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Modeling the green extraction of bioactive compounds from Pilangkasa fruit (Ardisia elliptica Thunb) through empirical and machine learning approaches
(2026-01-01)
Vinh, B. T.
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Loan, L. T.K.
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Mansamut, C.
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Tai, N. V.
Pilangkasa Fruit (Ardisia elliptica Thunb) contains the rich bioactive compounds, which still limited the study to utilization. This study aimed to investigate the effect of green extraction technique – ultrasoud-assisted extraction on the antioxidants recovery efficient and establish the kinetic extraction under different conditions. Five level of sonication amplitute (0, 25, 50, 75, 100%) were used for extraction at a time range from 0 to 90 minutes. The extraction yield (%), total phenolic compound (mg gallic acid equivalent per g, mgGAE/100 g), total anthocyanin content (mg/100 g) were determined. The experimental data was fitted with five empirical models to find the best fit model. The power of sonication greatly influenced the efficiency for recovering the phytochemical compounds in Pilangkasa fruit (Ardisia elliptica Thunb). The fast rate of extraction was found at the initial stage (after 30 min of extraction), then reached the equilibrium stage or slightly declined. Among the five empirical models, first-order model showed the best fit between the actual and predicted data. While, ANN model presented the fast and accurate prediction the extraction process. Based on the extraction rate constant from the first-order model, the level of around 50-75% of amplitute was considered as the appropriate condition for extraction polyphenol from Pilangkasa fruit with high yield of extraction, total polyphenol content, total anthocyanin content. These conditions could be further optimized and upscaled for used in food industry.
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MODELING AND OPTIMIZATION OF SACCHARIFICATION AND FERMENTATION OF BROKEN RICE
(2026-01-01)
Thuy, Nguyen Minh
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Hung, Tran Huy
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Viet Ha, Lam Thi
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Van Hao, Hong
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Giau, Tran Ngoc
Rice wine is a traditional alcoholic beverage derived from fermented glutinous rice or broken rice. The method is separated into two steps: first, the rice is cooked and liquefied/saccharified by molds and enzymes, followed by fermentation. The study examined how Aspergillus oryzae (0.1 - 0.2%) and α-amylase (0.01 - 0.04%) affect starch liquefaction and saccharification, as well as how Saccharomyces bayanus concentration (0.02 - 0.05%) and total soluble solids content (22 - 26%) impact rice wine fermentation. To improve process prediction and optimization, an artificial neural network integrated with a genetic algorithm (ANN-GA) was applied to model the nonlinear relationships between process variables and fermentation performance. The optimization approach utilizing a machine learning-based model demonstrated better prediction ability. Compared with conventional regression approaches, the ANN-GA model provided improved predictive accuracy and enabled the identification of optimal processing conditions for both saccharification and fermentation stages. The optimum content of Aspergillus oryzae and α-amylase was 0.181% and 0.036%, respectively, resulting in high starch saccharification efficiency with a total soluble solids content of 27.2oBrix. The volume of sugar solution achieved was 34.01 mL (from 50 g rice, yield 68.02%). In addition, using the optimal content of Saccharomyces bayanus of 0.043% and fermenting in an environment with high soluble solids content of 24.88oBrix produced wine with high ethanol and ester content, 12.19% by volume and 0.93 g/L, respectively. The methanol content of the fermented product under these optimal conditions was lower (49.8 mg/L). These findings demonstrate that the integration of machine-learning-based optimization can effectively enhance fermentation efficiency while maintaining product safety. Overall, the optimized saccharification and fermentation parameters provide a viable approach for producing rice wine with higher quality and safety assurances for this traditional product.
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Modeling of dehydration, polyphenol thermal degradation, and rehydration of instant germinated VD20 rice: Mathematical and artificial intelligence model
(2025-01-01)
Loan, L. T.K.
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Tat, T. Q.
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Minh, P. D.T.
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Thao, V. T.T.
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Hoang, P. T.M.
