NPJ Science of Food最新文献

MicroRNAs (miRNAs), small noncoding RNAs that regulate gene expression, are mediators of intercellular and cross-kingdom communication. They are detected in foods, and studies argue for their uptake by intestinal cells. To investigate the cumulative influence of food-derived miRNAs on human health, we performed a bioinformatic analysis of miRNomes of eight commonly consumed foods: four fruits (apple, banana, grape, orange) and four animal products (beef, chicken, pork, milk). We identified 2 and 4 common miRNAs among the 20 most abundant in fruits and animal foods, respectively. Functional predictions revealed that miRNAs are likely involved in regulating cell adhesion, cellular organization, or metabolism. Several miRNAs were shown, in the literature, when overexpressed, to exert beneficial effects on physiological functions and to contribute to disease prevention. This study suggests that food-derived miRNAs may act as novel dietary bioactives contributing to the health-promoting properties of whole foods, and when these foods are consumed in combinations.

Microscopic impurities can contaminate tea during production, processing, and packaging. Current technologies remove only visible contaminants, leaving microscopic foreign objects that compromise tea quality, and reliable detection methods remain lacking. To address this challenge, we propose YOLOv11-PFT, an improved deep learning model based on YOLOv11, enhanced with Powerful-IoU loss, FasterNet, and Triple Attention modules to boost detection accuracy, reduce model size, and improve feature extraction. The resulting lightweight model achieves 99.16% detection accuracy for microscopic tea contaminants, with Precision, Recall, F₁ score, and mAP all near 98.7-99.2%, GFLOPs of 5.5, inference speed of 340.6 FPS, and a model size of only 5.0 MB. It outperforms seven benchmark models in accuracy. YOLOv11-PFT offers an effective solution for microscopic contaminant detection in tea, supporting automation in food safety, intelligent quality control, and edge-device deployment in agriculture.

Obesity-induced muscle atrophy is a major health issue, in which gut microbiota play a key role in regulating metabolism and muscle health. This study investigated how composite dietary fiber protects against muscle atrophy in mice fed a high-fat diet (HFD). After 24 weeks of obesity induction, mice were divided into two groups: one continued on the HFD, while the other received the HFD supplemented with composite dietary fiber for 8 weeks. Composite dietary fiber ameliorated HFD-induced metabolic dysregulation by reducing adipose accumulation and improving insulin resistance. Notably, composite dietary fiber preserved skeletal muscle mass and function and downregulated the expression of key proteolytic markers Atrogin-1 and MuRF-1. The intervention enriched beneficial gut microbiota, particularly Bifidobacterium and other short-chain fatty acid (SCFA)-producing taxa, and elevated SCFA levels in both the colon and serum, with butyric acid increasing by 123.8% and 19.4%, respectively. PICRUSt2 analysis demonstrated enhanced microbial pyruvate and butanoate metabolism pathways, and correlation analyses revealed close relationships among microbiota, SCFAs, and muscle parameters. Collectively, these data suggest a potential mechanism whereby composite dietary fiber counteracts muscle atrophy in obesity by modulating the gut microbiota to increase SCFA production and downregulate proteolytic signaling, implicating its potential as a dietary intervention for muscle metabolic disorders.

In this study, the internal branched structure of highly branched α-glucans (HBαGs) was regulated by employing glycogen branching enzymes (GBEs) from different microbial sources and amylosucrase, with the purpose of synthesizing structurally diverse α-amylolyzates. Variations in GBE origin resulted in HBαGs with distinct fine structural features, including differences in α-1,6 branching degree and molecular weight. Following hydrolysis with Aspergillus oryzae α-amylase, the resulting α-amylolyzates exhibited a high degree of branching (from 26.6 to 30.9%) and large molecular weight (1.07 × 10⁶ to 1.86 × 10⁷g mol^-1) after removal of linear maltooligosaccharide region. These α-amylolyzates exhibit resistance to α-amylase due to their large molecular size and dense branching structure, and therefore can be hydrolyzed into glucose only by mucosal α-glucosidase complexes, notably from rat intestinal and recombinant human sources. As a result, various tailor-made α-amylolyzate samples, specifically designed based on different HBαGs, showed a significant reduction in the glucose generation rate. This study presents various enzymatic strategies for producing structurally diverse α-amylolyzates, which are slowly degraded in digestive enzymes. These materials extend the region of glucose release and absorption within the small intestine, thereby attenuating glycemic responses and suggesting their potential as functional ingredients for regulating glucose homeostasis.

The limited stability of L. reuteri in liquid formulations during storage, transport, and gastrointestinal transit presents a major challenge for its application as a probiotic. To address this, our study developed two distinct microcapsulation models for L. reuteri A-1, tailored for specific release profiles: slow-release and quick-release model. Utilizing single-factor experiments and response surface methodology, we optimized the encapsulation process, achieving a maximum embedding efficiency of 88.64% for the slow-release model. The quick-release model demonstrated a high cumulative release rate of 83.3%. Structural characterization revealed microcapsules with dense, smooth surfaces and internal porous structures. Storage stability tests confirmed that low temperature (4 °C) best preserved viability. In the DSS-induced murine colitis model, the quick-release model significantly alleviated disease symptoms, including weight loss, colon shortening, inflammatory cytokine imbalance, and mucosal damage. 16S rRNA analysis further showed that the quick-release system helped restore the gut microbiota of colitis mice to a state closer to that of healthy controls. This work establishes a novel technological platform for the controlled release and targeted delivery of probiotics, holding significant promise for the development of live biotherapeutic products.

