Foods最新文献

Intensive livestock and aquaculture systems require high-quality feeds with the correct nutritional composition. The decrease in wild fish proteins has led to demands within the feed supply chain for new alternatives to fulfil the growing demand for protein. In this context, edible insects like the yellow mealworm (Tenebrio molitor) have the greatest potential to become a valid alternative source of proteins. This study evaluated the growth performance and nutritional profile of yellow mealworm larvae reared under laboratory conditions on eight different agro-industrial by-products: wheat middling, durum wheat bran, rice bran, hemp cake, thistle cake, dried brewer's spent grains, dried tomato pomace, and dried distilled grape marc. The quantitative and qualitative impacts of rearing substrates on larvae were compared. The results showed that larvae adapt well to different substrates with different nutritional compositions, including the fibrous fraction. However, substrates affect larval growth feed conversion and larval macro composition. Hemp cake stood out for its superior nutritional value, as reflected by its high protein content and moderate NDF (Neutral Detergent Fiber) levels, which determine fast larval growth. On the contrary, imbalanced substrate lipid or carbohydrate content (rice bran), as well as the presence of potential antinutritional compounds (thistle cake), appeared to negatively affect growth performances.

High culture medium costs economically constrain bacterial cellulose (BC) production. In parallel, agro-industrial wastes are plentiful but often underutilised sources of carbon and nitrogen substrates that could support microbial growth and metabolite production. This study aimed to bioconvert agro-industrial waste sustainably into BC using response surface methodology. A novel lactic acid bacterium, Levilactobacillus brevis DSS.01, isolated from nata de coco wastewater, was evaluated alongside Acetobacter tropicalis KBC and Komagataeibacter xylinus TISTR 086 for BC production using Australian agro-industrial wastes. Preliminary screening identified pear pomace and rice bran as optimal low-cost carbon and nitrogen sources, respectively. The response surface methodology employing Box-Behnken Design determined the optimal agro-industrial waste medium composition for L. brevis DSS.01 to produce BC at 1.56 ± 0.15 g/L. The optimised agro-industrial waste medium substituted 85% of standard Hestrin-Schramm medium components, suggesting a significant reduction in culture medium and production costs. Scanning electron microscopy revealed BC fibres from L. brevis DSS.01 maintained a uniform diameter. Fourier transform infrared spectroscopy and X-ray diffraction analyses indicated minimal structural deviation in BC produced from optimised agro-industrial waste medium versus standard medium. These findings demonstrate economic and sustainable BC production through valorisation of agro-industrial residues, establishing lactic acid bacteria as alternative BC producers with potential food-grade applications in circular economy frameworks.

Food waste remains a significant challenge in the European Union, reflecting structural differences across economic sectors and member states. This study examines how macroeconomic conditions relate to sectoral food waste using harmonized Eurostat data for the EU-27, covering five stages of the food chain and three economic indicators: GDP (Gross Domestic Product) per capita, adjusted gross disposable income per capita, and the Harmonized Index of Consumer Prices for food. The research design integrates factor analysis, structural equation modeling, and hierarchical clustering. Results show that income-related variables have a positive, statistically significant effect on overall food waste, particularly in manufacturing and distribution. In contrast, food prices show a negative, statistically non-significant relationship with waste generation. Cluster analysis identifies two statistically distinct country groups; however, substantial internal heterogeneity indicates that these clusters reflect structural economic configurations rather than typological or behavioral categories. The findings suggest that macroeconomic factors partially explain cross-country differences in food waste and support the need for context-sensitive, sector-specific policy interventions.

Accurate segmentation of adhered (sticky) corn kernels and reliable damage detection are critical for quality control in corn processing and kernel selection. Traditional watershed algorithms suffer from over-segmentation, whereas deep learning methods require large annotated datasets that are impractical in most industrial settings. This study proposes W&C-SVM, a hybrid computer vision method that integrates an improved watershed algorithm (Sobel gradient and Euclidean distance transform), convex hull defect detection and an SVM classifier trained on only 50 images. On an independent test set, W&C-SVM achieved the highest damage detection accuracy of 94.3%, significantly outperforming traditional watershed SVM (TW + SVM) (74.6%), GrabCut (84.5%) and U-Net trained on the same 50 images (85.7%). The method effectively separates severely adhered kernels and identifies mechanical damage, supporting the selection of intact kernels for quality control. W&C-SVM offers a low-cost, small-sample solution ideally suited for small-to-medium food enterprises and breeding laboratories.

