Characterization of Rice Flour Quality using Artificial Neural Network – Genetic Algorithm for Production of Zhero: an Ethnic Himalayan Snack Product.
Zhero is an indigenous popular snack product of Bhutia and Tamang tribe of Sikkim, India. The present study focusses on standardizing its process operations such as soaking and grinding for production of uniform quality rice flour to be used as raw material in Zhero preparation for commercial proces...
| Publicado en: | Current Research in Nutrition & Food Science Vol. 14; no. 1; pp. 407 - 422 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
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Current Research in Nutrition & Food Science
Apr2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194017455&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194017455 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2347467X KIVQ jtl: Current Research in Nutrition & Food Science issn: 2347467X maglogo: N pubinfo: dt: Apr2026 vid: 14 iid: 1 pid: 25572 pub: Current Research in Nutrition & Food Science artinfo: ui: 194017455 194017455 194017455 10.12944/CRNFSJ.14.1.28 194017455 ppf: 407 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Characterization of Rice Flour Quality using Artificial Neural Network – Genetic Algorithm for Production of Zhero: an Ethnic Himalayan Snack Product. aug: au: APEKSHA JENA, SUJATA KUMAR, SITESH affil: Department of Processing and Food Engineering, Central Agricultural University, Ranipool, India. sug: subj: Snacks Food Handling Rice Analysis Food Quality Evaluation Neural Networks (Computer) Algorithms Evaluation Human India Temperature Time Particle Size Cooking Physiochemical Phenomena Data Analysis Software Regression Validity ab: Zhero is an indigenous popular snack product of Bhutia and Tamang tribe of Sikkim, India. The present study focusses on standardizing its process operations such as soaking and grinding for production of uniform quality rice flour to be used as raw material in Zhero preparation for commercial processing. The effects of soaking time, St (4 – 8 h) and temperature, ST (15-25 °C) and grinding time, GT (1 – 2 minutes) on the characteristics of rice flour have been studied. The process parameters were optimized using a multi-objective genetic algorithm (GA) along with a three-layer feed forward artificial neural network (ANN). The ANN model developed could satisfactorily predict all responses with R value of 0.999 for all training, testing, validation and global sets of data and MSE value 0.01883 for validated dataset. The final optimum conditions (St: ST: GT) were 4.2 h, 18 ℃, and 1 min respectively. The predicted rice flour quality at optimized process conditions were: 23.85 % w b moisture content, 0.543 g/cm³ bulk density, 0.57 g/cm³ tapped density, 28.09° angle of repose, 0.31 mm particle size, Carr’s index 5.055 and HR ratio 1.05. The experimental and ANN-GA model predicted responses had a relative percent error of < 10%, suggesting suitability of the developed model. This study represents a new attempt to conduct appropriate scientific research to determine the consistency of the production process and the quality of the final product, not only to confirm its origin and maintain its culture, but also to improve and standardize its technology for future commercial success. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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