Antifungal Activity of Aromatic Plants of the Lamiaceae Family in Bread.

The antifungal effect of aromatic plants (oregano, thyme, and Satureja) in dry form and as essential oils was evaluated in vitro (in potato dextrose agar (PDA)) and in bread against two phytopathogenic fungi found in food (Aspergillusniger and Penicillium). Gas and liquid chromatography were used to analyze essential oils attained by hydrodistillation of the aerial parts of the aromatic plants and of the dried plant aqueous solutions that were autoclaved for 20 min at 121 °C before analysis. Carvacrol, α-pinene, p-cymene, and γ-terpinene were the main components of the essential oils, whereas carvacrol, rosmarinic and caffeic acids were the main components of the water extracts. In vitro antifungal test results showed that the addition of plants in dry form had great antifungal potential against both fungal strains studied. Penicillium was more sensitive to the presence of aromatic plants than Aspergillus. Among the three plant species tested, thyme was the most potent antifungal against both fungi. For the bread product, all three aromatic plants studied showed inhibitory effects against both fungi. Results presented here suggest that oregano, thyme and Satureja incorporated in a bread recipe possess antimicrobial properties and are a potential source of antimicrobial ingredients for the food industry.

Self-Nanoemulsifying Drug Delivery Systems (SNEDDS) Containing Rice Bran Oil for Enhanced Fenofibrate Oral Delivery: In Vitro Digestion, Ex Vivo Permeability, and In Vivo Bioavailability Studies.

Lipid-based drug delivery systems (LbDDS), such as self-nanoemulsifying drug delivery systems (SNEDDS), constitute a prominent formulation approach for enhancing the aqueous solubility and oral bioavailability of poorly water-soluble compounds. Utilization of biorefinery wastes, such as oil from rice bran, may prove advantageous to both improving drug solubilization and absorption and to achieving sustainable agri-food waste valorization. Here, we assessed the effect of four SNEDDS compositions differing in the oil (rice bran oil and corn oil) and surfactant type (Kolliphor RH40 and EL) on the oral bioavailability of fenofibrate, a BCS class II compound. Prior to the in vivo oral administration of the SNEDDS in rats, drug solubilization was tested in vitro using the static digestion model, followed by the ex vivo permeability study of the predigested SNEDDS using the non-everted gut sac model. No significant variation was observed in the solubilization capacity within the different SNEDDS formulations. On the other hand, the ex vivo permeability data of the predigested SNEDDS correlated well with the in vivo bioavailability data designating the superiority of rice bran oil with Kolliphor EL as the surfactant, to enhance the oral absorption of fenofibrate. Results indicated that valorization of agro-industrial waste such as rice bran oil may prove useful in enhancing the oral performance of LbDDS in the case of fenofibrate, while at the same time maximizing the use of agricultural by-products via the creation of new sustainable value chains in the pharmaceutical field.

Predicting rice blast disease: machine learning versus process-based models.

BACKGROUND: In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. RESULTS: Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant "signals" during the "early warning" period, with a similar level of performance. M5Rules and WARM gave the maximum average normalized scores of 0.80 and 0.77, respectively, whereas Yoshino gave the best score for one site (Kalochori 2015). The best average values of r and r2 and %MAE (Mean Absolute Error) for the machine learning models were 0.70, 0.50 and 0.75, respectively and for the process-based models the corresponding values were 0.59, 0.40 and 0.82. Thus it has been found that the ML models are competitive with the process-based models. This result has relevant implications for the operational use of the models, since most of the available studies are limited to the analysis of the relationship between the model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. CONCLUSIONS: Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and - in cases when training datasets are available - offer a potentially greater adaptability to new contexts.

Impact of the combination of sourdough fermentation and hop extract addition on baking properties, antioxidant capacity and phenolics bioaccessibility of rice bran-enhanced bread.

The effect of hop sourdough fermentation combined with rice bran (RB) addition was investigated for improving technological, bioactive and sensory properties of wheat bread by optimizing the quantities of RB, yeast, sourdough and hop extract contents based on response surface methodology. The bread produced according to the optimized formula was compared with control bread, sourdough or RB-enhanced bread with regard to quality attributes, bioaccessible phenolics and antioxidant activity. Sourdough fermentation combined with RB flour increased volume of loaves and consumer acceptance with no significant variations in crumb moisture content and crumb firmness during storage. RB darkened crumb while no effects on colour were observed for sourdough bread. Sourdough technology allowed increasing phenolic content of RB bread with no significant differences in its antioxidant activity. The bioaccessible ferulic and sinapic acids, the main phenolics released in vitro digestion of optimized bread were lower compared to those of control bread.

Development and Characterization of a Self-Nanoemulsifying Drug Delivery System Comprised of Rice Bran Oil for Poorly Soluble Drugs.

Poor aqueous solubility and low bioavailability are limiting factors in the oral delivery of lipophilic drugs. In a formulation approach to overcome these limitations, rice bran (RB) oil was evaluated as drug carrier in the development of self-nanoemulsifying drug delivery systems (SNEDDS). The performance of RB in formulations incorporating Kolliphor RH40 or Kolliphor EL as surfactants and Transcutol HP as cosolvent was compared to a common oil vehicle, corn oil (CO). Serial dilutions of the preconcentrates were performed in various media [distilled water and simulated intestinal fluids mimicking fasted state (FaSSIF) and fed state (FeSSIF)] and at different dilution ratios to simulate the in vivo droplets' behavior. The developed SNEDDS were assessed by means of phase separation, droplet size, polydispersity index, and ζ-potential. Complex ternary diagrams were constructed to identify compositions exhibiting monophasic behavior, droplet size < 100 nm, and polydispersity index (PDI) < 0.25. Multifactor analysis and response surface areas intended to determine the factors significantly affecting droplet size. The oil capacity to accommodate lipophilic drugs was assessed via fluorescence spectroscopy based on the solvatochromic behavior of Nile Red. Solubility studies were performed to prepare fenofibrate- and itraconazole-loaded SNEDDS and assess their droplet size, whereas dissolution experiments were conducted in simulated intestinal fluids. Caco-2 cell viability studies confirmed the safety of the SNEDDS formulations at 1:100 and 1:1000 dilutions after cell exposure in culture for 4 h. The obtained results showed similar performance between RB and CO supporting the potential of RB as oil vehicle for the effective oral delivery of lipophilic compounds.

Optimizing Extraction Conditions of Free and Bound Phenolic Compounds from Rice By-Products and Their Antioxidant Effects.

Rice by-products are extensively abundant agricultural wastes from the rice industry. This study was designed to optimize experimental conditions for maximum recovery of free and bound phenolic compounds from rice by-products. Optimized conditions were determined using response surface methodology based on total phenolic content (TPC), ABTS radical scavenging activity and ferric reducing power (FRAP). A Box-Behnken design was used to investigate the effects of ethanol concentration, extraction time and temperature, and NaOH concentration, hydrolysis time and temperature for free and bound fractions, respectively. The optimal conditions for the free phenolics were 41⁻56%, 40 °C, 10 min, whereas for bound phenolics were 2.5⁻3.6 M, 80 °C, 120 min. Under these conditions free TPC, ABTS and FRAP values in the bran were approximately 2-times higher than in the husk. However, bound TPC and FRAP values in the husk were 1.9- and 1.2-times higher than those in the bran, respectively, while bran fraction observed the highest ABTS value. Ferulic acid was most evident in the bran, whereas p-coumaric acid was mostly found in the husk. Findings from this study demonstrates that rice by-products could be exploited as valuable sources of bioactive components that could be used as ingredients of functional food and nutraceuticals.