Starch‐Based Biodegradable Film from Fruit and Vegetable Waste and Its Standardization Modules Based on Neural Networks and Response Surface Methodology

Mausumi Sarma, Sourav Chakraborty, Radhakrishnan Kesavan, Kshirod Kumar Dash, Prakash Kumar Nayak
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Abstract

Abstract Fruits and vegetable waste‐based starch has numerous applications for use as a biodegradable film in food packaging materials. This study reviews fruit and vegetable waste‐based non‐commercial starches that can be utilized as an alternatives for commercial starches in biodegradable film growth. Circular economy, sustainable manufacturing goals, recycling waste and by‐products, and new basic concepts drive the hunt for alternative starch sources. Starches from unusual and abandoned fruits and vegetables offer stronger research potential. The characteristics of starch extracted from these sources and their use as a biodegradable film are emerging trends in the field of packaging technology. Further, millet starch, for example, is made from the waste of underused crops or other fruits and vegetables and presents a wealth of new avenues for biodegradable film study. In order to cease throwing away valuable carbohydrates, especially starch, these sources must incorporate into the concept of “circularity” and work toward more sustainable manufacturing practices. Besides, optimizing the biodegradable film composition to improve barrier and shelf life is also crucial. Thus, an additional study may apply response surface‐based hybrid optimization, neural networks, or deep learning‐oriented models to optimize biodegradable film composition and intelligent monitoring of the materials under the packing systems.

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基于神经网络和响应面方法的淀粉基果蔬垃圾生物降解膜及其标准化模块
果蔬废料淀粉作为一种生物可降解薄膜在食品包装材料中有着广泛的应用。本研究综述了以水果和蔬菜废料为基础的非商业淀粉,这些淀粉可以作为商业淀粉在生物可降解薄膜生长中的替代品。循环经济、可持续制造目标、废物和副产品回收利用以及新的基本概念推动着人们寻找替代淀粉来源。从不常见和废弃的水果和蔬菜中提取的淀粉提供了更大的研究潜力。从这些来源提取的淀粉的特性及其作为可生物降解薄膜的使用是包装技术领域的新兴趋势。此外,以小米淀粉为例,它是由未充分利用的作物或其他水果和蔬菜的废料制成的,为生物可降解薄膜的研究提供了丰富的新途径。为了停止浪费有价值的碳水化合物,特别是淀粉,这些来源必须纳入“循环”的概念,并朝着更可持续的制造实践努力。此外,优化生物可降解膜的组成以提高阻隔性和保质期也是至关重要的。因此,一项额外的研究可能会应用基于响应面的混合优化、神经网络或面向深度学习的模型来优化可生物降解薄膜的组成和包装系统下材料的智能监测。
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