Tiger nut (Cyperus esculentus L.) meal peptides (TMP) are bioactive peptides prepared by hydrolyzing tiger nut meal-a by-product of tiger nut oil extraction-with alkaline protease. These peptides have demonstrated promising immunomodulatory potential. In this study, proteins were extracted from tiger nut meal using the alkali dissolution and acid precipitation method. The enzymatic hydrolysis conditions were then optimized through single-factor experiments and response surface methodology. The optimal hydrolysis conditions were determined as follows: hydrolysis time 4h, pH 10, enzyme dosage 9000 U/g, and temperature 46°C. Under these conditions, the degree of hydrolysis reached (24.87 ± 0.91%). Subsequently, a peptide fraction with a molecular weight of <3kDa was obtained via ultrafiltration. In vitro, this fraction significantly promoted the proliferation of mouse splenic lymphocytes and induced the secretion of pro-inflammatory cytokines, including interleukin-2 (IL-2), interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α). In vivo experiments showed that TMP effectively improved immune function in cyclophosphamide (CTX)-induced immunosuppressed mice, as indicated by increased immune organ indices, elevated serum levels of both pro-inflammatory cytokines (IL-2, IFN-γ, and TNF-α) and the anti-inflammatory cytokine interleukin-4 (IL-4), and alleviated histopathological damage in spleen tissue. This study provides theoretical support for the development of TMP in immunomodulatory products and expands the potential application of tiger nut by-products in the functional food industry.

Lipid-coated aging has been proposed as an alternative to enhance flavor, particularly using butter as a coating material (butter-aged beef). Thus, this study compared the wet-aged and beef-aged with different lipid-based coatings: milk butter (butter-aged), cocoa butter (cocoa-aged), pork lard (lard-aged), and beef tallow (tallow-aged), all after 8 days of aging. Beef portions with lipid coatings developed cracks, particularly those coated with milk butter (16.7%), cocoa butter (100%), and beef tallow (100%), causing meat surface darkening. In lipid-coated samples, total weight loss (14.3%-16.6%) and yield (83.4%-85.7%) were not affected by treatments. Water activity was higher in initial samples (0.9909) compared to lipid-coated samples (0.9884-0.9890) (p<0.05). The aging process, with or without lipid coating, increased lightness and decreased redness, yellowness, and chroma compared to initial samples. Display storage time decreased L*, a*, and chroma values and increased hue angle, particularly after 6 days compared to Day 0 (p<0.05). Sensory evaluation revealed significant differences among treatments for aroma, flavor, and overall impression. Butter- and lard-aged samples showed slightly higher aroma and flavor scores compared to tallow-aged samples. Lipid-coated aging influenced the volatile profile. Principal component analysis revealed that wet-, butter-, cocoa-, and lard-aged samples were more associated with positive descriptors, which were positively associated with overall impression scores, whereas tallow-aged samples were associated with less desirable descriptors such as "bitter" and "rancid." In conclusion, lipid coatings promoted sensory modifications, supported by differences in volatile compounds, but did not improve consumer acceptance compared to wet-aged samples. PRACTICAL APPLICATIONS: Lipid-coated beef aging represents an alternative approach to explore modifications in aroma-related attributes of aged meat. Under the conditions evaluated in this study, this technique promoted significant changes in aroma, flavor, and overall impression, which were supported by differences in volatile compound profiles. However, these changes did not translate into substantial improvements in overall consumer acceptance compared to conventional wet aging. This technique may be of interest for exploratory or niche applications focused on flavor differentiation or product innovation. However, the practical advantages of lipid-coated aging remain dependent on processing conditions and should be further investigated.

