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Protein, fat and somatic cells are three important reference indicators in milk purchase, which determine the quality and price of milk. The traditional chemical analysis methods of these indexes are time-consuming and pollute the environment, while the mid-infrared spectrum has the advantages of fast, non-destructive and simple operation. In order to realize the rapid classification of milk quality and improve the production efficiency of dairy enterprises, 3216 Holstein milk samples were chosen as the research objects and mid-infrared spectroscopy technology was applied to realize the detection and classification of 4 different quality milks during the purchase process. The spectrum was preprocessed by using the first derivative and the first difference, and combined with the algorithm competitive adaptive reweighted sampling (CARS) and the shuffled frog leaping algorithm (SFLA), the effective characteristic variables that could represent different milks were selected, and the SVM model was established. Among them, the penalty parameter c and the kernel function parameter g which were the key parameters of the SVM model were optimized by using the grid search method (GS), genetic algorithm (GA) and particle swarm algorithm (PSO). The training time of GS, GA and PSO algorithms were compared, the results showed that the training time of GS was much longer than that of GA and PSO algorithms.The SFLA algorithm was generally better than the CARS algorithm, and the PSO optimized the SVM model the best. After the first-order difference preprocessing, the PSO-SVM established by using the SFLA algorithm to filter the characteristic variables, the accuracy of the training set, the accuracy of the test set and the AUC were 97.8%, 95.6% and 0.96489, respectively. This model has a high accuracy rate and has practical application value in the milk industry.

In this study, hyperspectral imaging was used for nondestructive detection of preserved eggs at different maturity levels during the pickling period. First, the optimal waveband was determined based on the one-dimensional spectra and two-dimensional correlation spectra in the time-series mode, separately. Then, the modeling effects of traditional machine learning and the improved ResNet20_SE model in the optimal waveband were compared, and the results showed that the improved ResNet20_SE model was better; the overall recognition accuracy was 97.29% for the synchronous spectral dataset, and the average detection speed for a single image was 24.62 ms. Finally, the better synchronous spectral dataset ResNet20_SE model was applied to the hyperspectral pixel spectral image to calculate the value of each pixel point, and a pseudo-color technique was used for the visual detection of the spatial distribution of preserved egg maturity during the pickling process. The results of this study showed that hyperspectral imaging combined with deep learning is useful for nondestructive detection of preserved egg maturity during curing, which can lay a theoretical foundation for high-throughput online sorting of preserved egg maturity in the future.

The near-infrared spectral data of salted duck eggs, prepared from Gaoyou Ma duck eggs, were collected during the whole curing period and based on them, a model for nondestructive and rapid detection of the key quality indicators of salted duck eggs (yolk moisture content, yolk sodium chloride concentration and salted egg yolk index). In order to reduce the influence of other external factors on spectrum acquisition, various spectral preprocessing methods such as multiplicative scatter correction and normalization combined with three feature selection algorithms including competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and uninformative variables elimination (UVE) were used to establish partial least squares regression (PLSR) models. Furthermore, CARS or UVE combined with SPA was used to establish a more robust model. It was found that the optimal band selection method for the three quality indicators of salted duck eggs was UVE combined with SPA, which had the best overall performance. Comparative analysis showed that the optimal model structures for egg yolk moisture content, egg yolk sodium chloride concentration, and salted egg yolk index were standardization-UVE + SPA-PLSR, Savitzky-Golay-UVE + SPA-PLSR, and mean centering-UVE + SPA-PLSR, respectively. The correlation coefficients were 0.933 4, 0.897 8 and 0.928 6 for the training set (Rc), and 0.927 6, 0.908 5 and 0.916 3 for the prediction set (Rp), respectively. The spectral model established in this study can allow the non-destructive detection of the physical and chemical indicators of salted duck eggs during the salting period.

