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Application of foam-mat drying to produce field crab powder: Foaming process optimization, drying kinetics, and final product characterization
(2025-08-01)
Thuy, Nguyen Minh
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Nhut Minh, Ngo Ngoc
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Kha, Nguyen Hoang
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Bich Thuy, Bui Thi
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Giau, Tran Ngoc
This study used foam-mat drying to make powder from field crab meat for the first time. In which, the effect of foaming conditions [egg albumin (EA, 7.96–16.44 %) and xanthan gum (XG, 0.04–0.44 %)] and drying temperature (65–80 °C) on powder quality were examined. With appropriate EA and XG levels of 13.16 % and 0.30 %, foam density and foam expansion were 0.395 g/mL and 279.78 %, respectively. The total energy required and specific energy consumption decreased. In contrast, thermal efficiency and energy efficiency rose with drying temperature, reaching 2.494–4.452 %, and 1.419–1.879 %, respectively. Temperature affects effective moisture diffusion coefficient according to the Arrhenius equation with an activation energy of 39.33 kJ/mol. Fitting experimental data to seven thin-layer drying models and an ANN model. The Aghbashlo model scored best, with the highest correlation coefficient. Nevertheless, the ANN model demonstrated slightly superior accuracy compared to the Aghbashlo model, indicating the potential for industrial system control.
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Developing a novel artificial model to predict the foaming properties and β-carotene content of lucuma (Pouteria lucuma) during foam-mat drying and process optimization
(2024-12-01)
Thuy, Nguyen Minh
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Duong, Le Thi Thuy
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Giau, Tran Ngoc
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Hao, Hong Van
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Minh, Vo Quang
Drying fruit puree by the foam drying method has become popular due to its simplicity, low cost, short drying time, and low thermal degradation. The objective of the study was to investigate the effect of foaming conditions on foam properties (foam expansion, foam density) and content of β-carotene in lucuma powder using Box-Behnken design (BBD) with 3 factors and 3 levels, including water:lucuma ratio (1:1–3:1), egg albumin concentration (EA, 5–15 %), and xanthan gum (XG, 0.1–0.3 %). Response surface methodology (RSM) and artificial neural network (ANN) were used for model establishment. The results showed that as the EA increased, the foam volume increased significantly, while the foam density decreased. The ANN-coupled BBD model structure of 3–10-3 demonstrated a high level of accuracy in predicting the impact of foaming formulation on responses, with a coefficient of determination exceeding 0.99. The optimal conditions by stimulation multiple-objective RSM for lucuma foam-mat drying were achieved with a water:lucuma ratio, EA, and XG of 2.53:1, 10.8 %, and 0.22 %, respectively. Based on these ideal conditions, the foam density, foam expansion, and β-carotene content of the dried powder were found to be 0.23 ± 0.04 g/mL, 228 ± 2 %, and 237.1 ± 0.1 μg/g, respectively. The obtained experimental values were very close to the model-predicted results, with very low differences identified when the validation was performed. These findings provide information for controlling the drying process using an artificial model and further applying lucuma powder in various fields in the food industry.
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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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