Instant decompression-induced swell drying of banana: Machine learning and swarm intelligence embedded modeling and process optimization

被引:2
作者
Trivedi, Dipika [1 ]
Gautam, Swapnil Prashant [2 ]
Abdul, Sayyad [1 ,4 ]
Hazarika, Manuj Kumar [2 ,5 ]
Chakraborty, Sourav [2 ,3 ,6 ]
机构
[1] Harcourt Butler Tech Univ, Dept Food Technol, Kanpur, India
[2] Tezpur Univ, Dept Food Engn & Technol, Tezpur, Assam, India
[3] Ghani Khan Choudhury Inst Engn & Technol, Dept Food Proc Technol, Malda, India
[4] Harcourt Butler Tech Univ, Dept Food Technol, Kanpur 8500721956, India
[5] Tezpur Univ, Dept Food Engn & Technol, Tezpur 784028, Assam, India
[6] Ghani Khan Choudhury Inst Engn & Technol, Dept Food Proc Technol, Malda 732141, India
关键词
hot air drying; instant controlled pressure drop; particle swarm optimization; vacuum drying; KINETICS; QUALITY; METHODOLOGY;
D O I
10.1111/jfpe.14431
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
A novel approach of instant decompression-induced swell drying also known as instant controlled pressure drop-hot air dried (ICPD-HAD) process was applied for the process performance and quality enhancement of dried banana slices. Using a hybrid approach of particle swarm embedded methodology, the process parameters treatment pressure (TP), treatment time (TT), and duration of decompressed State (DDS) were optimized for various responses including drying time (DT), browning index (BI), rehydration ratio (RR), and total antioxidant activity (TAA). The optimized process conditions were found to be 0.1 MPa of TP, 25 s of TT, and 12 s of DDS for a minimum DT of 225 min and, maximum BI, RR, and TAA, of 6.20%, 3.40%, and 98.78% respectively. The results also demonstrated that the dried banana samples produced by the ICPD-HAD method exhibited superior quality in terms of minimal DT and BI and maximum TAA and RR compared to those produced by the vacuum and hot air drying techniques. Microstructural investigation demonstrating the superiority of ICPD-induced samples provided additional evidence for the minimum DT requirement. Furthermore, the use of an artificial neural network was employed to investigate the drying kinetics of the novel instant decompression-induced swell drying process. With R-2 greater than 0.99 and a minimum mean square error (MSE) of 0.000131, the 2-4-1 artificial neural network (ANN) architecture performed admirably as a means of optimal simulation and robust control of the drying process.Practical applicationsInstant decompression-induced swell drying also known as ICPD-HAD process is a novel approach for the drying of banana sample with enhanced process performance and quality attires. It is common for banana drying to cause loss of total antioxidant activity and other important functional properties like color and texture. This issue can be solved and process performance can be enhanced with less energy use by employing an innovative swell drying technique triggered by instant decompression. Therefore, the dried banana samples made using this cutting-edge technique will be advantageous from a health point of view. This method can be used by the food manufacturing sector to produce dried bananas of a higher quality and greater consumer acceptance.
引用
收藏
页数:15
相关论文
共 34 条
[1]   Optimization of Drying Conditions for Quality Dried Tomato Slices Using Response Surface Methodology [J].
Abano, E. E. ;
Ma, H. ;
Qu, W. .
JOURNAL OF FOOD PROCESSING AND PRESERVATION, 2014, 38 (03) :996-1009
[2]  
Abano E. E., 2011, AFRICAN J FOOD SCI, V5, P148
[3]   Artificial Neural Network Modeling of Drying Kinetics and Color Changes of Ginkgo Biloba Seeds during Microwave Drying Process [J].
Bai, Jun-Wen ;
Xiao, Hong-Wei ;
Ma, Hai-Le ;
Zhou, Cun-Shan .
JOURNAL OF FOOD QUALITY, 2018,
[4]   Development and Testing of an ANN Model for Estimation of Runoff from a Snow Covered Catchment [J].
Bhadra A. ;
Bandyopadhyay A. ;
Chakraborty S. ;
Roy S. ;
Kumar T. .
Journal of The Institution of Engineers (India): Series A, 2017, 98 (1-2) :29-39
[5]   Instant Controlled Pressure Drop (DIC) Treatment for Improving Process Performance and Milled Rice Quality [J].
Chakraborty S. ;
Gautam S.P. ;
Das P.P. ;
Hazarika M.K. .
Journal of The Institution of Engineers (India): Series A, 2019, 100 (04) :683-695
[6]   Adaptive neuro-fuzzy interface system and neural network modeling for the drying kinetics of instant controlled pressure drop treated parboiled rice [J].
Chakraborty, Sourav ;
Gautam, Swapnil Prashant ;
Sarma, Mausumi ;
Hazarika, Manuj Kumar .
FOOD SCIENCE AND TECHNOLOGY INTERNATIONAL, 2021, 27 (08) :746-763
[7]   Neural network and computational fluid dynamics modeling for the gelatinization kinetics of instant controlled pressure drop treated parboiled rice [J].
Chakraborty, Sourav ;
Gautam, Swapnil Prashant ;
Bordoloi, Tridisha ;
Hazarika, Manuj Kumar .
JOURNAL OF FOOD PROCESS ENGINEERING, 2020, 43 (11)
[8]   Low-cost drying methods for developing countries [J].
Chua, KJ ;
Chou, SK .
TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2003, 14 (12) :519-528
[9]   The particle swarm - Explosion, stability, and convergence in a multidimensional complex space [J].
Clerc, M ;
Kennedy, J .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (01) :58-73
[10]   Modeling of Microwave Vacuum Drying Kinetics of Bael (Aegle marmelos L.) Pulp by Using Artificial Neural Network [J].
Dash K.K. ;
Chakraborty S. ;
Singh Y.R. .
Journal of The Institution of Engineers (India): Series A, 2020, 101 (02) :343-351