The presence of cracks reduces egg quality and safety, and can easily cause food safety hazards to consumers. Machine vision-based methods for cracked egg detection have achieved significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches that rely on improving network structures or increasing training data volumes cannot effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown test egg data. Firstly, multi-domain egg data are built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust cracked egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 (You Only Look Once v5) model trained by the proposed multi-domain training method with NSFE-MMD has a detection mAP of 86.6% on the unknown test Domain 4, and the YOLOV8 (You Only Look Once v8) model has a detection mAP of 88.8% on Domain 4, which is an increase of 8% and 4.4% compared to the best performance of models trained on a single domain, and an increase of 4.7% and 3.7% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain training method has a detection mAP of 87.9% on egg data of the unknown testing Domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain training method, which can be more suitable for the large quantity and variety of egg detection production. [ABSTRACT FROM AUTHOR]

Uncontrolled inflammation occurred in sepsis results in multiple organ injuries and shock, which contributes to the death of patients with sepsis. However, the regulatory mechanisms that restrict excessive inflammation are still elusive. Here, we identified an Ig-like receptor called signaling lymphocyte activation molecular family 7 (SLAMF7) as a key suppressor of inflammation during sepsis. We found that the expression of SLAMF7 on monocytes/macrophages was significantly elevated in patients with sepsis and in septic mice. SLAMF7 attenuated TLR-dependent MAPK and NF-[kappa]B signaling activation in macrophages by cooperating with Src homology 2-containing inositol-5'-phosphatase 1 (SHIP1). Furthermore, SLAMF7 interacted with SHIP1 and TNF receptor-associated factor 6 (TRAF6) to inhibit K63 ubiquitination of TRAF6. In addition, we found that tyrosine phosphorylation sites within the intracellular domain of SLAMF7 and the phosphatase domain of SHIP1 were indispensable for the interaction between SLAMF7, SHIP1, and TRAF6 and SLAMF7-mediated modulation of cytokine production. Finally, we demonstrated that SLAMF7 protected against lethal sepsis and endotoxemia by downregulating macrophage proinflammatory cytokines and suppressing inflammation-induced organ damage. Taken together, our findings reveal a negative regulatory role of SLAMF7 in polymicrobial sepsis, thus providing sights into the treatment of sepsis., Introduction Sepsis is one of the leading causes of in-hospital death, but the pathological mechanisms underlying this disease have yet to be uncovered (1). Defining the clinical criteria for the [...]

Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on subjective evaluations, resulting in significant variability. This study focused on hydroponically grown lettuce during the rosette stage and investigated the patterns of changes in the indicators and spectral curves over time. By employing spectral preprocessing and selecting characteristic wavelengths, three models were developed to predict the indicators. The results showed that the optimal model structures were S_G-UVE-PLSR (SSC and vitamin C) and Nor-CARS-PLSR (moisture content). The PLSR models achieved prediction set correlation coefficients of 0.8648, 0.8578, and 0.8047, with residual prediction deviations of 1.9685, 1.9568, and 1.6689, respectively. The optimal models were integrated into a portable device, using real-time analysis software written in Matlab2021a, for the prediction of the physicochemical indicators of lettuce during the rosette stage. The results demonstrated prediction set correlation coefficients of 0.8215, 0.8472, and 0.7671, with root mean square errors of prediction of 0.5348, 1.5813, and 2.3347 for a sample size of 180. The small discrepancies between the predicted and actual values indicate that the developed device can meet the requirements for real-time detection. [ABSTRACT FROM AUTHOR]

