Šovljanski Olja, Lončar Biljana, Pezo Lato, Saveljić Anja, Tomić Ana, Brunet Sara, Filipović Vladimir, Filipović Jelena, Čanadanović-Brunet Jasna, Ćetković Gordana, Travičić Vanja
Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia.
Engineering Department, Institute of General and Physical Chemistry, Studentski trg 12/V, 11000 Belgrade, Serbia.
Foods. 2023 Dec 29;13(1):134. doi: 10.3390/foods13010134.
This study explores the unexploited potential of Artificial Neural Network (ANN) optimization techniques in enhancing different drying methods and their influence on the characteristics of various sweet potato varieties. Focusing on the intricate interplay between drying methods and the unique characteristics of white, pink, orange, and purple sweet potatoes, the presented experimental study indicates the impact of ANN-driven optimization on food-related characteristics such as color, phenols content, biological activities (antioxidant, antimicrobial, anti-hyperglycemic, and anti-inflammatory), chemical, and mineral contents. The results unveil significant variations in drying method efficacy across different sweet potato types, underscoring the need for tailored optimization strategies. Specifically, purple sweet potatoes emerge as robust carriers of phenolic compounds, showcasing superior antioxidant activities. Furthermore, this study reveals the optimized parameters of dried sweet potato, such as total phenols content of 1677.76 mg/100 g and anti-inflammatory activity of 8.93%, anti-hyperglycemic activity of 24.42%. The upgraded antioxidant capability is presented through DPPH, ABTS, RP, and SoA assays with values of 1500.56, 10,083.37, 3130.81, and 22,753.97 μg TE/100 g, respectively. Additionally, the moisture content in the lyophilized sample reached a minimum of 2.97%, holding favorable chemical and mineral contents. The utilization of ANN optimization proves instrumental in interpreting complex interactions and unlocking efficiencies in sweet potato drying processes, thereby contributing valuable insights to food science and technology.
本研究探索了人工神经网络（ANN）优化技术在强化不同干燥方法及其对各种甘薯品种特性影响方面尚未开发的潜力。该实验研究聚焦于干燥方法与白、粉、橙、紫甘薯独特特性之间的复杂相互作用，表明了ANN驱动的优化对诸如颜色、酚类含量、生物活性（抗氧化、抗菌、抗高血糖和抗炎）、化学及矿物质含量等食品相关特性的影响。结果揭示了不同甘薯类型在干燥方法效果上存在显著差异，强调了定制优化策略的必要性。具体而言，紫甘薯是酚类化合物的强大载体，展现出卓越的抗氧化活性。此外，本研究还揭示了甘薯干的优化参数，如总酚含量为1677.76毫克/100克、抗炎活性为8.93%、抗高血糖活性为24.42%。通过DPPH、ABTS、RP和SoA测定法呈现出提升后的抗氧化能力，其值分别为1500.56、10083.37、3130.81和22753.97微克TE/100克。此外，冻干样品中的水分含量最低达到2.97%，且保持了良好的化学和矿物质含量。ANN优化的应用被证明有助于解读复杂的相互作用并提高甘薯干燥过程的效率，从而为食品科学与技术提供了有价值的见解。
