Artificial neural network model- and response surface methodology-based optimization of Atractylodis Macrocephalae Rhizoma polysaccharide extraction, kinetic modelling and structural characterization

Application of ionic liquid ultrasound-assisted extraction (IL-UAE) of lycopene from guava (Psidium guajava L.) by response surface methodology and artificial neural network-genetic algorithm

Lycopene-rich guava (Psidium guajava L.) exhibits significant economic potential as a functional food ingredient, making it highly valuable for the pharmaceutical and agro-food industries. However, there is a need to enhance the extraction methods of lycopene to fully exploit its beneficial uses. In this study, we evaluated various ionic liquids to identify the most effective one for extracting lycopene from guava. Among thirteen ionic liquids with varying carbon chains or anions, 1-butyl-3-methylimidazolium chloride demonstrated the highest productivity. Subsequently, a single-factor experiment was employed to test the impact of several parameters on the efficiency of lycopene extraction using this selected ionic liquid. These parameters included extraction time, ultrasonic power, liquid-solid ratio, concentration of the ionic liquid, as well as material particle size. Moreover, models of artificial neural networks using genetic algorithms (ANN-GA) and response surface methodology (RSM) were employed to comprehensively assess the first four key parameters. The optimized conditions for ionic liquid ultrasound-assisted extraction (IL-UAE) were determined as follows: 33 min of extraction time, 225 W of ultrasonic power, 22 mL/g of liquid-solid ratio, 3.0 mol/L of IL concentration, and extraction cycles of three. Under these conditions, lycopene production reached an impressive yield of 9.35 ± 0.36 mg/g while offering advantages such as high efficiency, time savings, preservation benefits, and most importantly environmental friendliness.

Comparison of extraction processes, characterization and intestinal protection activity of Bletilla striata polysaccharides

We optimized the extraction process of Bletilla striata polysaccharides using orthogonal design, Box-Behnken design (BBD), and genetic algorithm-back propagation (GA-BP), then compared and evaluated them to confirm that the combination of BBD and GA-BP neural networks was capable of increasing polysaccharide yields and antioxidant activity. The optimal extraction parameters were as follows: liquid-to-solid ratio of 15 mL/g, extraction power of 450 W, and extraction time of 34 min. Under these conditions, the polysaccharide yield and antioxidant activity were 8.29 ± 0.50 % and 26.20 ± 0.28 (mM FE/mg). Subsequently, the polysaccharide was purified to obtain purified Bletilla striata polysaccharides 1 (pBSP1) with a Mw of 255.172 kDa. Scanning electron microscope (SEM), ultraviolet-visible detector (UV), fourier transform infrared spectrometer (FTIR), high performance liquid chromatography (HPLC), X-ray diffraction (XRD), nuclear magnetic resonance (NMR) and periodate oxidation were used to analyze the structure of pBSP1. The results showed pBSP1 had a smooth surface and a rough interior, with a composition of α-D conformation glucose (18.23 %) and β-D conformation mannose (53.77 %), and an amorphous crystal structure. According to the results of thermogravimetric and rheological tests, pBSP1 exhibits good thermal stability and viscoelastic behavior. Furthermore, pBSP1 protected lipopolysaccharide (LPS)-induced GES - 1 and Caco2 cells, the results showed pBSP1(400 μg/mL) lowered TEER synthesis in Caco2 cells as well as apoptosis and reactive oxygen species (ROS) production in both cells, indicating that pBSP1 may have an intestine protective effect.

