Effects of Different Drying Methods on the Mycoflora Associated with Cocoyam (Colocasia esculenta (L) Schott) Chips (Achicha) in Storage

Ilondu, E. M.

doi:10.9734/jabb/2017/31035

Cited by 1 publication

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“…Though there have been reported works on the preservation of cocoyam through the drying process (Famurewa et al., 2014; Ilondu, 2017; Ogunlakin et al., 2012; Omotosho et al., 2015), the present study aims to utilize RSM, ANN, and ANFIS in modeling the drying of cocoyam slices and the optimization using genetic algorithm (GA) with moisture content reduction as the focus. This is the major property that increases the shelf life of cocoyam.…”

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confidence: 99%

ANFIS, ANN, and RSM modeling of moisture content reduction of cocoyam slices

Onu

Igbokwe

Nwabanne

et al. 2021

Food Processing Preservation

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The capability of response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference systems (ANFIS) in modeling and predicting moisture content reduction in the drying of cocoyam (Colocasia taro) slices was the purpose of this study. Design of experiment was utilized. Modified Fick's second law of diffusion was used to determine effective moisture diffusivity (D eff ). Results indicated that drying time, air velocity, and temperature significantly affected the drying process. D e ff values obtained ranged from 2.97 × 10 −10 to 7.30 × 10 −10 m/s. Page model with R 2 = 0.994, SSE = 0.0053, RMSE = 0.0611, best described the kinetics of the drying process. ANN, RSM, and ANFIS all showed significant modeling and predicting ability with R 2 of 0.9583, 0.9519, and 0.9971, respectively. Genetic algorithm (GA) optimization showed minimum moisture content of 14.362%, 11.919%, and 11.293% for RSM-GA, ANN-GA, and ANFIS-GA, respectively. Additional statistical analysis lent credence to ANFIS as the best in modeling and predicting the moisture content reduction of cocoyam slices.

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