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To investigate the effect of dietary addition of Bacillus subtilis on the antioxidant capacity, meat color, protein oxidation, lipid oxidation and sensory quality of pork during storage in high-oxygen modified atmosphere packaging, 12 Duroc × Landrace × Yorkshire pigs were equally allocated into 2 groups that were fed a basal diet alone and in combination with B. subtilis, respectively. Following slaughter, the longissimus dorsi muscle was taken and cut into two parts: one part was used to measure the activity of antioxidant enzymes; the other part was packaged under high oxygen conditions and stored at 4 ℃. The meat color and the contents of malondialdehyde (MDA), carbonyl, and sulfhydryl were measured after 0, 5, 10, 15, and 20 d, and sensory evaluation was performed. The results demonstrated that the activities of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase (CAT) significantly increased in the B. subtilis supplementation group compared with those in the control group (P < 0.05). Furthermore, dietary supplementation with B. subtilis significantly improved the color of pork stored in high-oxygen modified atmosphere packaging (P < 0.05). During low-temperature storage, dietary supplementation with B. subtilis decreased the MDA content (P < 0.05) and inhibited the lipid oxidation of pork stored in high-oxygen modified packaging. In addition, it significantly inhibited the formation of carbonyl compounds and the loss of sulfhydryl compounds (P < 0.05), and significantly increased the sensory color score of high-oxygen modified atmosphere packaged pork and the overall preference score after opening the package (P < 0.05). In summary, dietary addition of B. subtilis could improve the antioxidant capacity of pork, thereby maintaining the color and inhibiting protein and lipid oxidation in pork stored in high-oxygen modified atmosphere packaging, ultimately enhancing the eating quality of pork.

To investigate the mechanism of action of umami-enhancing peptides, molecular simulation and sensory evaluation were employed to analyze the umami-enhancing effects of three peptides derived from chicken: LPLQD, DGGRYY, and DEAGPSIVH, the molecular mechanism of umami-enhancing peptide-glutamic acid-taste receptor interaction and the dynamic binding process. The results indicated that all three umami-enhancing peptides exhibited good water solubility and non-toxicity and had an additive effect on the umami taste of 0.35% glutamic acid solution. Molecular docking results revealed that the presence of umami-enhancing peptides greatly enhanced the interactions between the ligand system and the taste receptor type 1 (T1R1) including electrostatic interaction, hydrophobic interaction, and hydrogen bonding interaction, thereby leading to enhanced binding stability. Furthermore, molecular dynamic simulations disclosed that the venus flytrap (VFT) domain in the T1R1 receptors was relatively stable, while the cysteine-rich domain exhibited significant spatial fluctuations. Statistics of hydrogen bonds indicated that the addition of umami-enhancing peptides to the ligand system enhanced the hydrogen bonding interaction and consequently binding intensity between ligands and receptors. By analyzing the frontier molecular orbitals, serine, tyrosine, glutamine, arginine, and histidine residues were identified as active sites in the peptides that contributed to the binding with T1R1. The above results provide theoretical support for understanding the umami-enhancing mechanism of peptides and for the development of new umami enhancers.

In order to study the aroma-active components of pungent spices and to promote the standardization of their processing and application, this work used solvent-assisted flavor evaporation (SAFE) combined with gas chromatography-mass spectrometry/olfactometry (GC-MS/O) to qualitatively and quantitatively analyze the aroma-active components of 20 pungent spices. Using olfactory analysis, a total of 203 aroma-active compounds with flavor dilution (FD) values ≥ 9 were identified mainly consisting of alcohols, sulfur compounds, olefins, esters, ketones, aldehydes, phenols and acids, and the concentrations of aroma-active compounds varied between different spices. Principal component analysis (PCA) showed that all samples could be well classified into three groups. The aroma-active compounds of garlic, Welsh onion, chive and onion were dominated by sulfur compounds, while the aroma-active components of the other pungent spices were dominated by olefins, alcohols, phenols and ketones. Aroma-active sulfur-containing compounds were detected in garlic at the highest level of 3 484 777.68 μg/kg, including disulphide and trisulphide, contributing to the characteristic flavor of Allium plants. The highest content of aroma-active olefinic compounds of 72 847 224.14 μg/kg was detected in ginger, and the major aroma-active components of ginger were α-gingerene and β-sesquiterpene. Alcohols and ketones were detected in wild mint at high levels of 284 886.09 and 196 167.66 μg/kg, respectively, and the characteristic aroma components of wild mint were menthol and carvone. Aroma-active aldehydes were detected in litsea at high levels of 469 242.68 μg/kg. The major aroma-active components detected in litsea were citral and citronellal. This study provides a theoretical basis for the deep processing of pungent spices.