Temperature and relative humidity can significantly affect quality of paddy rice during storage. Limited studies established the link between storage time, environmental fluctuations, changes in grain and flour physicochemical properties, and culinary performances. In a West African context, IR 841 paddy rice variety was stored under humid-sub-humid (HSH), and dry (DRY) conditions for 12 months. Over 12 months, rice stored under DRY conditions experienced greater environmental fluctuations than rice stored under HSH conditions. Grain water absorption capacity (WAC) increased during storage under DRY conditions, rising from 3.3 ± 0.3 to 3.8 ± 0.3 g/g DM between 0 and 12 months. Flour amylose content and soluble solids remained relatively stable from month 0 to 6 in all conditions, and further under HSH conditions. The observed changes led to improved grain cooking performance after 6 months of storage under DRY conditions. After 12 months, a decrease in rice flour WAC and a peak in viscosity were observed, while mean particle size increased from 42 ± 1 to 67 ± 3 μm under HSH conditions and from 31 ± 3 to 83 ± 3 μm under DRY conditions. Storage time may reduce the breadmaking capacity of rice flour. Overall, environmental fluctuations under DRY conditions strongly affected rice grain and flour properties.

The quality and safety of grain and oil food are paramount to sustainable societal development and public health. Implementing early warning analysis and risk control is critical for the comprehensive identification and management of grain and oil food safety risks. However, traditional risk prediction models are limited by their inability to accurately analyze complex nonlinear data, while their reliance on centralized storage further undermines prediction credibility and traceability. This study proposes a deep learning risk prediction model integrated with a blockchain-based traceability mechanism. Firstly, a risk prediction model combining Grey Relational Analysis (GRA) and Bayesian-optimized Tabular Neural Network (TabNet-BO) is proposed, enabling precise and rapid fine-grained risk prediction of the data; Secondly, a risk prediction method combining blockchain and deep learning is proposed. This method first completes the prediction interaction with the deep learning model through a smart contract and then records the exceeding data and prediction results on the blockchain to ensure the authenticity and traceability of the data. At the same time, a storage optimization method is employed, where only the exceeding data is uploaded to the blockchain, while the non-exceeding data is encrypted and stored in the local database. Compared with existing models, the proposed model not only effectively enhances the prediction capability for grain and oil food quality and safety but also improves the transparency and credibility of data management.

A major challenge for food-derived bio-nanomaterials is achieving consistent and predictable functional properties to ensure their quality. Ginger-derived exosome-like nanovesicles (GELNs) serve as an ideal model for this challenge, yet the impact of ginger geographical origin on GELNs remains unknown. This study aims to establish a quality control framework for food-derived bio-nanomaterials. GELNs were comprehensively analyzed. Untargeted metabolomics identified differential metabolites, which were then screened for correlation with antioxidant capacity. Machine learning was employed to pinpoint potential quality markers, and Kyoto Encyclopedia of Genes and Genomes enrichment analysis highlighted key metabolic pathways. Significant variations in physicochemical properties and bioactivities were observed. We identified 190 differential compounds and established a panel of 6 potential quality markers. Enrichment analysis revealed eight key pathways, with "microbial metabolism in diverse environments" and "galactose metabolism" being most prominent. The quality marker mollicellin I (derived from Chaetomium brasiliense) provided empirical support linking GELNs quality to geography-specific microbiota. Our findings provide evidence that the geographic origin of raw materials is a primary determinant of GELNs quality, based on a systematic analysis of their chemical and functional properties. We develop a transferable quality control framework, laying the groundwork for producing superior natural food-derived nanomaterials.

