Advances in Designing Essential Oil Nanoformulations: An Integrative Approach to Mathematical Modeling with Potential Application in Food Preservation

Abstract

Preservation of foods, along with health and safety issues, is a growing concern in the current generation. Essential oils have emerged as a natural means for the long-term protection of foods along with the maintenance of their qualities. Direct applications of essential oils have posed various constraints to the food system and also have limitations in application; hence, encapsulation of essential oils into biopolymers has been recognized as a cutting-edge technology to overcome these challenges. This article presents and evaluates the strategies for the development of encapsulated essential oils on the basis of fascination with the modeling and shuffling of various biopolymers, surfactants, and co-surfactants, along with the utilization of different fabrication processes. Artificial intelligence and machine learning have enabled the preparation of different nanoemulsion formulations, synthesis strategies, stability, and release kinetics of essential oils or their bioactive components from nanoemulsions with improved efficacy in food systems. Different mathematical models for the stability and delivery kinetics of essential oils in food systems have also been discussed. The article also explains the advanced application of modeling-based encapsulation strategies on the preservation of a variety of food commodities with their intended implication in food and agricultural industries.

Investigation of Guar Gum and Xanthan Gum Influence on Essential Thyme Oil Emulsion Properties and Encapsulation Release Using Modeling Tools

“…The kinetic rate constant-based calculation assumes a homogeneous release mechanism across the matrix, which might oversimplify the complex interactions between thyme oil molecules and the hydrocolloid network. On the other hand, the application of Fick’s second law considers the spatial concentration gradients within the matrix, offering a more nuanced view of diffusion that considers the heterogeneity of the system [ 41 ].…”

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization

“…The data basis was divided into 70% training and 30% testing data. The developed ANN models were trained 100,000 times per model with a random number of neurons in the hidden layer (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). Various activation functions and randomly assigned values for weighting coefficients and biases were employed in the modeling process.…”

“…To gauge the efficacy and performance of the Support Vector Machine (SVM) and Artificial Neural Network (ANN) models in predicting output variables from input data, various statistical parameters were computed. These parameters encompassed the reduced chi-square (X 2 ) (4), root mean square error (RMSE) (5), mean systematic error (MBE) (6), mean percentage error (MPE) (7), total squared error (SSE) (8), average absolute relative deviation (AARD) (9), and coefficient of determination (r 2 ) (10). The RMSE values serve as indicators of the model's efficiency by assessing the agreement between calculated values and experimentally measured values.…”

“…Integrating machine learning models plays a crucial role in modeling and optimizing formulations in food applications [ 7 ]. These advanced computational tools, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), contribute to a more precise understanding of ingredient interactions and their impact on product attributes [ 8 ].…”