Analysis of crystallization phenomenon in Indian honey using molecular dynamics simulations and artificial neural network. (1st December 2019)

• Record Type: Journal Article
• Authors: Naik, Rishi Ravindra; Gandhi, Neha S.; Thakur, Mamta; Nanda, Vikas
• Is Part Of: Food chemistry. Volume 300(2019)
• Publication Date: 2019-12-01
• Subjects: Crystallization -- Molecular dynamics -- Honey -- Artificial neural network -- FG ratio; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2019.125182
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Molecular dynamics simulations were conducted on six honey mixture systems. FG ratio of 1.18 had highest Van der walls and electrostatic interactions. Honey with lowest gyration radius, SASA and RMSD formed most stable crystal. Water, sucrose and maltose had significant effect on the crystallization. Artificial neural network predicted stability of honey crystallization (R 2 = 0.99). Abstract: Molecular dynamics simulation was performed on sugar profile and moisture content-based mixture systems of six Indian honey samples. Comparative studies were performed to understand the interactive effects of fructose, glucose, sucrose, maltose and water on crystallization. All simulations led to formation of stable crystal but with different interaction energies. Post-simulation analysis showed that Fructose:Glucose of 1.18 formed the most stable crystal with highest van der Waals and electrostatic interactions. The stability of crystal was further validated with least gyration radius (209 ± 1.81 nm 2 ), accessible surface area (4.09 ± 0.04 nm) and root mean square displacement (3.51 ± 0.00261 nm). Results indicated that not only Fructose:Glucose ratio but also sucrose, maltose and water had a significant effect on the overall crystallization process. The simulation data was used to train the artificial neural network which predicted the stability of honey crystallization depending on Fructose:Glucose and Glucose:Water ratios.