Melting-flesh nectarines undergo rapid postharvest softening. 1-Methylcyclopropene (1-MCP) effectively delays this process yet may suppress flavor development. Here, we integrated texture parameters, volatile profiles (83 compounds; SPME-GC-MS), and lipid profiles (234 species; LC-MS/MS) from yellow-fleshed nectarines under Control and 1-MCP treatments over an 8-day ambient shelf life. 1-MCP extended the acceptable firmness window and delayed the C6-aldehyde-to-lactone flavor transition. Lipidomic profiling identified 234 lipid species across five classes and 23 subclasses, dominated by glycerolipids (38.9%) and glycerophospholipids (37.2%). During storage, 192 species (82.1%) were differentially accumulated, featuring coupled glycerophospholipid degradation and triacylglycerol accumulation. Double bond index analysis further revealed class-specific unsaturation remodeling. Spearman correlation (|ρ|>0.8, FDR<0.05) yielded 454 strong volatile-lipid pairs (62.8% negative) and 70 texture-metabolite pairs. Mantel test, canonical correlation analysis, and Procrustes analysis confirmed robust inter-omics associations. In the correlation network, γ-octalactone and γ-decalactone emerged as hub nodes linking lipid metabolism to flavor dimensions. γ-Decalactone exhibited the strongest firmness correlation among all metabolites (ρ=-0.94), suggesting potential as a nondestructive softening indicator. Unsaturation-stratified analysis revealed that glycerophospholipid monounsaturated fatty acid (MUFA) species (double bond=1) exhibited the strongest flavor associations, whereas class-level lipid totals were nonsignificant. This highlights molecular species specificity in lipid-flavor linkages. Machine learning identified four consensus markers (d-limonene, monogalactosyldiacylglycerol [MGDG] 36:4, MGDG 36:6, and phosphatidylethanolamine 43:2) that discriminate Control from 1-MCP-treated fruit, providing molecular targets for preservation optimization. PRACTICAL APPLICATIONS: The strong correlation between γ-decalactone and firmness (ρ=-0.94) suggests that gas-sensor or electronic-nose detection of this peach-aroma volatile could enable nondestructive assessment of nectarine softening. The correlation network further suggests that membrane lipid catabolism is closely associated with both lactone-based flavor development and texture loss, providing a mechanistic basis for optimizing 1-methylcyclopropene (1-MCP) dosage and timing to balance firmness retention with flavor preservation. The four consensus markers may additionally serve as molecular references for shelf-life prediction and quality grading. Regarding sensor-based implementation, the wide dynamic range of γ-decalactone observed in this study (<1 to ∼900ng/g fresh weight) and its high concentration at marketable softening are favorable for electronic nose detection; however, practical deployment would require standardized headspace sampling protocols and cultivar-specific calibration.

This study investigates the effects of various meat sources, including beef, pork, lamb, and chicken, on the quality and digestion characteristics of meat noodles with a meat content of 50% and compares them with traditional meat-free noodles made from semolina flour. Results indicated that the addition of meat significantly reduced the dough's elasticity (p < 0.05) and affected some of the cooking properties to varying degrees, except in chicken noodles. Among the samples, chicken noodles exhibited better cooking quality than others, with a cooking loss of 2.1%, a broken strip rate of 6.7%, and a water absorption rate of 30.0%. Conversely, the incorporation of other meat sources adversely affected cooking performance. In vitro digestion assessments revealed that all meat noodles exhibited lower digestion rates compared with wheat flour noodles, which is important for increasing satiety in modern diets. Notably, chicken noodles had a larger average particle size of digested chyme and exhibited slower release of free reducing sugars and free amino acids, likely due to the higher myofibrillar protein content and myosin proportion, which may facilitate the formation of a denser composite structure with glutenin in semolina flour. The myosin provided a more unfolding nature, which may enhance hydrophobic interactions and disulfide bonds with glutenin, thereby contributing to the superior performance. The deeper insights into how the meat source affects the digestive and physical properties of meat doughs and noodles are promising for the design and development of meat-incorporated foods.

This study successfully developed a high internal phase emulsion (HIPE) stabilized by Polygonatum cyrtonema polysaccharide (PCP) for the efficient delivery of Lactobacillus acidophilus (La). Characterization of the extracted polysaccharide revealed an average molecular weight of approximately 1.27×105Da. Ultraviolet-visible (UV-Vis) scanning and Fourier-transformed infrared (FTIR) spectra confirmed the high purity of PCP and revealed its characteristic pyranose and furanose ring structures. Atomic force microscopy observation showed a non-helical, entangled network morphology. The emulsion system maintained a probiotic survival rate of 63.75% under simulated gastrointestinal digestion, retained 50.2% viability after heat treatment at 70°C, and preserved stable bacterial counts after 28 days of storage at 4°C, demonstrating significantly superior protection (p<0.05) compared to the gum arabic-based system. Mechanistic analysis indicated that the amphiphilic structure of PCP enables the formation of dense interfacial films and gel networks, effectively shielding the probiotics from environmental stress. These findings confirm the potential of PCP-based HIPEs as a green and efficient probiotic delivery carrier for functional food applications.

