Journal of the Science of Food and Agriculture最新文献

Background: Assamese glutinous Bora rice (Oryza sativa L.) is widely used for various ethnic food preparations. However, its resistant starch (RS) content, which influences the glycemic index (GI), remains poorly characterized. This exploratory study examined nine popular cooking and eating quality (CEQ) traits in 21 Bora rice lines, and performed molecular characterization and expression profiling during grain development, emphasizing machine learning (ML)-based prediction of RS content.

Results: The endosperm of Bora rice lines contains 80% to 90% starch, predominantly amylopectin, with a lower proportion of RS. Low gelatinization temperature, shorter cooking times at boiling temperatures, and soft gel length are key physicochemical traits of this group. Oryza sativa L. 'Aghani Bora' requires 68 minutes to prepare fully at room temperature. This reflects its low gelatinization temperature and soft gel formation, which are characteristic of Bora rice. Glycemic index-linked polymorphic markers can support molecular breeding of Bora rice for low GI. GBSSI and SSIIa transcripts were downregulated in genotypes exhibiting low RS content. Significant correlations were observed among CEQ traits. The radial basis function network model for predicting RS content in Bora rice yielded a high R² (0.9155) and a low mean squared error (0.0690).

Conclusion: Amylose appears to have a critical role in determining most CEQ characteristics but has less influence on readiness to eat. Bora rice requires genetic improvement because its low RS content can lead to a high GI. The low-cost machine learning (ML) model developed in this study provides an effective tool for rapid prediction of RS content in rice and other starchy cereal crops. © 2026 Society of Chemical Industry.

Protein fractionation plays a crucial role in producing high-value protein ingredients, shaping nutritional quality, functionality, and sustainability outcomes. This review examines major fractionation techniques, including physical methods (sieving, air classification, electrostatic separation) and chemical approaches (isoelectric precipitation, alkaline extraction, aqueous fractionation), together with emerging hybrid strategies. Quantitatively, dry fractionation typically delivers 30-60% protein purity at ~0.5-1.0 MJ kg^-1 flour (excluding milling), while wet fractionation can achieve 70-95% purity but requires substantially more resources, including ~2-5 MJ kg^-1 water removed for drying. Hybrid routes offer intermediate or superior performance by combining high purity with lower energy and water use. Applications in bread, pasta, meat analogues, and dairy substitutes demonstrate the functional and nutritional advantages of these protein fractions. A structured database search with defined keywords and criteria ensured methodological transparency. The novelty of this review lies in integrating process efficiency, techno-functional performance, and sustainability metrics into a unified assessment framework, addressing gaps not fully covered in previous reviews. Collectively, these insights highlight the potential of optimized, hybridized fractionation methods to support sustainable and innovative food processing. © 2026 Society of Chemical Industry.

Background: Unsaturated fatty acids (UFAs) in algal oil are prone to oxidation, which leads to the formation of a fishy odor and may reduce product quality significantly. This study investigated the changes in the fishy odor during the oxidation of algal oil.

Results: The results show that the oxidation rate of oil accelerated significantly after 4 days, resulting in the intensification of the fishy odor. Volatile organic compounds (VOCs) were analyzed to identify the off-odor substances and 103 compounds were identified. Among these, (E, E)-2,4-hexadienal had the highest content (216.65 μg g^-1) and a variable importance in projection (VIP) of 5.39, playing a crucial role in the characterization of odor. Combined with odor activity value (OAV) analysis, hexanal, (E)-2-heptenal, (E, E)-2,4-hexadienal, (E, E)-2,4-heptadienal, 1-hepten-3-one, and 1-octen-3-ol were identified as the characteristic components of the fishy odor. Further research revealed that these odor constituents exhibited significant correlations with fatty acid composition. These compounds are derived primarily from the secondary metabolites generated during oxidation of long-chain UFAs in algal oil.

Conclusion: This study clarified the key substances and sources of fishy odor in algal oil. It provides an important basis for the development of odorless algal oil products. © 2026 Society of Chemical Industry.

Honey-bee products offer a broad spectrum of uses in food applications. Bee products, are regarded as excellent sources of high-quality nutrients, coming directly from nature with no need for artificial additives. This notion, in addition to their functional properties, makes them idealsuper foods or sources of supplements with beneficial effects on human health. This review covers the quality and safety aspects of daily usage of bee products, in addition to addressing the hazardous contaminants that may occur in bee products. Extensive studies on the applications of bee products in food preservation report their ability to extend the shelf life of food products due to their antioxidant and antimicrobial activity. Additionally, the levels of chemical contaminants in honey and bee products appear to be very low. Honey and bee products do not pose any threat to human health. © 2026 Society of Chemical Industry.