Insightful properties-performance study of Ti-Cu-O heterojunction sonochemically embedded in mesoporous silica matrix for efficient tetracycline adsorption and photodegradation: RSM and ANN-based modeling and optimization

This study aims to provide a comprehensive evaluation of the photocatalytic properties and performance of the Cu-Ti-O heterojunction sonochemically embedded in the mesoporous silica matrix. Various characterization analyses and adsorption/photodegradation experiments were performed to assess the potential of the sample for tetracycline (TC) removal. The characterization results indicated that sonication contributes to better dispersion of Ti-Cu-O species, resulting in more uniform particle sizes, stronger semiconductors-silica interaction, and less agglomeration. Furthermore, sonication significantly affected the optical nanocomposite features, leading to an improvement in charge carrier separation and a decrease in the band gap of Ti-Cu-Si (S) by approximately 2.6 eV. Based on the textural results, the ultrasound microjets increased the surface area and pore volume, which facilitate mass transfer and provide suitable adsorption sites for TC molecules. Accordingly, Cu-Ti-Si (S) demonstrated higher adsorption capacity (0.051 g TC/g adsorbent) and eliminated TC significantly faster (0.0054 L.mg-1.min-1) than a non-sonicated sample during 120 min of irradiation, resulting in 2.84 times improvement in the constant rate. In addition, experimental results were accurately modeled using a central composite design in combination with response surface methodology (RSM) and artificial neural networks (ANN) to predict and optimize TC photodegradation. Both RSM and ANN models revealed excellent predictability for TC degradation efficiency, with R2 = 99.47 and 99.71%, respectively. At optimal operational conditions (CTC = 20 ppm, photocatalyst dosage = 1.15 g.L-1, pH = 9, and irradiation time = 100 min), more than 95% and 87% of TC were degraded within the UV (375 W) and simulated solar light (400 W) irradiation periods, respectively. It was observed that the Cu-Ti-Si (S) nanocomposite maintained remarkable stability after four cycles with only a negligible 3% loss of activity, owing to the superior interaction between the bimetallic heterojunction and the silica matrix.

Study on the physicochemical properties and antioxidant activities of Flammulina velutipes polysaccharide under controllable ultrasonic degradation based on artificial neural network

The polysaccharide fraction (FVP2) with molecular weight of 1525.09 kDa and intrinsic viscosity of 3.43 dL/g was isolated and purified from Flammulina velutipes (F. velutipes), and the ultrasonic degradation model of FVP2 was established to predict the molecular weight and intrinsic viscosity at the same time based on artificial neural network. FVP2U1 (1149.11 kDa, 1.78 dL/g), FVP2U2 (618.91 kDa, 1.19 dL/g) and FVP2U3 (597.35 kDa, 0.48 dL/g) with different molecular weights or viscosity were produced by this model to explore the effect of ultrasound on the physicochemical properties and antioxidant activity of FVP2. The results showed that ultrasonic treatment did not change the types of characteristic functional groups, monosaccharide composition and glycosidic bond of FVP2, but changed the chemical composition ratio and the degree of polymerization. Under ultrasonic treatment, the intrinsic viscosity of FVP2 still decreased significantly when the molecular weight did not decrease. Compared to other components subjected to ultrasonic degradation, FVP2U1 demonstrated higher molecular weight and viscoelasticity, while exhibiting lower antioxidant activity. In the case of no significant difference in molecular weight and monosaccharide composition, FVP2U3 with lower intrinsic viscosity has stronger hydration ability, higher crystallization index, lower viscoelasticity and stronger antioxidant capacity than FVP2U2.

Quality and Process Optimization of Infrared Combined Hot Air Drying of Yam Slices Based on BP Neural Network and Gray Wolf Algorithm

