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":"5 ","pages":"Article 100132"},"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\":\"5 \",\"pages\":\"Article 100132\"},\"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.
期刊介绍:
Food Chemistry: Molecular Sciences is one of three companion journals to the highly respected Food Chemistry.
Food Chemistry: Molecular Sciences is an open access journal publishing research advancing the theory and practice of molecular sciences of foods.
The types of articles considered are original research articles, analytical methods, comprehensive reviews and commentaries.
Topics include:
Molecular sciences relating to major and minor components of food (nutrients and bioactives) and their physiological, sensory, flavour, and microbiological aspects; data must be sufficient to demonstrate relevance to foods and as consumed by humans
Changes in molecular composition or structure in foods occurring or induced during growth, distribution and processing (industrial or domestic) or as a result of human metabolism
Quality, safety, authenticity and traceability of foods and packaging materials
Valorisation of food waste arising from processing and exploitation of by-products
Molecular sciences of additives, contaminants including agro-chemicals, together with their metabolism, food fate and benefit: risk to human health
Novel analytical and computational (bioinformatics) methods related to foods as consumed, nutrients and bioactives, sensory, metabolic fate, and origins of foods. Articles must be concerned with new or novel methods or novel uses and must be applied to real-world samples to demonstrate robustness. Those dealing with significant improvements to existing methods or foods and commodities from different regions, and re-use of existing data will be considered, provided authors can establish sufficient originality.