Alcohol-related liver disease (ALD) is a global health issue with limited therapeutic options. Flavonoids, a diverse group of plant-derived compounds, have been shown to ameliorate ethanol-induced liver injury in both in vivo and in vitro ALD models, highlighting their potential as natural therapeutic agents for ALD. However, the clinical use of flavonoids is hindered by their generally poor solubility and low oral bioavailability. To overcome these challenges, several nanotechnology-based flavonoid formulations, including nanocrystals, liposomes, nanoemulsions, polymer particles, polymer micelles, and nanogels, have been developed. The efficacy of these flavonoid nanoformulations in the context of ALD remains largely unexplored. This review summarizes current knowledge of ALD, the protective effects of various flavonoids, and recent advances in flavonoid nanoformulations. It also discusses future research directions in this field. © 2026 Society of Chemical Industry.

Background: Lutein, a valuable xanthophyll from Chlorella sorokiniana, is vital for ocular and metabolic health. However, lutein degradation under high light or suboptimal nutrient hampers productivity during the growth phase.

Results: This study optimized lutein yield via light modulation (4k-14k lux), intermittent high-intensity (patterned) exposure, and nutrient refinement. Light-dark cycling (18:6, 8k lux) improved the lutein to 65.48 mg L^-1 and increased biomass to 6.12 g L^-1. A patterned 14k lux photobioreactor yielded 69.14 mg L^-1 and 7.01 g L^-1 biomass. Temperature modulation (35 °C) and urea as a nitrogen source under a one-stage bioprocess further increased lutein to 72.45 and 82.60 mg L^-1 and biomass to 6.0-8.0 g L^-1. A two-stage process combining 10k lux light and macro- and micronutrient enrichment achieved a maximum lutein yield of 86.40 mg L^-1 with 8.31 g L^-1 biomass. Compared with the control (62.1 mg L^-1 lutein; 6.75 g L^-1 biomass), the optimized two-stage strategy enhanced lutein production by ~39.1%, while biomass increased by 23.1%, indicating a proportionally higher pigment-to-biomass productivity ratio.

Conclusion: From an economic perspective, the integrated strategy in a two-stage process can reduce costs by 28-32% of lutein, owing to improved nutrient utilization, enhanced energy efficiency in light modulation, and shorter cultivation time. The process thus demonstrates a favorable productivity-to-cost ratio, strengthening the economic feasibility of microalgal lutein production. Integrated light-nutrient strategies effectively enhance lutein production while minimizing degradation. This sustainable approach supports SDG 3 (health), SDG 9 (innovation), and SDG 13 (climate action), paving the way for a scalable microalgae lutein bioprocess. © 2026 Society of Chemical Industry.

Background: In order to investigate the primary factors influencing the germination of Euryale ferox seed, two varieties of E. ferox seed, thorny and thornless, were selected as test materials. After an accelerated germination test, the physicochemical components, enzyme activities, microstructure, and functional properties of the germinate and non-germinate seed kernels were analyzed and compared.

Results: The results indicated that the starch content from germinate seed kernels significantly decreased, whereas the α-amylase activity, protease activity, glucose content, soluble protein content, free amino acid content, and total phenol content all significantly increased (P < 0.05). Compared with non-germinate samples, a greater proportion of spherical aggregates in germinate seed kernels exhibited damage, presumably due to the hydrolytic action of endogenous enzymes. Rapid viscosity analysis demonstrated that germination significantly decreased the peak viscosity, final viscosity, and thermal stability of the seed kernels. In comparison with non-germinate seeds, germinate seed kernels exhibited a substantial enhancement in both solubility and swelling power. Additionally, the proportions of α-helix and β-turn decreased significantly (P < 0.05) in proteins from germinate seed kernels, while the proportions of antiparallel β-sheet, parallel β-sheet, and random coil significantly increased (P < 0.05). Furthermore, the short-range order of starch molecules in germinate seed kernels was reduced.

Conclusion: In summary, the activation and elevated activity of endogenous enzymes, particularly α-amylase and protease, are pivotal drivers of E. ferox seed germination. The process entails extensive component transformation and structural remodeling within the seed kernel, primarily mediated through enzymatic hydrolysis, which collectively determines germination potential. © 2026 Society of Chemical Industry.