VD20 rice, a local rice variety in Vietnam, is currently undergoing restoration and provides limited information about product development. In order to produce the instant germinated VD20 rice, the study on kinetics of dehydration, polyphenol thermal degradation, and rehydration of the instant product was carried out. Different temperatures were applied in this study, including 50°C, 55°C, 60°C, and 65°C. Various models were developed to describe these changes. The Page model provided the best fit for the sample's dehydration properties, with the moisture diffusivity (D<inf>eff</inf>) ranging from 7 x 10-12 to 1.19 x 10-11 m2/s and an activation energy of 31.70 kJ/mol. A zero-order model described the change in polyphenol during the drying process. The half-life values ranged from 3.737 h to 5.723 h. Also, the ANN model was used. This is an intelligent model made of an artificial neural network. It worked better and faster than earlier models like the Page model for dehydration behavior and the zero-order model for degradation property. The rehydration ratio of instant germinated rice also fitted well with the exponential model. These developed insights could facilitate further optimization and production on a larger scale, thereby enabling farmers to produce more products from this rice.
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Drying kinetics and mathematical modeling of dried macaroni supplemented with Gac aril
(2023-01-01)
Thuy, N. M.
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Giau, T. N.
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Tai, N. V.
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Minh, V. Q.
Macaroni is dried after being boiled to maintain their quality and extend their shelf life. Evaluation of moisture and nutrient changes aims to provide an overview of the phenomena occurring during the drying process. Thin layer drying kinetics of macaroni supplemented with Gac aril in hot air drying was investigated for temperatures range of 60 to 90ºC with six theoretical models were evaluated. It was observed that the Logarithmic equation gave the best prediction to the drying kinetics evidenced by high coefficient of determination (R2 ranging from 0.98-0.99). The lowest RMSE (0.0103 to 0.0426) and χ2 values (0.0002 to 0.0024) were obtained from the Logarithmic model within the defined temperature range. Diffusivity coefficients of moisture transfer were found ranging from 1.64×10−12 to 2.31 ×10−12 m2s−1 with activation energy was determined at 11.81 kJ.mol−1. Drying at 80ºC for 9 hours was selected for this product, the degradation kinetics of β-carotene and lycopene in macaroni followed a first-order kinetic model with half-life values were determined.
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IMPACT OF DRYING TEMPERATURES ON DRYING BEHAVIOURS, ENERGY CONSUMPTION AND QUALITY OF PURPLE SWEET POTATO FLOUR
(2022-01-01)
Thuy, Nguyen Minh
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Hiep, Le Huy
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Tai, Ngo Van
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Huong, Huynh Thi Thu
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Minh, Vo Quang
to purple sweet potatoes could enhance the economic value of this material. Furthermore, energy consumption, as well as the change in the quality of the product, are the important characteristics that determine the product’s quality and effect on the environment. Material and methods. This research examined the impact of various drying temperatures on kinetic behaviour, effective moisture diffusivity coefficient (D<inf>eff</inf>), activation energy (E<inf>a</inf>), specific energy consumption (SEC), color, shrinkage and physicochemical characteristics of purple sweet potatoes. The final quality of the sample was also evaluated. Results. Seven models were applied and fitted to actual data of the drying process. The two-term model showed the best fit with high R2, and low RMSE and Chi-square. The calculated D<inf>eff</inf> and Ea values were 1.58– 2.67 m2/s and 17.95 kJ/mol, respectively. The energy consumption of drying purple sweet potatoes ranged from 107.92 to 119.01 kWh/kg. The quality of the product was maintained when a sample was dried at 60°C. Conclusion. Temperature strongly affected the quality of dried purple sweet potatoes and energy consumption. The first report about the value of energy used during the drying process of sweet potatoes also provides more information about the effect of the drying process on carbon emissions to the environment. Therefore, research aimed at improving product quality and minimizing environmental impacts should be implemented in the future and concerned with ensuring sustainable agricultural production.

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