Modelling approaches for predicting moisture transfer during baking of chhana podo (milk cake) incorporated with tikhur
- PDF / 3,212,714 Bytes
- 17 Pages / 595.276 x 790.866 pts Page_size
- 42 Downloads / 166 Views
ORIGINAL PAPER
Modelling approaches for predicting moisture transfer during baking of chhana podo (milk cake) incorporated with tikhur (Curcuma angustifolia) starch F. Magdaline Eljeeva Emerald1 · Heartwin A. Pushpadass1 · M. Manjunatha1 · K. Manimala2 · D. Dejey3 · Karthik Salish1,4 · B. Surendra Nath1 Received: 25 November 2018 / Accepted: 29 June 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Chhana podo, a popular dairy product of India, is prepared by baking the dough of chhana (heat-acid coagulated milk solids), semolina and sugar. Heat induced moisture loss during baking influences the chain of physico-chemical changes, which in turn determine the quality of the final product. Thus, in this work, modelling approaches for predicting moisture transfer during baking of chhana podo prepared by replacing semolina with tikhur (Curcuma angustifolia) starch were developed. Moisture loss, moisture ratio, specific volume, oven spring, volumetric expansion and other physico-chemical quality changes of the dough were determined during baking, and were used in the development of moisture transfer models. The effective moisture diffusivity of chhana podo increased from 8.10 × 10–9 to 37.05 × 10–9 m2/s as baking temperature increased from 110 to 150 °C. The artificial neural networks (ANN) model based on multilayer feed-forward (MLFF) algorithm and radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) models based on Sugeno-type fuzzy inference system were developed to predict moisture ratio, and their predictive performance was compared with classical multiple linear regression and empirical models. Amongst the four prediction approaches, the MLFF neural network with baking temperature and time as input factors, and with 12 neurons in the hidden layer, produced the best performance in predicting moisture ratio with R2 as high as 0.9946 (99.46% accuracy) and RMSE of 0.0188 for the entire range of baking temperature studied. Sensitivity analysis showed that baking temperature was the most influential parameter (strength ratio was 0.89) deciding moisture transfer. MLFF neural network was observed to be a simple and efficient technique for predicting the complex moisture transfer phenomenon during baking, which could be used by the industry to optimize the baking conditions. Keywords ANFIS · Baking · Chhana podo (milk cake) · Moisture transfer · Sensitivity analysis · SIMULINK
Introduction
* F. Magdaline Eljeeva Emerald [email protected] 1
Southern Regional Station, ICAR-National Dairy Research Institute, Bengaluru 560030, India
2
Dr. Sivanthi Aditanar College of Engineering, Tiruchendur 628215, India
3
Department of Computer Science and Engineering, University College of Engineering, Nagercoil 629 004, India
4
Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907‑2022, USA
In India, nearly 7% of milk is converted into heat-acid coagulated products such as chhana and paneer [1]. Chhana, obtained by citric acid
Data Loading...