In recent years, the application of athermal processing methods has gained increasing attention as a promising approach in preserving temperature-sensitive health-beneficial phytochemicals in food and beverage products, especially those containing naturally derived ingredients. This study focused on exploring the impacts of emerging athermal juice processing methods, including forward osmosis concentration and ultrasonic-assisted sterilization, on the phenolic and flavonoid content of apple juice products, while also employing the Box-Behnken experimental design approach to optimize key parameters of these processing methods for the purpose of maximizing phytochemical retention. Overall, under optimized conditions, these athermal processing methods were capable of preserving over 84% of the total phenolic and flavonoid content of apple juice (388.61 ± 7.82 mgGAE/L and 288.03 ± 8.22 mgQE/L, respectively, compared to 423.65 ± 5.48 mgGAE/L and 339.54 ± 3.03 mgQE/L for untreated apple juice samples). Noticeably, these values were markedly higher than most pre-packaged apple juice products currently available in Vietnam; other than one single brand, the highest values of total phenolic and flavonoid content recorded for PP products were only 346.25 ± 1.59 mgGAE/L and 203.29 ± 0.59 mgQE/L, respectively. This result highlighted the need to modernize manufacturing facilities in order to better satisfy the expectations of increasingly health-conscious consumers. PRACTICAL APPLICATIONS: Apple juice concentrates are the main ingredient for most reconstituted clear apple juice products. This study demonstrated the applicability of emerging athermal processing methods in the manufacturing of apple juice concentrates that are both shelf-stable and nutritious, retaining the majority of phytochemicals existing in natural apple juice. Through comparing nutritious contents with commercial apple juice products currently available in the Vietnam market, especially products from domestic brands, it was evidenced that such an athermal approach could be employed by local manufacturers in order to improve the quality of their products.

Noodles were prepared by replacing wheat flour with 0%-25% Sedum aizoon L. (FSAL) powder, and the effects of FSAL addition on dough rheology, gluten structure, noodle quality, descriptive sensory attributes, antioxidant activity, and in vitro starch digestibility were investigated. In parallel, gluten proteins were characterized by Fourier transform infrared spectroscopy, ultraviolet-visible (UV-Vis) spectroscopy, intrinsic fluorescence, SDS-PAGE, surface hydrophobicity, sulfhydryl/disulfide content, and noncovalent interaction analysis. The data indicated that FSAL markedly affected starch thermal transitions, with retrogradation enthalpy decreasing from 9.5J/g for the control sample to 5.8J/g after FSAL incorporation. Moderate FSAL addition improved several cooking and textural quality attributes, and the FSAL20 sample exhibited a relatively balanced descriptive sensory profile, with high perceived intensities of hardness, elasticity, chewiness, taste, and aroma. At elevated inclusion levels, however, disruption of the gluten network was observed, indicative of weakened protein organization and a decline in cooking, textural, and sensory quality attributes. FSAL also enhanced the antioxidant capacity of noodles, with DPPH and ABTS radical scavenging activities increasing from 42% and 12% in the control to 63% and 80%, respectively, at 25% FSAL addition. In vitro digestion showed that resistant starch increased from 49% to 63%, whereas rapidly digestible starch decreased from 33% to 12%. Collectively, these results indicate that FSAL influences noodle quality primarily by altering gluten conformation and intermolecular interactions within the dough matrix. The present results support the use of FSAL powder as an effective functional component in cereal products, and a 20% inclusion level achieved a suitable balance between the descriptive sensory attribute profile and functional benefits. PRACTICAL APPLICATIONS: Appropriate incorporation of Sedum aizoon L. powder improves noodle texture, antioxidant capacity, and dietary fiber enrichment while reducing starch digestibility in vitro. These results support the development of functional noodle products with enhanced nutritional value and potential commercial application.

