European Food Research and Technology最新文献

Culinary spices and herbs are vulnerable to fraudulent practices adulterating or substituting their authentic composition. Recently, we conducted an in-house validation of thirty real-time quantitative polymerase chain reaction (qPCR) methods aiming at detecting and quantifying the top five adulterants of six commonly consumed spices and herbs: paprika/chilli, turmeric, saffron, cumin, oregano and black pepper. Each of the thirteen qPCR methods meeting all the in-house validation criteria have been further tested in an interlaboratory trial including fifteen European laboratories. For each method the participants received DNA templates of binary mixtures for five standard samples together with five test samples of unknown adulterant concentration. Interlaboratory validation parameters included repeatability, reproducibility and trueness. Measurement uncertainties, limits of detection and limits of quantification were also determined. After data examination and outlier removal, relative repeatability standard deviation ranged from 4% to 25%, relative reproducibility standard deviation ranged from 6% to 25% and trueness bias ranged from − 11% to 27%. The thirteen qPCR methods are therefore fully validated and may be included in international standards for deployment in official control laboratories.

To enhance the quality and flavor of fermented jujube juice, this study applied ultrasound treatment (UST) at different fermentation stages and employed a combined flavoromics and metabolomics approach for analysis. The research discovered that applying UST at the 8 h mark (UST 8) was the optimal strategy, significantly enhancing microbial growth, total phenolics, flavonoids, and antioxidant activity. A total of 13 characteristic flavor substances were identified by GC-IMS and GC-MS. UST 8 yielded the most desirable flavor profile, characterized by the highest proportion of 10 key aromatic compounds, including 2,5-dimethylfuran and beta-damasnone. Metabolic pathway analysis revealed that the catabolism of branched-chain amino acids and the degradation of lipids were central to this process, identifying L-valine, DL-leucine, and linalyl acetate as potential flavor biomarkers. This provides a novel processing strategy and a scientific basis for the industrial production of premium functional beverages, directly informing quality control and product development.

This paper designs an integrated framework using machine learning and ensemble models for single and multi-label classification of crops. For both types of classification, we first applied Gaussian Naive Bayes, logistic regression, multilayer perceptron, K-nearest neighbors, decision tree, random forest, and hoeffding tree individually. Next, different ensemble models, including bagging, voting, and stacking, were implemented and evaluated. Bagging classifiers for Decision Trees were tuned for the number of estimators and maximum sample size using a grid search approach. On the other hand, voting classifiers (hard and soft) that combine two weak models with one strong model, and stacking classifiers that use weak models as base models and a strong model as the meta-model, were trained using default hyperparameters. It has been found from the results that for single-label classification, Gaussian Naive Bayes achieved the best accuracy of 99.44% with a kappa of 0.994, mean absolute error of 0.0661, root mean squared error of 0.890, and a time complexity of 0.01 s. The best bagging classifier gave 99.17% accuracy. Voting classifiers reached an accuracy of 99.44% (hard) and 98.89% (soft), while the best stacking model gave 98.07%. In multi-label classification, K-nearest neighbors gave 97.82% accuracy. Bagging and voting achieved accuracies of 98.67% and 98.87%, respectively, and the highest-performing stacking classifiers demonstrated an accuracy of 94.82%. In conclusion, this research examined a variety of accurate and efficient models that help improve productivity and economic growth in agriculture.

