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Artificial neural network (ANNs) and mathematical modelling of hydration of green chickpea

Information Processing in Agriculture 2021年第1期8卷 75-86页

作者： Yogesh Kumar Lochan Singh Vijay Singh Sharanagat ayon tarafdar Department of Food Engineering National Institute of Food Technology Entrepreneurship and ManagementHaryanaIndia Department of Agriculture and Environmental Sciences National Institute of Food Technology Entrepreneurship and ManagementHaryanaIndia

The present study was aimed to model the hydration characteristics of green chickpea(GC)using mathematical modelling and examine predictive ability of artificial neural network(ANN)*** of GC was performed at different temperatures 25,35,45,55 and 65℃.Different mathematical models were tested for the hydration at different *** ANN modelling,the hydration time and hydration temperature were used as input variables and moisture ratio,moisture content and hydration ratio were taken as output *** model best described the hydration behavior at 25℃;while hydration at high-temperature was better described by Page model and Ibarz et *** optimum temperature obtained for hydration was 35℃.Effective mass diffusion coefficient(D_(e))increased from 1.5510^(-11)-1.7910^(-9) m^(2)/s with the increase in the hydration *** low activation energy(39.66 kJ/moL)shows the low-temperature sensitiveness of *** temperature hydration(25℃)required higher time(>200 min)to achieve the equilibrium moisture content(EMC),however high temperature hydration(35–65℃)reduced the EMC time(150 min).ANN was used to predict the hydration behavior and K fold cross validation was performed to check the over fitting of ANN *** show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and *** was found suitable for moisture ratio(MR)and hydration ratio(HR)prediction,as opposed to PURELIN for moisture content(MC)*** model prediction was obtained when the number of neurons in the hidden layer for MC,MR and HR was 12,8 and 15,*** and ANN modelling results are useful to improve/predict the MC,MR and HR during hydration process of GC at different temperature and other similar process.

关键词： Green chickpea Water absorption Hydration temperature ANN modeling

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Physico-functional evaluation,process optimization and economic analysis for preparation of muffin premix using apple pomace as novel supplement

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Systems Microbiology and Biomanufacturing 2021年第3期1卷 302-310页

作者： Taru Negi Devina Vaidya ayon tarafdar Shubham Samkaria Nilakshi Chauhan Swati Sharma Ranjna Sirohi Department of Food Science and Technology G.B.Pant University of Agriculture and TechnologyPantnagarUttarakhand 263145India Department of Food Science and Technology Dr.Y.S.Parmar University of Horticulture and ForestryNauniHimachal Pradesh 173230India Division of Livestock Production and Management ICAR-Indian Veterinary Research InstituteIzatnagarBareillyUttar Pradesh 243122India Centre for Energy and Environmental Sustainability LucknowUttar Pradesh 226029India

Apple pomace is a rich source of dietary fiber that can be easily incorporated in bakery products to enhance the nutritional quality of the *** the present study,apple pomace powder was utilized for formulating fiber-enriched muffin *** pomace powder(APP)was supplemented in refined wheat flour at different concentration and 33%was found highly acceptable by the sensory panelists on a 9 point hedonic *** were analyzed for proximate composition,functional properties(water holding,swelling,foaming and emulsion),phenolic content and calorific *** study of polyethylene and aluminium laminate pouch(ALP)packed fiber-enriched muffin pre-mix was also conducted for six months at ambient *** evaluation showed that the cost of APP-based pre-mix was comparable to conventional wheat-based *** of this study support the possibility of producing high-fiber muffin pre-mix with desirable quality and quantity in a sustainable manner.

关键词： Apple pomace Muffin pre-mix Sensory evaluation Shelflife Bakery

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