Shelf life estimation of glassy confections using moisture sorption isotherms

Abstract

This study aimed to develop a mathematical model of moisture diffusion applied to estimate the shelf life of glassy confection. The model was applied and validated on hard candies produced on an industrial scale at temperatures of 20 and 25 C. The results of the predicted and experimental shelf life at 20 C and 75.5% RH were 41 and 39 days respectively. At 25 C and 75.3% RH, it was 24 and 23 days, respectively. The model proved to be reliable for estimating shelf life, with a low percent relative error. At both temperatures, the model proved to be reliable for predicting moisture content as a function of time with R 2 values close to the unit and mean percentage absolute error less than 10%. The method is practical and low cost and can be easily reproduced in future studies of stability in the confectionery industry. Practical ApplicationsThe problem of the stickiness of glassy confectionery on the packaging impacts consumer complaints, returns, and environmental impacts. The model applied in this study can be used as a basis for the development of practical and low-cost methods for estimating expiration dates and shelf life in future studies of stability in the confectionery industry. | INTRODUCTIONSugar confectionery represents a major industry throughout the world.

Correlation of low field nuclear magnetic resonance relaxation with composition and glass transition of hard candies

“…Here, the high total glucose-fructose content (~35% w/w) of FS predominated the system and retarded the interactions between the sucrose molecules even at the rubbery state during candy production. Glucose and fructose molecules are able to interact intensively with sucrose molecules via interactions such as hydrogen bonding (Spanemberg et al, 2022). Therefore, sucrose nucleation rate was diminished, and crystal lattice formation was restricted in 50:50_FS samples.…”

Hard Candy Production and Quality Parameters: A review

“…Recently researchers have prepared studies regarding novel and predictive approaches for hard candy formulation, production, packaging and storage. Some examples include optimization of hard candy formulations for longer shelf life by D-optimal mixture design ( Spanemberg et al, 2019 ), application of image processing and convolutional neural networks (CNNs) for detection and classification of defective hard candies (color, shape and texture defects) ( Wang et al, 2021 ) and application of mathematical models of moisture diffusion to estimate the shelf life of hard candies ( Spanemberg et al, 2022 ). Such techniques are likely to contribute to the design of best possible hard candy formulations with desirable quality characteristics, longer shelf life, better process economics and consumer acceptance with reduced dependence on extensive traditional research and development.…”