Astaxanthin (AST), as a potent natural antioxidant, holds broad application prospects in the functional food sector. However, its poor water solubility, low chemical stability, and insufficient bioaccessibility severely limit its practical efficacy. To overcome this bottleneck, this study developed a novel AST microencapsulation delivery system based on thermally induced gelation of soy protein isolate (SPI) and chitosan (CTS), with structural solidification achieved through freeze-drying technology. Through systematic optimization of the AST-to-wall material ratio (1:2), the prepared microcapsules achieved a 92.63% encapsulation efficiency. Microscopy and spectroscopy approaches confirmed that the microcapsules exhibited a regular spherical structure, with AST uniformly dispersed in an amorphous state within the wall material matrix. In vitro simulated digestion experiments demonstrated that encapsulated AST exhibited a 1.67-fold increase in gastrointestinal retention and a 4.5-fold improvement in bioaccessibility compared to free AST. This enhancement was primarily attributed to the physical protection provided by the wall material and its sustained-release properties in digestive fluids. Long-term stability assessment (26 weeks) further validated the advantages of this delivery system. At 4 °C and 25 °C, AST retention in microcapsules increased by by 38% and 41%, respectively, with half-lives extended to 114 days and 78 days. The SPI/CTS gel template method proposed in this study offers a safe and efficient physical cross-linking strategy for stabilizing AST, achieving synergistic improvements in stability and bioaccessibility without chemical modification. This technological system holds significant application value in the development of nutritional supplements and functional foods, providing a reference model for the delivery of liposoluble active ingredients.

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The higher demand for functional foods, combined with the growing adoption of vegan diets, requires the food industry to develop alternative foods that are not animal-based, utilizing plant-based matrices. Peas, like most legumes, are sources of protein, carbohydrates, and other nutrients. However, this matrix is still poorly explored for the development of fermented non-dairy-like products. The objective of this study was to investigate the potential of pea extracts as a base matrix for the development of fermented and potentially probiotic plant-based products by evaluating their physicochemical and technological properties. Five strains of potentially probiotic lactic acid bacteria (LAB) were evaluated. Fermentation results showed that all strains were able to ferment the pea aqueous extracts, with Lactococcus lactis 32B4-1 (9.51 log CFU/mL) and Lactiplantibacillus plantarum BL011 (9.19 log CFU/mL) presenting the highest growth rates in 12 h. The lactic acid production was observed along with cell growth, reaching a maximum concentration of 4.2 g/L by L. lactis 32B4-1. After fermentation, the antioxidant activity increased by 30.71 to 78.27% and the extracts changed from bright green colour to olive. The changes in volatile compounds were strain dependent. L. lactis 32B4-1 and L. plantarum BL011 showed beneficial changes, altering volatile metabolites, including aldehydes, known to impart an undesirable flavour to legumes. L. lactis 32B4-1 and L. plantarum BL011 showed potential for industrial applications for the preparation of fermented pea extracts and could be further explored to ferment other plant matrices.

Soybean quality is crucial for the industry’s competitiveness, but traditional grading methods are slow, subjective, and inaccurate. Non-destructive technologies combined with machine learning algorithms emerge as promising alternatives to overcome these limitations. This study aimed to investigate the use of Near-Infrared (NIR) Spectroscopy and hyperspectral sensors as fast and accurate tools to analyze the physicochemical properties of soybean grains, as well as to identify the machine learning (ML) models with the highest accuracy in classifying the different types. One hundred whole-grain samples were used for each of Types I, II, Basic Standard, and Off-Type. The moisture, crude protein, starch, lipid, crude fiber, and ash contents were determined using a FOSS NIR DS2500 spectrometer. Hyperspectral reflectance data were acquired with an ASD FieldSpec 4 Jr sensor. The instrument covers the 350 to 2500 nm spectral range. The acquired spectra were processed and averaged into representative intervals prior to model development. Six ML models were tested in WEKA 3.8.6 softwares: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM), Zero-R (SL), J48 Decision Tree (J48), and REPTree, evaluated using the metrics correct classification percentage, Kappa, and F-score. Among the models evaluated, the SVM model showed the best performance in classifying the physicochemical quality of soybean grains, achieving 99.5% accuracy, followed by ANN (88.1%) and J48 (76.4%). For the SVM model, the regularization (C) and radial basis kernel (γ) parameters were maintained at the standard values ​​defined by the WEKA 3.8.6 software. The Basic Standard and Off-Type classes obtained the highest class-specific accuracies among all grain types. The integration of NIR, hyperspectral sensors, and ML enabled the characterization of the physicochemical quality of the soybeans, demonstrating classification efficiency and outperforming conventional methods. The average analysis time per sample was approximately one minute, highlighting the potential of this approach to automate soybean grading, with greater reliability, agility, and standardization of the process.

