煮损失,改善肉类营养成分,抑制病原微生物生长繁殖,降低脂质氧化水平,延长肉类产品货架期等优点.但是有机酸浓度过高则会降低肉类品质.本文介绍了肉制品加工中几种常见有机酸及有机酸处理对肉类食用品质、加工品质、营养品质、卫生品质、饲养品质的影响,并对其功能、存在问题进行总结,提出解决措施,以期为肉品工业的发展提供理论参考依据.

近些年,具有高效、无损、实时在线等特点的光学检测技术在肉品检测领域快速发展,如光谱成像技术、机器视觉等,实现了多指标的同步检测及检测结果的可视化.文章综述了基于光学技术的可视化检测方法在评估肉品品质方面的应用,总结了不同可视化检测方法的特点、存在的问题并阐明了其发展趋势,以期为我国肉品品质高效、快速检测装备的研发提供参考.