In this paper, the effects on drying time (Y1), the color difference (Y2), unit energy consumption (Y3), polysaccharide content (Y4), rehydration ratio (Y5), and allantoin content (Y6) of yam slices were investigated under different drying temperatures (50-70 °C), slice thicknesses (2-10 mm), and radiation distances (80-160 mm). The optimal drying conditions were determined by applying the BP neural network wolf algorithm (GWO) model based on response surface methodology (RMS). All the above indices were significantly affected by drying conditions (p < 0.05). The drying rate and effective water diffusion coefficient of yam slices accelerated with increasing temperature and decreasing slice thickness and radiation distance. The selection of lower temperature and slice thickness helped reduce the energy consumption and color difference. The polysaccharide content increased and then decreased with drying temperature, slice thickness, and radiation distance, and it was highest at 60 °C, 6 mm, and 120 mm. At 60 °C, lower slice thickness and radiation distance favored the retention of allantoin content. Under the given constraints (minimization of drying time, unit energy consumption, color difference, and maximization of rehydration ratio, polysaccharide content, and allantoin content), BP-GWO was found to have higher coefficients of determination (R2 = 0.9919 to 0.9983) and lower RMSEs (reduced by 61.34% to 80.03%) than RMS. Multi-objective optimization of BP-GWO was carried out to obtain the optimal drying conditions, as follows: temperature 63.57 °C, slice thickness 4.27 mm, radiation distance 91.39 mm, corresponding to the optimal indices, as follows: Y1 = 133.71 min, Y2 = 7.26, Y3 = 8.54 kJ·h·kg-1, Y4 = 20.73 mg/g, Y5 = 2.84 kg/kg, and Y6 = 3.69 μg/g. In the experimental verification of the prediction results, the relative error between the actual and predicted values was less than 5%, proving the model's reliability for other materials in the drying technology process research to provide a reference.

Ultrasound-assisted hydrogen peroxide-ascorbic acid method to degrade sweet corncob polysaccharides can help treat type 2 diabetes via multiple pathways in vivo

In this study, we aimed to investigate the impact of various ultrasound durations on the structure and bioactivity of sweet corncob polysaccharides treated with ultrasound-assisted degradation using hydrogen peroxide and ascorbic acid (H2O2-Vc). We subjected sweet corncob polysaccharides to ultrasound treatment for 0, 30, 60, and 90 min alongside the H2O2-Vc method. We then analyzed their chemical composition and structure. Additionally, we administered these polysaccharides to mice with type 2 diabetes (T2DM) through gavage at a dosage of 200 mg/kg/day. The results indicated a significant reduction in the molecular weight of the degraded sweet corncob polysaccharides, while their composition remained relatively stable. However, the basic structure of the polysaccharides was retained. In vivo experiments demonstrated that ultrasound-assisted degradation of these polysaccharides had a positive impact on T2DM, particularly the 60-minute ultrasound treatment (UH-DSCBP-60 min), which effectively controlled blood glucose levels by regulating glycolipid metabolism in the livers of mice with T2DM. This approach also reduced inflammation and oxidative stress levels and inhibited disaccharide activity in the small intestine. We demonstrated that ultrasound can positively affect the sweet corncob polysaccharides hypoglycemic activity. The findings of our study provide a theoretical foundation for the valuable utilization of sweet corncob polysaccharides.

Development of an ultrasound-assisted pre-treatment strategy for the extraction of D-Limonene toward the production of bioethanol from citrus peel waste (CPW)

Citrus is one of the world's most abundant fruits containing vitamins, pigments, and fragrances, making it vital for several industries. However, these fruits contain about 45-50% residues (peels), which often end up as waste and can be harmful to the environment if not properly treated. Bioethanol production from citrus peel waste offers a potential solution to this problem. Hence, this study explores the potential of using ultrasound-assisted pre-treatment method as a novel strategy to extract D-Limonene (essential oil in the residue), and further demonstrates bioethanol production. This was done by investigating ultrasonication's optimal effect on pre-treatment of the citrus residue, followed by bioethanol production. The results show that, optimum values for D-Limonene extraction were obtained at a temperature of 14.6 °C and an ultrasound intensity of 25.81 W/cm2 with a validation yield of 134 ± 4.24 mg/100 g dry CPW. With optimal ultrasonic parameters, the study went further to demonstrate the effect of the essential oil on bioethanol production which is hindered by the oils present. Key findings show better bioethanol yield once the essential oil was extracted (treated) from the citrus waste as opposed to it not extracted (untreated), with a 66 and a 29% increase when comparing simultaneous saccharification and fermentation (SSF) and sequential hydrolysis and fermentation (SHF) respectively. Based on this result, ultrasound-assisted extraction as a pretreatment method was found suitable for bioethanol production from citrus residue and could be utilized as a biorefinery pre-treatment approach to scale bioethanol production.