Cantaloupe Peel (CP) is often discarded as waste, despite its exceptional composition as a by-product. It is rich in polyphenols, tannins, carotenoids, and other bioactive compounds. Cantaloupe Peel Extract (CPE) shows significant potential due to these compounds, offering various health benefits, including anti-obesity, anti-inflammatory, anti-hyperglycemic, immune-boosting, anti-diabetic, anti-cancer, and antioxidant effects. As a functional ingredient in the food industry, CP has valuable applications in pectin and bioethanol production. Additionally, using CPE in seawater offers further advantages, creating functional seawater that can be used for developing new functional foods. Further research is needed to enhance sustainability in industries that utilize CP. Furthermore, utilizing these materials in cosmetics, pharmaceuticals, and environmental management helps reduce waste and promotes the adoption of green technology.

Beer, a globally popular alcoholic beverage, maintains its commercial value through visual clarity, a key quality trait affecting consumer acceptance. As a colloidal system, the non-biological turbidity of beer is a key issue affecting its shelf-life stability. This review elucidates the molecular interactions among essential colloidal components that induce turbidity formation: (1) The formation of chill haze is primarily driven by protein-polyphenol complexes, which arise through hydrophobic interactions and hydrogen bonding. The haze-active proteins include proline-rich prolamins (15–32 kDa), lipid transfer protein 1 (LTP1), protein Z, and serpins, all of which contribute to turbidity. (2) Dextrins, particularly the understudied low-molecular-weight (LMW) fraction, are proposed to exacerbate turbidity via hydrogen-bond aggregation and oxidative cross-linking. Emerging albeit indirect evidence suggests that residual LMW dextrins may act as oxidative stimuli or nucleation sites, interacting synergistically with protein-polyphenol complexes and other polysaccharides to accelerate haze formation. (3) β-glucan and arabinoxylan facilitate particle aggregation by enhancing viscosity and promoting gelation. Environmental factors that affect the stability of beer colloids, such as temperature fluctuations, ionic strength, dissolved oxygen, mechanical vibrations, and light, significantly accelerate the turbidity process. In addition, a comprehensive stabilization strategy was proposed for the above mechanisms, including barley genotype selection and enzymatic clarification technology. As a critical review that systematically integrates existing research, these insights and technological advancements offer solutions for extending beer shelf life while preserving sensory quality, effectively addressing key challenges in modern brewing.

