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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
;
Tien, Vo Quoc
;
Giau, Tran Ngoc
;
Hao, Hong Van
;
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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Effect of foaming conditions and drying temperatures on total polyphenol content and drying rate of foam-mat dried banana powder: Modeling and optimization study
(2024-12-01)
Van Tai, Ngo
;
Van Hao, Hong
;
Han, Tran Thi Ngoc
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Giau, Tran Ngoc
;
Thuy, Nguyen Minh
This study aims to optimize the parameters of the foam-mat drying to produce banana powder. Input parameters of the foam drying such as foaming agent (egg albumin) concentration used from 5 to 15 %, the foam stabilizer (maltodextrin) used from 1 to 3 % and the drying temperature varied between 60 and 80 °C. Factors with 3 levels are arranged according to the Box-Behnken design, which further modelled by RSM (response surface methodology) and ANN (artificial neural network), and optimized. The drying rate and total polyphenol content (TPC) of banana powder under the studied conditions were determined as the target output. The moisture, color, total polyphenol, antioxidant activity and some physical parameters of final fine powder were analyzed. Increasing temperature has increased the drying rate. In addition, increasing the concentrations of egg albumin and maltodextrin maintained the highest TPC and maximum drying rate. ANN model showed the higher forecasting capacity than that of RSM. Moreover, simultaneous optimization of two responses (TPC and drying rate) was selected to maximize the desired value at the concentration of albumin, maltodextrin and drying temperature of 11.86 %, 1.92 %, 74.94 °C, respectively, corresponding to the highest TPC value and drying rate of 1.31 mgGAE/g DW and 2.48 g water/g dry matter/min. At this condition, the drying time was recorded as 103 min. Validation of the optimal ratios showed that the experimental values of TPC and drying rate were in good agreement with the model predicted data. The moisture content and water activity of product were found to be 5.87 ± 0.07 % and 0.37 ± 0.01. The product had bright colors with L*, a* and b* values were measured as 86.4 ± 0.5, 1.75 ± 0.08 and 15.2 ± 0.3, respectively. The high DPPH radical scavenging activity was detected (53.5 %) with the water solubility index and water absorption index of banana powder was determined at value of 56.98 % and 5.17 g/g, respectively. Foam mat dried banana powder was well preserved in paper packaging with aluminum foil.
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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
;
Giau, Tran Ngoc
;
Hao, Hong Van
;
Minh, Vo Quang
;
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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