Document Type
Article
Publication Date
10-1-2025
Keywords
Strawberries, Edible coatings, Shelf life, Predictive modeling, Post, harvest technology
Abstract
Strawberries deteriorate rapidly under warm, humid conditions, and to make things worse, in many tropical settings refrigerated logistics are limited. Edible biopolymer coatings offer a low-cost method to slow quality loss, but multicomponent formulations behave nonlinearly. In this study, ten types of coatings were developed and evaluated for their effects on the physicochemical (weight loss, firmness, total soluble solids (TSS), pH, color change (Delta E)) and biochemical properties (total phenolic content (TPC), antioxidant property (DPPH) of strawberries over a 6-day storage period at 25 degrees C (298.15K). The optimal formulation, consisting of 0.56 % (w/v) alginate, 0.33 % (w/v) guar gum, and 2.11 % (w/v) pectin, shows 7 % weight loss, 586.34 N firmness, a pH of 4.08, and a Delta E of 4.32 according to our machine learning models. Additionally, two formulation families emerged from the optimization: pectin-rich coatings extended shelf-life with lower weight loss and restrained color change, while alginate-rich coatings maintained better firmness. All machine learning models (Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGB)) achieved high predictive accuracy (R-2 > 0.97).
Citation
Saklain Niam, Iftekhar Ahmad, Md Abu Rayhan, Sajid Mahmood, Parvej Hasan Jon, Md Monir Ahmed, Machine learning-based optimization of alginate, guar gum, and pectin-based edible coatings for extended strawberry shelf life, LWT, Volume 233, 2025, 118548, ISSN 0023-6438, https://doi.org/10.1016/j.lwt.2025.118548.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Comments
Web of Science
Elsevier