ObjectiveThe fresh cut rose industry has shown a positive growth trend in recent years, demonstrating sustained development. Considering the current fresh cut roses grading process relies on simple manual grading, which results in low efficiency and accuracy, a new model named Flower-YOLOv8s was proposed for grading detection of fresh cut roses.MethodsThe flower head of a single rose against a uniform background was selected as the primary detection target. Subsequently, fresh cut roses were categorized into four distinct grades: A, B, C, and D. These grades were determined based on factors such as color, size, and freshness, ensuring a comprehensive and objective grading system. A novel dataset contenting 778 images was specifically tailored for rose fresh-cut flower grading and detection was constructed. This dataset served as the foundation for our subsequent experiments and analysis. To further enhance the performance of the YOLOv8s model, two cutting-edge attention convolutional block attention module (CBAM) and spatial attention module (SAM) were introduced separately for comparison experiments. These modules were seamlessly integrated into the backbone network of the YOLOv8s model to enhance its ability to focus on salient features and suppressing irrelevant information. Moreover, selecting and optimizing the SAM module by reducing the number of convolution kernels, incorporating a depth-separable convolution module and reducing the number of input channels to improve the module's efficiency and contribute to reducing the overall computational complexity of the model. The convolution layer (Conv) in the C2f module was replaced by the depth separable convolution (DWConv), and then combined with Optimized-SAM was introduced into the C2f structure, giving birth to the Flower-YOLOv8s model. Precision, recall and F1 score were used as evaluation indicators.Results and DiscussionsAblation results showed that the Flower-YOLOv8s model proposed in this study, namely YOLOv8s+DWConv+Optimized-SAM, the recall rate was 95.4%, which was 3.8% higher and the average accuracy, 0.2% higher than that of YOLOv8s with DWConv alone. When compared to the baseline model YOLOv8s, the Flower-YOLOv8s model exhibited a remarkable 2.1% increase in accuracy, reaching a peak of 97.4%. Furthermore, mAP was augmented by 0.7%, demonstrating the model's superior performance across various evaluation metrics. The effectiveness of adding Optimized-SAM was proved. From the overall experimental results, the number of parameters of Flower-YOLOv8s was reduced by 2.26 M compared with the baseline model YOLOv8s, and the reasoning time was also reduced from 15.6 to 5.7 ms. Therefore, the Flower-YOLOv8s model was superior to the baseline model in terms of accuracy rate, average accuracy, number of parameters, detection time and model size. The performances of Flower-YOLOv8s network were compared with other target detection algorithms of Fast-RCNN, Faster-RCNN and first-stage target detection models of SSD, YOLOv3, YOLOv5s and YOLOv8s to verify the superiority under the same condition and the same data set. The average precision values of the Flower-YOLOv8s model proposed in this study were 2.6%, 19.4%, 6.5%, 1.7%, 1.9% and 0.7% higher than those of Fast-RCNN, Faster-RCNN, SSD, YOLOv3, YOLOv5s and YOLOv8s, respectively. Compared with YOLOv8s with higher recall rate, Flower-YOLOv8s reduced model size, inference time and parameter number by 4.5 MB, 9.9 ms and 2.26 M, respectively. Notably, the Flower-YOLOv8s model achieved these improvements while simultaneously reducing model parameters and computational complexity.ConclusionsThe Flower-YOLOv8s model not only demonstrated superior detection accuracy but also exhibited a reduction in model parameters and computational complexity. This lightweight yet powerful model is highly suitable for real-time applications, making it a promising candidate for flower grading and detection tasks in the agricultural and horticultural industries.

Visual analytics (VA) technology has become a significant tool for business decision-makers to explore novel insights. One of its key features is analytical reasoning through interactive visualization that users have direct sensemaking in problem-solving processes. Few studies have examined how VA can be applied in business education for improving educational purpose. Social cognitive theory grounded in the current study as a framework to guide a qualitative exploration. The investigation was guided by two research questions: (1) What are the benefits and challenges of VA used in business education? (2) How do students interact with VA to improve knowledge construction in problem-solving? In-depth interviews and focus groups were conducted to derive data for thematic analysis from 25 informants. Three researchers conducted coding and data analysis through Nvivo 11.0. Five themes were identified in the corpus of data: (1) Perceived benefits to learning, (2) Prerequisite conditions, (3) Application context, (4) Motivational engagement, and (5) Knowledge construction. The study suggests that incorporating VA technology with problem-solving facilitates students' knowledge organisation and acquisition.