Honey contains a diverse range of phenolic compounds that contribute to its functional properties; however, comparative data linking bee species, geographical origin, and quantitative phenolic profiles of Indonesian honeys remain limited. Accordingly, this study compared six honeys: three from Apis mellifera (Subang, Batang, Pasuruan) and three from Heterotrigona itama (Tanggamus, Banjar, Kapuas Hulu), examining whether species and origin influence composition, function, and relevance for bioresearch. The product quality was evaluated toward SNI 8664:2018; quantified TPC and TFC; assessed antioxidant activity (DPPH, ABTS, FRAP), antibacterial properties (agar-well diffusion), and anti-inflammatory effects (BSA denaturation); characterized targeted phenolics using UPLC–MS/MS; and conducted in silico docking studies on CDK2 (which promotes cancer cell proliferation) and Chk1 (DNA-damage checkpoint) with ADME, brief molecular dynamics, and MM-PBSA analysis. Every sample fulfilled the freshness requirements; darker honeys had lower pH and greater Pfund, and higher TPC/TFC values coincided with stronger antioxidant responses. H. itama Tanggamus (H1) had the highest TPC/TFC and the strongest antioxidant response (DPPH IC₅₀ 10.08 mg mL^− 1), whereas A. mellifera Batang (A2) had the lowest. The honey samples analyzed exhibited more pronounced antibacterial properties against S. aureus compared to E. coli, as evidenced by a larger zone of inhibition ranging from 9.7 to 18.6 mm. Specific honey samples also showed measurable anti-inflammatory effects, with IC₅₀ values of 5.24 mg mL⁻¹ for H1 and 6.38 mg mL⁻¹ for A3. UPLC-MS/MS analysis revealed an increase in taxifolin and benzoic acid derivatives in stingless bee honey. Furthermore, in silico analysis identified 6-phenylisoquinoline from A. mellifera honey harvested in Subang as a potential activator of CDK2/Chk1. Overall, both the type of honey and its geographic origin had a significant impact on the phenolic composition and associated bioactivity. It underscores the importance of confirming the source and highlights the necessity for various quantification techniques and additional cell-based validation. S. aureus exhibited more antibacterial activity than E. coli (9.7–18.6 mm), and anti-inflammatory activity followed suit (H1 IC₅₀ 5.24 mg mL^− 1; A3 6.38 mg mL^− 1). In stingless-bee honeys, UPLC-MS/MS revealed the enrichment of taxifolin and benzoic-acid derivatives, while in silico study revealed 6-phenylisoquinoline from A. mellifera Subang to be a CDK2/Chk1 engager. Species and region structured phenolic profiles and bioactivity, supporting origin verification and motivating orthogonal quantification and cell-based validation.

Edible insects are traditionally consumed in Uganda and are increasingly recognized as nutritionally valuable and environmentally sustainable food resources. In this study, the nutritional composition, chemical profiles, biological activities, and safety of three commonly consumed edible insects—Ruspolia differens (green and brown morphs) and Macrotermes nigeriensis—were comprehensively evaluated. Extraction yields differed markedly among species, with R. differens morphs showing substantially higher methanolic yields (46.74–48.93%) than M. nigeriensis (22.57%). Fatty acid analysis revealed oleic acid as the predominant component (42.2–44.1%), followed by palmitic and linoleic acids, indicating a lipid profile rich in mono- and polyunsaturated fatty acids. Volatile profiling demonstrated species-specific aroma signatures, with M. nigeriensis characterized by acetic acid and pyrazines, while R. differens morphs exhibited high levels of isovaleric acid and diverse aromatic compounds. Amino acid analysis showed that methanolic extracts generally contained higher concentrations and broader profiles than aqueous extracts, with taurine, glutamic acid, alanine, and ethanolamine as the dominant components. Elemental analysis by ICP–MS indicated appreciable levels of essential minerals (K, Ca, Fe, Zn, and Se), while potentially toxic elements (As, Cd, Hg, and Pb) were detected at low and variable concentrations depending on species and morph. In enzyme inhibition assays, R. differens methanolic extracts exhibited strong α-glucosidase inhibition (up to 88.16%), comparable to acarbose, whereas α-amylase and cholinesterase inhibition remained limited. Antioxidant activity was moderate, with M. nigeriensis aqueous extract showing the highest DPPH• and ABTS•⁺ scavenging capacities among samples. Antimicrobial activity was generally weak to moderate, with methanolic extracts showing greater efficacy, particularly against Candida albicans. Cytotoxicity and genotoxicity assessments using human dermal fibroblasts and lymphocytes revealed no significant adverse effects at the tested concentrations, with cell viability remaining above 80% and chromosomal aberration indices comparable to negative controls. Overall, the results demonstrate that Ugandan edible insects possess favorable nutritional profiles, selective bioactivities, and an acceptable preliminary safety profile, supporting their potential as alternative food resources. However, controlled rearing, standardized processing, and further studies on bioavailability and long-term consumption are warranted.

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