Samuel Jaddu, S. Abdullah, Madhuresh Dwivedi, Rama Chandra Pradhan
{"title":"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","authors":"Samuel Jaddu, S. Abdullah, Madhuresh Dwivedi, Rama Chandra Pradhan","doi":"10.1016/j.fochms.2022.100132","DOIUrl":null,"url":null,"abstract":"<div><p>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 R<sup>2</sup> 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.</p></div>","PeriodicalId":34477,"journal":{"name":"Food Chemistry Molecular Sciences","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a6/14/main.PMC9465321.pdf","citationCount":"8","resultStr":"{\"title\":\"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\",\"authors\":\"Samuel Jaddu, S. Abdullah, Madhuresh Dwivedi, Rama Chandra Pradhan\",\"doi\":\"10.1016/j.fochms.2022.100132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 R<sup>2</sup> 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.</p></div>\",\"PeriodicalId\":34477,\"journal\":{\"name\":\"Food Chemistry Molecular Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2022-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a6/14/main.PMC9465321.pdf\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food Chemistry Molecular Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666566222000600\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Chemistry Molecular Sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666566222000600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
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
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.