Salted egg yolks from salted duck eggs are widely utilized in the domestic and international food industry as both raw materials and ingredients. When salted egg yolks are not fully cured and matured, they exist in a fluid state, with a mixture of solid and liquid internally. Due to this composition, they are susceptible to deterioration during storage and usage, necessitating their detection and classification. In this study, a dataset specifically for salted egg yolks was established, and the ConvNeXt‐T model, employed as the benchmark model, underwent two notable improvements. First, a lightweight location‐aware circular convolution (ParC) was introduced, utilizing a ParC‐block to replace a portion of the original ConvNeXt‐T block. This enhancement aimed to overcome the limitations of convolution in extracting global feature information while integrating the global sensing capability of vision transformer and the localization capability of convolution. Additionally, the activation function was modified through substitution. These improvements resulted in the final model. Experimental results indicate that the enhanced model exhibits faster convergence on the custom salted egg yolk dataset compared to the baseline model. Furthermore, a significant reduction of model parameters by a factor of 4 led to a 2.167 percentage point improvement in the accuracy of the test set. The ParC‐ConvNeXt‐SMU‐T model achieved an accuracy of 96.833% with 26.8 million parameters. Notably, the improved model demonstrates exceptional effectiveness in recognizing salted egg yolks. Practical Application: This study can be widely applied in the process of salted egg yolk production and quality inspection, which can improve the actual sorting efficiency of salted egg yolks and reduce the labor cost at the same time. It can also be used for nondestructive testing of salted egg yolks by governmental enterprises and other regulatory authorities. [ABSTRACT FROM AUTHOR]

The sterilization of liquid eggs plays a crucial role in the production of liquid egg products. Traditional pasteurization techniques can easily cause protein denaturation, while non-thermal sterilization techniques are often constrained by processing intensity and time. Improving the effectiveness of liquid egg sterilization while preserving the stability of its functional attributes poses a significant challenge. In response to this issue, a synergistic ultrasonic mild thermal sterilization system for liquid eggs is proposed, accompanied by the optimization of its process parameters. COMSOL is employed to simulate the acoustic field distribution of the ultrasonic–thermal system in the liquid egg medium. Verification is conducted through acoustic intensity measurements, and analysis is performed to obtain the optimal arrangement of ultrasonic transducers. Based on Modbus communication, an ultrasonic–thermal synergistic sterilization system is designed. Sterilization experiments are conducted with both 20 kHz + 28 kHz and 20 kHz + 40 kHz multifrequency ultrasound, compared with traditional 20 kHz single-frequency ultrasound. The results indicate that multifrequency ultrasound improves sterilization efficiency by approximately 15% compared to traditional single-source ultrasound. Utilizing multifrequency ultrasonic–thermal synergistic sterilization experiments, a three-factor, three-level response surface test is conducted with sterilization rate and foaming properties as evaluation criteria. The results indicate a strong correlation between ultrasonic frequency, processing time, heating temperature, and sterilization performance, with the impact magnitude being sterilization temperature > processing time > ultrasound frequency. Parameter optimization analysis is performed using a genetic algorithm, yielding sterilization conditions of 55 °C, 11 min and 30 s processing time, and 20 + 40 kHz ultrasonic frequency. The liquid egg sterilization rate is 99.32%, an average decimal reduction of 3.17 log values, and foaming properties are 42.79%.Through comparative analysis, it is determined that the sterilization rate of the ultrasonic–thermal synergistic sterilization system meets national standards, and functional properties such as foaming are superior to traditional pasteurization. This validates the proposed ultrasonic–thermal synergistic liquid egg sterilization control system as effective and feasible. [ABSTRACT FROM AUTHOR]

Existing chaotic system exhibits unpredictability and nonrepeatability in a deterministic nonlinear architecture, presented as a combination of definiteness and stochasticity. However, traditional two‐dimensional chaotic systems cannot provide sufficient information in the dynamic motion and usually feature low sensitivity to initial system input, which makes them computationally prohibitive in accurate time series prediction and weak periodic component detection. Here, a natural exponential and three‐dimensional chaotic system with higher sensitivity to initial system input conditions showing astonishing extensibility in time series prediction and image processing is proposed. The chaotic performance evaluated theoretically and experimentally by Poincare mapping, bifurcation diagram, phase space reconstruction, Lyapunov exponent, and correlation dimension provides a new perspective of nonlinear physical modeling and validation. The complexity, robustness, and consistency are studied by recursive and entropy analysis and comparison. The method improves the efficiency of time series prediction, nonlinear dynamics‐related problem solving and expands the potential scope of multi‐dimensional chaotic systems. [ABSTRACT FROM AUTHOR]