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

Qiu, Junjie; Shi, Menglin; Li, Siqi; Ying, Qianyi; Zhang, Xinxin; Mao, Xinxin; Sheng, Shi; Wu, Shaohua

doi:10.1016/j.ultsonch.2023.106408

Cited by 15 publications

(4 citation statements)

References 35 publications

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“…R 2 of 0.79 further implies that 21% of total variation is not explained by the model. Models with R 2 > 0.7 are generally acceptable but should be in conjunction with lack of fit, F -values, sum of squared estimate of errors (SSE), and residual plots as shown in literature in such scenarios [ 25 – 29 ]. The model F -value and the p -value were 174.71 and 0.05554 respectively.…”

Section: Resultsmentioning

confidence: 99%

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

Teke,

De Vos,

Smith

et al. 2023

Bioprocess Biosyst Eng

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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.

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Section: Resultsmentioning

confidence: 99%

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

Teke,

De Vos,

Smith

et al. 2023

Bioprocess Biosyst Eng

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show abstract

“…Polysaccharides were extracted under these specified conditions: A 1000-mL glass beaker was filled with Schizochytrium limacinum powder (10 g), a particular concentration of papain (2, 3, 4, 5, and 6%), and a phosphate buffer (0.2 mol/L) in liquid-to-material ratios (15,20,25,30, and 35 mL/g). Then, SLMPs were extracted using various ultrasonic times (10,20,30,40, and 50 min), ultrasonic powers (40, 50, 60, 70, and 80 W), and ultrasonic temperatures (30,40,50,60, and 70 • C) at a 40 KHz ultrasonic frequency.…”

Section: Enzyme-assisted Ultrasonic Extraction Of Polysaccharides (Sl...mentioning

confidence: 99%

“…When high-frequency ultrasonic vibration generates a cavitation effect on the cell wall, a few weak tension areas will be torn into tiny cavities, and the fluid will be released into the solvent. The rapid expansion and closure of these microscopic cavities results in a violent collision of liquid particles, creating a mild-temperature environment that promotes ingredient solubility and separation [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Enzyme-Assisted Ultrasonic Extraction and Antioxidant Activities of Polysaccharides from Schizochytrium limacinum Meal

Zhang,

Chen,

et al. 2024

Foods

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Enzyme-assisted ultrasonic extraction (EAUE) was utilized and optimized for extracting polysaccharides from Schizochytrium limacinum meal (SLMPs) via the response surface methodology. The optimal EAUE conditions were determined as follows: enzyme concentration at 5.18%, ultrasonic temperature at 53 °C, ultrasonic duration of 40 min, ultrasonic power at 60 W, and a liquid-to-material ratio of 34 mL/g, achieving a polysaccharide extraction yield of 11.86 ± 0.61%. The purified polysaccharide component, SLMP1-1, isolated using DEAE Sepharose Fast Flow and Sephadex G-100 columns, exhibited potent antioxidant activity. SLMP1-1, with a molecular weight of 25.5 kDa, comprises glucose, mannose, arabinose, and galactose in a molar ratio of 16.39:14.75:1:693.03. 1H NMR analysis revealed the α configuration of SLMP1-1. Antioxidant assessments, including DPPH, ABTS, and ferric ion reduction assays, were detected with inhibitory values at 21.82–82.98%, 38.21–98.46%, and 3.30–20.30% at 0.2–1.0 mg/mL. This confirmed the effective antioxidant capacity of SLMP1-1, which is notably enhanced post oral and gastric digestion. The findings suggest that polysaccharides extracted from Schizochytrium limacinum meal hold significant promise as natural antioxidants.

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“…BP neural networks have been widely recognized as effective for solving complex, highly non-linear, and poorly defined science and engineering problems [16]. BP neural networks have been used for the prediction of hot air drying characteristics of wheat [17], microwave vacuum drying characteristics of carrots [18], prediction of polysaccharides in rhizomes of Atractylodis macrocephaly [19], prediction of flavor changes in the drying process of ginger [20], and prediction of infrared drying of broccoli moisture ratio [21]. Although BP neural networks have many advantages, they also have some limitations, such as easy overfitting, sensitivity to initial weight selection, and the need for a large amount of training data [22].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Zheng,

Xiao

et al. 2024

Foods

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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.

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

Cited by 15 publications

References 35 publications

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

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

Enzyme-Assisted Ultrasonic Extraction and Antioxidant Activities of Polysaccharides from Schizochytrium limacinum Meal

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

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