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Modelling of ultrasonic assisted osmotic dehydration of cape gooseberry using adaptive neuro-fuzzy inference system (ANFIS).
Kumar Dash, Kshirod; Sundarsingh, Anjelina; BhagyaRaj, G V S; Kumar Pandey, Vinay; Kovács, Béla; Mukarram, Shaikh Ayaz.
Afiliación
  • Kumar Dash K; Department of Food Processing Technology, Ghani Khan Choudhury Institute of Engineering and Technology (GKCIET), Malda, West Bengal 732141, India. Electronic address: kshirod@tezu.ernet.in.
  • Sundarsingh A; Department of Food Processing Technology, Ghani Khan Choudhury Institute of Engineering and Technology (GKCIET), Malda, West Bengal 732141, India.
  • BhagyaRaj GVS; Department of Food Processing Technology, Ghani Khan Choudhury Institute of Engineering and Technology (GKCIET), Malda, West Bengal 732141, India.
  • Kumar Pandey V; Department of Bioengineering, Integral University, Lucknow, Uttar Pradesh, India.
  • Kovács B; Faculty of Agriculture, Food Science and Environmental Management Institute of Food Science, University of Debrecen, Debrecen 4032, Hungary.
  • Mukarram SA; Faculty of Agriculture, Food Science and Environmental Management Institute of Food Science, University of Debrecen, Debrecen 4032, Hungary. Electronic address: Ayaz.shaikh@agr.unideb.hu.
Ultrason Sonochem ; 96: 106425, 2023 Jun.
Article en En | MEDLINE | ID: mdl-37141660
ABSTRACT
In the present investigation, the cape gooseberry (Physalis peruviana L.) was preserved by the application of osmotic dehydration (sugar solution) with ultrasonication. The experiments were planned based on central composite circumscribed design with four independent variables and four dependent variables, which yielded 30 experimental runs. The four independent variables used were ultrasonication power (XP) with a range of 100-500 W, immersion time (XT) in the range of 30-55 min, solvent concentration (XC) of 45-65 % and solid to solvent ratio (XS) with range 16-114 w/w. The effect of these process parameters on the responses weight loss (YW), solid gain (YS), change in color (YC) and water activity (YA) of ultrasound assisted osmotic dehydration (UOD) cape gooseberry was studied by using response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). The second order polynomial equation successfully modeled the data with an average coefficient of determination (R2) was found to be 0.964 for RSM. While for the ANFIS modeling, Gaussian type membership function (MF) and linear type MF was used for the input and output, respectively. The ANFIS model formed after 500 epochs and trained by hybrid model was found to have average R2 value of 0.998. On comparing the R2 value the ANFIS model found to be superior over RSM in predicting the responses of the UOD cape gooseberry process. So, the ANFIS was integrated with a genetic algorithm (GA) for optimization with the aim of maximum YW and minimum YS, YC and YA. Depending on the higher fitness value of 3.4, the integrated ANFIS-GA picked the ideal combination of independent variables and was found to be XP of 282.434 W, XT of 50.280 min, XC of 55.836 % and XS of 9.250 w/w. The predicted and experimental values of response at optimum condition predicted by integrated ANN-GA were in close agreement, which was evident by the relative deviation less than 7%.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Ultrason Sonochem Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Ultrason Sonochem Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2023 Tipo del documento: Article