Accurate identification of the geographical origin of tea leaves is crucial for ensuring quality assurance and traceability within the tea industry. This study introduces Origin-Tea, a novel lightweight convolutional neural network that innovatively combines depthwise separable convolutions with squeeze-and-excitation (SE) attention mechanisms to effectively capture subtle phenotypic variations while minimizing computational costs. Unlike prior approaches that depend on heavy architectures or handcrafted features, Origin-Tea is explicitly designed for efficiency and interpretability in agricultural applications. Comprehensive ablation studies confirm the significant contribution of each architectural component to the model's robust performance. The dataset comprises 900 high-resolution RGB images of Yunkang 10 tea leaves, independently collected from seven distinct regions in Yunnan Province. A 10-fold stratified nested cross-validation (CV) was employed, with one-fold designated for testing, one for validation, and the remaining eight for training in each iteration. Data augmentation techniques, including flipping, rotation, and exposure adjustments, were applied solely to the training set to enhance model robustness without compromising the intrinsic phenotypic features. Origin-Tea achieved an average overall accuracy (OA) of 0.92 ± 0.03 and a Kappa coefficient of 0.90 ± 0.03, outperforming the best-performing baseline, CoAtNet (OA = 0.89 ± 0.03), by 3.37% accuracy while reducing parameters by over 90% (1.7 M versus 17 M). Furthermore, in an independent test on 1788 scanner-captured images from four villages, Origin-Tea demonstrated excellent generalization with an OA of 0.97. These results highlight the model's potential as a scalable, field-deployable solution for intelligent tea provenance verification and precision phenotyping.

This study evaluates the feasibility of producing nozzle-less eugenol-infused gelatin nanofibers as active coatings to improve fish quality and shelf life during cold storage. Nanofibers were fabricated using nozzle-less electrospinning with gelatin concentrations of 10-20% and gelatin:eugenol ratios of 100:0 to 50:50. The optimal formulation-20% gelatin with a 50:50 ratio-achieved >99.98% encapsulation efficiency and the smallest average fiber diameter (91.11 ± 18.53 nm). FTIR confirmed strong hydrogen bonding between gelatin and eugenol, while TGA indicated improved thermal stability. Higher eugenol loading enhanced antioxidant activity, limiting lipid oxidation to <0.15 mg MDA/kg compared with >1 mg MDA/kg in controls after 3 days. Microbial counts in coated fish at day 7 (2.80 log CFU/g) remained lower than uncoated samples at day 3 (3.88 log CFU/g). The coating also preserved texture and color throughout storage. Overall, these findings highlight the potential of bioactive nanofiber coatings for food preservation and waste reduction.

Non-nutritive sweeteners are widely used in multiple diets and are considered a healthy alternative for pregnant women reducing sugar consumption, thereby preventing maternal obesity and gestational diabetes. While some controversies have been raised regarding their safety for human consumption, particularly aspartame, the effects of aspartame on prenatal brain development have rarely been studied. In this study, we investigated whether maternal aspartame consumption within a physiologically relevant range for normal daily consumption by humans affects neocortical development in mice. Here we show that daily maternal aspartame consumption at 18% of the European Food Safety Authority-approved daily dosage does not significantly alter the structure of the somatosensory cortex, nor the numbers of excitatory neurons, inhibitory neurons, astrocytes, or oligodendrocytes in mouse pups less than 48 hours old. These results suggest that aspartame has no detectable impact on somatosensory cortex development in mice. This study provides additional information that can be utilized by expectant mothers making choices about their diet.

Brazilian seasonings exhibit a rich variety of flavors that are largely attributed to terpenes. However, the systematic identification of the key aroma-active terpenes in these seasonings are poorly identified, lacking characterization by sustainable and high-resolution analytical methods. This study aimed to develop an integrated, environmentally friendly analytical protocol to characterize aroma-active terpenes in 26 traditional Brazilian seasonings. A novel solvent-minimized extraction method, hydrophilic microporous cartridge for direct immersion solid phase microextraction (HMCart-DI-SPME), was optimized by a factorial design for efficient recovery of volatile and semi-volatile terpenes. Analysis by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC/MS) identified 125 terpenes from five classes. Odor activity values (OAVs) were calculated for 48 compounds to assess their contribution to the aroma profile. Notably, high OAVs of eucalyptol, linalool, pulegone and geraniol confirmed their central role in the seasoning's sensory properties. The integrated strategy combining HMCart-DI-SPME, GC×GC/MS and OAV analysis enabled an unprecedented resolution of these complex matrices. This methodology offers an effective, environmentally friendly alternative for flavor analysis, contributing to the chemical valorization of Brazilian biodiversity. The robust data produced has significant potential applications in the food, pharmaceutical and fragrance industries.

To help counteract food fraud and meet consumer expectations, the pork industry requires reliable quality-monitoring and traceability systems. In this context, rapid evaporative ionisation mass spectrometry (REIMS) could be rolled out as a real-time, accurate metabolic fingerprint-based classifier of pork meat characteristics and quality issues, such as genetic origin and boar taint. Here, fingerprinting of >3000 pig neck fat samples enabled highly accurate pig breed classification (pairwise comparison of Commercials (Pietrain × Hampshires × Durocs, Large-Whites, Durocs), Hampshires and Large-Whites, where data modelling using support vector machine (SVM, all pairwise comparisons > 89%) and orthogonal partial least squares-discriminant analysis (OPLS-DA, >90%) outperformed random forest (RF, 72.0-79.5%). Boar taint classification showed comparable results between OPLS-DA, RF and SVM (93.5-96.0%), but it was important to apply strategies to avoid false negatives and positives, including the construction of balanced models (tainted vs. non-tainted).