Background: Plant-derived proteins are rapidly emerging as innovative ingredients in the food sector because of their sustainability and ethical benefits compared with animal-based proteins. Among dairy applications, fermented beverages are the most suitable products for the incorporation of these proteins. This study evaluated how cold plasma (CP) treatment time and concentrations of modified pea protein isolate (PPI) affected the quality and stability of a hybrid dairy beverage.

Results: Higher PPI levels increased titratable acidity, whereas CP-treated PPI resulted in higher pH values reaching 4.36 at 1.8 g 100 mL^-1. Both PPI concentration and CP treatment improved the water-holding capacity (WHC), with a maximum WHC of 29.68% achieved at 1.8 g 100 mL^-1 PPI and a CP treatment time of 30 s. Longer CP time and higher PPI levels increased a* and b* values significantly (P < 0.05). Viscosity peaked at 214.35 Pa s at 1.8 g 100 mL^-1 PPI and a CP treatment time of 60 s but declined at 120 s. Pea protein isolate also promoted Streptococcus and Lactobacillus spp. growth, especially with shorter CP time (P < 0.05).

Conclusion: These findings suggest that brief CP treatment is an effective approach to modifying PPI, improving its suitability for use in the development of functional hybrid fermented dairy beverages. © 2026 Society of Chemical Industry.

Background: Alkaline salt stress significantly impairs the growth and development of lilies. Although China has abundant wild lily resources, most species are highly sensitive to saline and alkaline stress, leading to a lack of salt-tolerant varieties. Currently, studies on the mechanisms of salt tolerance and salt-tolerance gene mining in lilies remain limited.

Results: In this study, physiological, biochemical and transcriptomic responses of alkaline salt-tolerant Lilium asiaticum and non-alkaline salt-tolerant Lilium davidii var. willmottiae were compared under Na₂CO₃ stress with and without exogenous abscisic acid (ABA) pretreatment. It was found that the alkali tolerance of lily seedlings significantly increased with ABA pretreatment compared to those without ABA, suggesting that a small amount of ABA could mitigate the damage caused by alkaline salt stress. Nitro blue tetrazolium chloride staining confirmed that ABA pretreatment alleviated oxidative damage in stressed seedlings. RNA-sequencing identified 2958 differentially expressed genes (DEGs) in L. asiaticum and 25 927 in L. davidii var. willmottiae, with 1338 commonly expressed genes. DEGs were mainly enriched in organic and cellular metabolism processes. Weighted correlation network analysis revealed lily alkaline salt stress responses primarily involve phytohormone signaling, mitogen-activated protein kinase signaling and starch-sucrose metabolism.

Conclusion: These findings provided valuable insights into the salt stress-related genes and metabolic pathways in lilies, aiding in the exploration of salt tolerance mechanisms, screening for salt tolerance genes and breeding of new salt-tolerant lily varieties. © 2026 Society of Chemical Industry.

Background: Tegillarca granosa is prone to spoilage and deterioration during storage due to the action of microorganisms and enzymes. The traditional shelf-life prediction methods have problems such as strong destructiveness, long time consumption, complex operation and strict requirements for personnel. This study model constructed an intelligent prediction model of T. granosa based on a multilayer perceptron (MLP).

Results: Under different storage temperatures (25, 4, -18 °C), the physicochemical indicators total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS), total viable bacteria count (TVC), and sensory characteristics (color, electronic nose) of T. granosa all showed a deteriorating trend over time. Shelf-life prediction model outputs the shelf life by inputting multidimensional variables such as TVB-N, TBARS, color, electronic nose and TVC. The quality prediction models include three types: predicting the TVB-N values and TBARS values by inputting storage temperature and days; predicting the TVB-N value by inputting the response value of the electronic nose sensor. All the prediction models performed outstandingly, with the coefficient of determination (R²) remaining above 0.98, the mean absolute error controlled within 0.50, and the mean square error within 0.4.

Conclusion: The above results fully verified that the models have good prediction accuracy and stability. By applying machine learning technology, the shelf life and quality dynamic change patterns of aquatic products have been predicted rapidly, accurately, and non-destructively, which has significant practical value for the quality control of aquatic products and the reduction of economic losses. © 2026 Society of Chemical Industry.