Intelligent Modeling and Analysis of Moisture Sorption Isotherms in Milk and Pearl Millet–Based Weaning Food “Fortified Nutrimix”

Sharma, A. K.; Sawhney, I. K.; Lal, Mohanjee

doi:10.1080/07373937.2013.858265

Cited by 8 publications

(9 citation statements)

References 25 publications

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“…Hence, the classical model had to be revised for each temperature. Similar superior performance of ANFIS model over connectionist and GAB models in predicting sorption was reported by Sharma et al (2014) for milk-pearl millet-based fortified powder. Al-Mahasneh et al (2012) reported that the neuro-fuzzy technique provided much better fit as compared to classical nonlinear regression methods.…”

Section: Performance Evaluation Of Different Modelssupporting

confidence: 74%

“…It can be blended with milk to produce weaning and geriatric foods that are rich in vitamins, fibre and iron. Several such milk-malted millet foods have been developed to cater various consumer groups (Shah et al 1987;Salooja and Balachandran 1988;Sharma et al 2014). One of the most common methods of producing malted foods is by spray drying.…”

Section: Introductionmentioning

confidence: 99%

“…Many traditional empirical and semi-empirical models are reported in literature for describing the sorption behaviour of food powders. However, in recent times, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been successfully used in the prediction of moisture sorption behaviour of processed foods (Siripatrawan and Jantawat 2008;Sharma et al 2014). The ANN is a soft-computing approach that imitates the human neural system that can perform spontaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Sharma and Sawhney (2015) studied the moisture sorption characteristics in dried acid casein and reported that the connectionist model outperformed conventional empirical models. Sharma et al (2014) reported that neural network models were superior as compared to the conventional sorption models. As described above, the moisture sorption isotherms and its thermodynamics of milk-foxtail millet powder has not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Soft computing modelling of moisture sorption isotherms of milk-foxtail millet powder and determination of thermodynamic properties

et al. 2016

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Moisture sorption isotherms of spray-dried milk-foxtail millet powder were determined at 10, 25 and 40 °C. Sorption data was fitted using classical and soft-computing approaches. The isotherms were of type II, and equilibrium moisture content (EMC) was temperature dependent. The BET monolayer moisture content decreased from 3.30 to 2.67 % as temperature increased from 10 to 40 °C. Amongst the classical models, Ferro-Fontan gave the best fit of EMC-aw data. However, the Sugeno-type adaptive neuro-fuzzy inference system (ANFIS) with generalized bell-shaped membership function performed better than artificial neural network and classical models with RMSE as low as 0.0099. The isosteric heat of sorption decreased from 150.32 kJ mol(-1) at 1 % moisture content to 44.11 kJ mol(-1) at 15 % moisture. The enthalpy-entropy compensation theory was validated, and the isokinetic and harmonic mean temperatures were determined as 333.1 and 297.5 K, respectively.

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Section: Performance Evaluation Of Different Modelssupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Soft computing modelling of moisture sorption isotherms of milk-foxtail millet powder and determination of thermodynamic properties

et al. 2016

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“…Such a NN is trained to fit a dataset D by minimizing an error function (or performance function) as is function is minimized using the standard optimization method [37,38].…”

Section: Neural Networkmentioning

confidence: 99%

Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

Chopra

Sharma

Kumar

et al. 2018

Advances in Civil Engineering

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A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via "R" software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. e dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R 2 ) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

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Intelligent Modelling of Moisture Sorption Isotherms in Milk Protein-Rich Extruded Snacks Prepared from Composite Flour

Sharma

Panjagari

Singh

2018

Communications in Computer and Information Science

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Intelligent Modeling and Analysis of Moisture Sorption Isotherms in Milk and Pearl Millet–Based Weaning Food “Fortified Nutrimix”

Cited by 8 publications

References 25 publications

Soft computing modelling of moisture sorption isotherms of milk-foxtail millet powder and determination of thermodynamic properties

Soft computing modelling of moisture sorption isotherms of milk-foxtail millet powder and determination of thermodynamic properties

Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

Intelligent Modelling of Moisture Sorption Isotherms in Milk Protein-Rich Extruded Snacks Prepared from Composite Flour

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