Multipin cold plasma electric discharge on hydration properties of kodo millet flour: Modelling and optimization using response surface methodology and artificial neural network – Genetic algorithm

IF 4.1 Q2 FOOD SCIENCE & TECHNOLOGY Food Chemistry Molecular Sciences Pub Date : 2022-12-30 DOI:10.1016/j.fochms.2022.100132
Samuel Jaddu, S. Abdullah, Madhuresh Dwivedi, Rama Chandra Pradhan
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引用次数: 8

Abstract

The effect on functional properties of kodo millet flour was studied using multipin cold plasma electric reactor. The analysis was carried out at various levels of voltage (10–20 kV) and treatment time (10–30 min) for four different parameters such as water absorption capacity (WAC), oil absorption capacity (OAC), solubility index (SI) and swelling capacity (SC). Response surface methodology (RSM) and artificial neural network – genetic algorithm (ANN – GA) were adopted for modelling and optimization of process variables. The optimized values obtained from RSM were 20 kV and 17.9 min. On the contrary, 17.5 kV and 23.3 min were the optimized values obtained from ANN – GA. The RSM optimal values of WAC, OAC, SI and SC were 1.51 g/g, 1.40 g/g, 0.06 g/g and 3.68 g/g whereas optimized ANN – GA values were 1.51 g/g, 1.50 g/g, 0.06 g/g and 4.39 g/g, respectively. Infrared spectra, peak temperature, diffractograms and micrographs of both optimized values were analyzed and showed significant differences. ANN showed a higher value of R2 and lesser values of other statistical parameters compared to RSM. Therefore, ANN – GA was treated as the best model for optimization and modelling of cold plasma treated kodo millet flour. Hence, the ANN – GA optimized values of cold plasma treated flour could be utilized for practical applications in food processing industries.

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多针冷等离子体放电对小米粉水化性能的影响:基于响应面法和人工神经网络-遗传算法的建模与优化
采用多针冷等离子体反应器,研究了其对小米粉功能特性的影响。在不同电压(10-20 kV)和处理时间(10-30 min)下,对吸水能力(WAC)、吸油能力(OAC)、溶解度指数(SI)和溶胀能力(SC)等4个不同参数进行分析。采用响应面法(RSM)和人工神经网络-遗传算法(ANN - GA)对过程变量进行建模和优化。RSM的最优值为20 kV和17.9 min,而ANN - GA的最优值为17.5 kV和23.3 min。WAC、OAC、SI和SC的RSM最优值分别为1.51 g/g、1.40 g/g、0.06 g/g和3.68 g/g, ANN - GA的RSM最优值分别为1.51 g/g、1.50 g/g、0.06 g/g和4.39 g/g。对两个优化值的红外光谱、峰温、衍射图和显微图进行了分析,发现两者存在显著差异。与RSM相比,ANN的R2值更高,其他统计参数值更低。因此,ANN - GA可作为冷等离子体处理小米粉优化建模的最佳模型。因此,人工神经网络-遗传算法优化后的冷等离子体处理面粉可用于食品加工行业的实际应用。
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来源期刊
Food Chemistry Molecular Sciences
Food Chemistry Molecular Sciences Agricultural and Biological Sciences-Food Science
CiteScore
6.00
自引率
0.00%
发文量
83
审稿时长
82 days
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