This study develops a comprehensive workflow integrating Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) with advanced supervised machine learning to authenticate the botanical origin of honeys from five distinct floral sources-coriander, orange blossom, astragalus, rosemary, and chehelgiah. While HS-SPME-GC-MS combined with traditional chemometrics (e.g., PCA, LDA, OPLS-DA) is well-established for honey discrimination, the application and direct comparison of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Neural Network (NN) models represent a significant advancement in multiclass prediction accuracy and model robustness. A total of 57 honey samples were analyzed to generate detailed volatile organic compound (VOC) profiles. Key chemotaxonomic markers were identified: anethole in coriander and chehelgiah, thymoquinone in astragalus, p-menth-8-en-1-ol in orange blossom, and dill ester (3,6-dimethyl-2,3,3a,4,5,7a-hexahydrobenzofuran) in rosemary. Principal component analysis (PCA) revealed clear separation across botanical classes (PC1: 49.8%; PC2: 22.6%). Three classification models-RF, XGBoost, and NN-were trained on standardized, stratified data. The NN model achieved the highest accuracy (90.32%), followed by XGBoost (86.69%) and RF (83.47%), with superior per-class F1-scores and near-perfect specificity (>0.95). Confusion matrices confirmed minimal misclassification, particularly in the NN model. This work establishes HS-SPME-GC-MS coupled with deep learning as a rapid, sensitive, and reliable tool for multiclass honey botanical authentication, offering strong potential for real-time quality control, fraud detection, and premium market certification.

Pulses (edible dry seeds from legumes) are among the most important crops worldwide. These legumes contain a diverse range of carbohydrates, some of which, such as RFOs (raffinose family oligosaccharides), are considered antinutritional factors due to their negative impact on digestion. An analytical method based on high-power ultrasound-assisted extraction and HPLC analysis was developed and validated for the quantitative determination of soluble carbohydrates (verbascose, stachyose, raffinose, sucrose, galactinol, glucose, galactose, fructose, and myo-inositol) in common beans (Phaseolus vulgaris) and peas (Pisum sativum). The proposed method is fast (extraction time: 1 min), reproducible (RDS: 6.9%), accurate (97.5%), and environmentally sustainable. The method was applied to local collections of P. vulgaris (n = 12) and P. sativum (n = 34), revealing similar qualitative profiles but notable quantitative differences. In P. vulgaris, sucrose and stachyose were predominant, while in P. sativum, verbascose stood out. The total sugar content was higher in peas, especially in commercial varieties, which also showed elevated sucrose levels. Some local varieties combined high sugar content with favorable relative levels between RFOs and other sugars, making them valuable candidates for breeding programs. Linear discriminant analysis enabled classification and prediction of species and varieties, confirming the usefulness of soluble carbohydrates as tools for characterizing these plant materials.

In this study, we prepared edible films using chitosan/gelatin/phlorotannins (CGPs) embedded with probiotics and evaluated their preservation effects on strawberries. Edible films encapsulating Limosilactobacillus fermentum FUA033 (CGPFUA033) were prepared using the casting method. The intermolecular interactions, crystal structure, thermal stability, and morphology of the films, both prior to and following the incorporation of L. fermentum FUA033, were characterized using FT-IR, XRD, TG, and SEM analyses. The preservation efficacy of the edible films, with and without encapsulated L. fermentum FUA033, was assessed by monitoring the physical, chemical, and microbial properties, as well as the visual quality, of strawberries during a eight-day storage period. The results showed that encapsulation of L. fermentum FUA033 enhanced intermolecular interactions and thermal stability within the film matrix but did not significantly affect the crystalline structure of the edible film. At 0, 2, 4, 6, and 8 days, the CGPFUA033 treatment had preservation effects: the weight loss was 30.70 ± 1.53%, the total soluble solid content was 8.83 ± 0.28%, the decay index was 45.33 ± 1.53%, the malondialdehyde content was 7.44 ± 0.13 μmol/g, firmness was 21.49 ± 0.83 N, and the ascorbic acid content was 43.51 ± 0.79 mg/100 g. The shelf life of strawberries was extended by six days in the CGPFUA033 treatment group. Therefore, the chitosan/gelatin/phlorotannin edible film embedded with L. fermentum FUA033 has high preservation effects on strawberries, highlighting that L. fermentum FUA033 can be used as a probiotic for enhancing food preservation.