Efficient recovery of phenolic compounds is critical for valorizing Crataegus songarica K. Koch for food and nutraceutical applications. Conventional maceration (CM) is often limited by low extraction efficiency and prolonged processing time, whereas energy-assisted techniques may enhance extraction performance but still lack comprehensive data-driven assessment. In this study, conventional maceration, ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and infrared-assisted extraction (IRAE) were comparatively evaluated using total identified phenolics and individual flavonoids as response variables. Under the investigated extraction conditions, MAE achieved the highest phenolic recovery (158.82 ± 4.37mg 100 g- 1) following 0.5min of microwave irradiation combined with a subsequent reflux-assisted extraction stage, compared with 70.15 ± 2.14mg 100 g- 1 obtained by CM after 60min, while IRAE and UAE yielded 129.77 ± 3.18 and 83.32 ± 2.11mg 100 g- 1, respectively. Chemometric analyses, including principal component analysis and hierarchical cluster analysis, clearly differentiated extraction modes, primarily according to temperature, solvent concentration, and dry-matter content. Predictive modeling was performed using Ridge regression, random forest, support vector machine, XGBoost, and multilayer-perceptron artificial neural network models under nested cross-validation conditions. Among the tested approaches, XGBoost provided the strongest predictive performance (cross-validated R2 ≈ 0.76, RMSE ≈ 27mg 100 g- 1, MeanAE ≈ 21mg 100 g- 1) and identified temperature and dry matter as the dominant variables controlling phenolic recovery. The results demonstrate that integrating controlled extraction experiments with interpretable machine learning approaches provides a scalable and potentially resource-efficient framework for optimizing phenolic extraction from hawthorn and related bioactive plant materials.

This study investigated the influence of mass ratios on the properties of soybean peptide aggregate (SPA)-soluble soybean polysaccharide (SSPS) composite particles (CPs) fabricated under ultrasonic assistance. The physicochemical and emulsifying properties of CPs were systematically evaluated. Results indicated that the mass ratio played a critical role in determining CPs characteristics. Increasing SSPS content initially led to an enlargement in particle size, followed by a subsequent reduction. Both ζ-potential and three-phase contact angle (θ) decreased with higher SSPS content, with a θ of 87.26° observed at a 1:1 ratio. CPs with SPA-SSPS ratios of 3:1 and 2:1 exhibited lower emulsifying activity and stability compared to SPA alone, whereas ratios between 1:1 and 1:3 demonstrated significantly enhanced emulsifying performance. Mechanistic studies revealed that nanoparticle formation was primarily driven by electrostatic interactions and hydrogen bonding, as confirmed by ζ-potential and Fourier-transform infrared spectroscopy analyses. Atomic force microscopy further indicated the structural encapsulation of SPA by SSPS. Among the tested ratios, the emulsions stabilized by CPs with a 1:1 ratio displayed the smallest droplet size, highest viscosity, and maximal free fatty acid release rate, alongside improved viscoelastic properties (G' and G″) and phase stability. However, higher SSPS ratios were associated with increased free radical content. PRACTICAL APPLICATIONS: These findings provide new insights into the role of mass ratios in modulating the functional properties of SPA-SSPS CPs and offer a promising strategy for developing high-performance Pickering emulsifiers in food and pharmaceutical applications.

Accurate determination of rice protein is essential for quality control. This study evaluated machine learning models (partial least squares regression, PLSR; support vector machine, SVM) combined with feature selection algorithms (random frog, RF; competitive adaptive reweighted sampling, CARS; Monte-Carlo uninformative wavelength elimination, MCUVE) and explainable artificial intelligence method (SHapley Additive exPlanations, SHAP) analysis to predict protein content. Results indicated that CARS-PLSR achieved the highest accuracy (RMSEP=0.266, R2P=0.976, residual prediction deviation [RPD]=6.612). Statistical analysis (F-test and t-test) verified the model's reliability. Furthermore, SHAP analysis revealed that wavelengths at 1218, 1688, and 1209nm made the highest contributions, corresponding to N-H and C-H vibrations. This study not only provides a rapid detection method but also elucidates the chemical basis of the model's high predictive performance. PRACTICAL APPLICATIONS: This study provides a high-speed, non-destructive method for accurately measuring rice protein content, allowing food processors to monitor grain quality in real-time. By using "explainable" AI to reveal the specific chemical markers driving the results, this technology offers a transparent and reliable alternative to slow, expensive laboratory testing.