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Enhancing antioxidant extraction efficiency from red dragon fruit peel by green approach using novel optimization technique
(2025-01-01)
Loan, Le Thi Kim
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Thao, Le Thi Nhu
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Vinh, Bui The
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Mansamut, Chaiyut
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Tai, Ngo Van
This study is the first application of a combined sonication and enzyme extraction technique as green technology to recover biological compounds from the peel of red-fleshed dragon fruit, utilizing Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). The peel of dragon fruit had sonication pretreatment for 10–30 min (X<inf>1</inf>), followed by hydrolysis using 0.1 % Pectinex Ultra SP-L enzyme at temperatures ranging from 30 to 60 °C (X<inf>2</inf>) for a duration of 60–120 min (X<inf>3</inf>). The Box-Behnken design was employed to structure the experiment. The levels of polyphenol, betacyanin, and antioxidant activity in the extract were utilized to assess the efficacy of the extraction method. The research demonstrated a substantial enhancement in efficiency by the application of ultrasound pretreatment during the enzymatic hydrolysis of dragon fruit peel. The study identified the ideal parameters for the extraction process using the ANN-GA approach, which include an ultrasonic duration of 27.5 min, an enzyme incubation temperature of 47.1 °C, and an enzyme incubation duration of 135.1 min. Under these conditions, the extract exhibited a total phenolic content of 165.34 mg GAE/g peel weight, betacyanin content of 131.87 mg/100 g peel weight, and an antioxidant activity of 0.92 mg TE/100 g by using DPPH radical scavenging activity assay. The research demonstrated that dual treatment enhances the extraction process of chemicals from by-products, particularly dragon fruit peel. The study established a foundation for future research on the utilization and integration of effective extraction technologies to enhance the quality of extracts for use in the food sector.
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Impact of foam-mat drying conditions of “Gấc” aril on drying rate and bioactive compounds: Optimization by novel statistical approaches
(2024-12-30)
Thuy, Nguyen Minh
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Tien, Vo Quoc
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Giau, Tran Ngoc
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Hao, Hong Van
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Minh, Vo Quang
This study was conducted to optimize the foam-mat drying conditions to maximize quality [β-carotene and total polyphenol content (TPC)] and drying rate of “Gấc” aril powder by using two novel statistical techniques as Response Surface Methodology (RSM) and Artificial Neural Network (ANN) couple with Genetic Algorithm (GA). During production process, level of egg albumin (EA) used for foaming process and drying temperature mainly influenced the drying rate and content of antioxidant compounds in powder. ANN model of 3–10–3 showed more accuracy and faster prediction capacity than RSM model did. ANN-GA model predicted the optimal conditions to be 13.31 % EA, 0.26 % xanthan gum and drying temperature of 73.1 °C, with the drying rate of 1.89 g-water/g-dry matter/min, β-carotene content of 395.88 μg/g, TPC of 1.68 mgGAE/g. These results confirmed the suitability and promising of foam-mat drying for “Gấc” aril powder production, to be producing food ingredient containing highly bioactive compounds.
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Artificial intelligence optimization for producing high quality foam-mat dried tomato powder and its application in nutritional soup
(2024-12-01)
Thuy, Nguyen Minh
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Giau, Tran Ngoc
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Hao, Hong Van
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Minh, Vo Quang
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Tai, Ngo Van
The present work aims to investigate the effect of foam-mat drying on drying rate and lycopene content of tomato powder using a three-level Box-Behnken experimental design of Response Surface Methodology (RSM). Three process parameters included egg albumin (EA) ranging from 3 to 9 % as a foaming agent, carboxymethyl cellulose (CMC) from 0.2 to 0.6 % as a foam stabilizer and drying temperatures (60, 70, and 80<sup>o</sup>C). The responses measured drying rate (DR) and lycopene content, which are the indication of drying process and product quality. Optimization of drying process using RSM and artificial neural network coupled genetic algorithm (ANN-GA) models has been also investigated. Foam mat dried tomato powder produced under optimal conditions was then used to prepare a nutritious soup powder with 4 designed recipes with other nutritious ingredients. The results showed that the ANN-GA model (network structure of 3-10-2) could predict and optimize better than the RSM model. The optimal conditions for foam-mat drying process were EA of 6.67 %, CMC of 0.381 %, and drying temperature of 70.6<sup>o</sup>C. These gave the DR and lycopene to be 3.004 g water/g dry matter/min and 392.8 μg/g, respectively. Validation optimal condition was performed and showed that the experimental values obtained were greatly close to the predicted values. From the 4 designed soup formulas, the macronutrient content in formula F2 met the range for AMDR with the percentage of calories from protein, lipid, and carbohydrate being 21.28 %, 20.28 %, and 58.44 %, respectively. It was proven that ANN-GA is a more reliable and robust predictive modelling tool for foam-mat tomato powder production optimization compared to RSM model. Also, the promising application of tomato powder in nutritious soup production also was shown in this study, which could further research in larger scale.
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Prediction of the germination rate and antioxidant properties of VD20 Rice by utilizing Artificial neural network-coupled response surface methodology and product characterization
(2024-10-01)
Loan, Le Thi Kim
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Tat, Truong Quoc
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Minh, Pham Do Trang
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Thao, Vo Thi Thu
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Hoang, Pham Thi Minh
The current research aims to predict and optimize process conditions to produce germinated VD20 with a high rate of germination and antioxidant properties. Box-Behnken design (BBD) was used to build models with three factors [soaking time (ST: 4–6 h), germination time (GT: 18–22 h), and germination temperature (33–37 °C)] and three replications. The data set from the BBD experiment was used to create an artificial neural network (ANN) model that estimated the change in responses by variable factors. The ANN model was extremely accurate, with an overall correlation coefficient (R) of 0.9997 and showed the best fit with actual and predicted data. The germination conditions were further optimized using multi-objective RSM, which revealed that the optimal conditions were ST of 5.34 h, GT of 20.78 h, and germination temperature of 35.6 °C. The experimental validation revealed a high level of agreement between the results of the BBD models forecasted and the actual experimental values. The ANN-coupled BBD methodology is a promising hybrid method for modeling, forecasting, and optimizing the impact of process conditions on the quality of germinated grain. In addition, the raw and germinated VD20 rice were analyzed for their techno-functional properties, estimated glycemic index (eGI), and FTIR. Lower peak viscosity, values of breakdown and setback, and phytic acid were found after rice was germinated. The result revealed that high antioxidant content and activity, which were confirmed by the FTIR pattern, and low digestion behaviors (eGI = 64.23) were the attributes of the germinated product. Furthermore, the results of pasting, thermal, swelling power, and solubility showed the wide range of further application of this material, which should receive more consideration in future research.

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