Modelling of ultrasonic assisted osmotic dehydration of cape gooseberry using adaptive neuro-fuzzy inference system (ANFIS)

被引:5
|
作者
Dash, Kshirod Kumar [1 ]
Sundarsingh, Anjelina [1 ]
BhagyaRaj, G. V. S. [1 ]
Pandey, Vinay Kumar [2 ]
Kovacs, Bela [3 ]
Mukarram, Shaikh Ayaz [3 ]
机构
[1] Ghani Khan Choudhury Inst Engn & Technol GKCIET, Dept Food Proc Technol, Malda 732141, W Bengal, India
[2] Integral Univ, Dept Bioengn, Lucknow, Uttar Pradesh, India
[3] Univ Debrecen, Food Sci & Environm Management Inst Food Sci, Fac Agr, H-4032 Debrecen, Hungary
关键词
Cape gooseberry; Ultrasonication; Osmotic dehydration; ANFIS; WATER ACTIVITY; QUALITY; L; PRETREATMENTS; TEMPERATURE; OPTIMIZATION; ANTIOXIDANT; PARAMETERS; PRESSURE; OSMOSIS;
D O I
10.1016/j.ultsonch.2023.106425
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
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 1:6-1:14 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 in-tegrated 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%.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
    Al-Hmouz, Ahmed
    Shen, Jun
    Al-Hmouz, Rami
    Yan, Jun
    IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, 2012, 5 (03): : 226 - 237
  • [22] An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM
    Caydas, Ulas
    Hascalik, Ahmet
    Ekici, Sami
    EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) : 6135 - 6139
  • [23] An Implementation of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for Odor Source Localization
    Wang, Lingxiao
    Pang, Shuo
    2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2020, : 4551 - 4558
  • [24] Modelling Unconfined Groundwater Recharge Using Adaptive Neuro-Fuzzy Inference System
    Mohamed Nabil I. Elsayed, Khaled
    Rustum, Rabee
    Adeloye, Adebayo J.
    PROCESSES, 2020, 8 (10) : 1 - 17
  • [25] MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System
    Khalifa, Amine B.
    Frigui, Hichem
    2015 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2015), 2015,
  • [26] Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering
    Cabalar, Ali Firat
    Cevik, Abdulkadir
    Gokceoglu, Candan
    COMPUTERS AND GEOTECHNICS, 2012, 40 : 14 - 33
  • [27] PREDICTION OF BIOMASS PELLET DENSITY USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM(ANFIS) METHOD
    Liu, Juan
    Yan, Zhuoyu
    Xu, Mingze
    Liu, Yudi
    Bai, Xuewei
    Xiu, Yonghai
    Wei, Desheng
    INMATEH-AGRICULTURAL ENGINEERING, 2023, 70 (02): : 181 - 190
  • [28] Modeling of dielectric behavior of polymers nanocomposites using adaptive neuro-fuzzy inference system (ANFIS)
    Mohamed, R. A.
    EUROPEAN PHYSICAL JOURNAL PLUS, 2022, 137 (03):
  • [29] Modeling of dielectric behavior of polymers nanocomposites using adaptive neuro-fuzzy inference system (ANFIS)
    R. A. Mohamed
    The European Physical Journal Plus, 137
  • [30] Dynamic characterization of flexible vibrating structures using adaptive neuro-fuzzy inference system (ANFIS)
    Ismail, A. Y.
    Ismail, R.
    Darus, I. Z. Mat
    2006 4TH STUDENT CONFERENCE ON RESEARCH AND DEVELOPMENT, 2006, : 155 - +