Scholarlink

<p>In this study, the effectiveness of aqueous ozone treatment in reducing deltamethrin residues from cucumbers was thoroughly analyzed. To optimize the treatment process, two advanced techniques, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN), were employed. Under the conditions determined by RSM (70% ozone concentration and 29&#xa0;min of treatment time), deltamethrin degradation reached 97.5%. The cucumbers treated under these conditions exhibited a color change value of 15.5 and a firmness value of 9.96 N, indicating the treatment's effect on physical attributes. ANN identified a slightly different set of optimal conditions <i>i.e.,</i> 98.23% ozone concentration and 30&#xa0;min of treatment. Under these conditions, the deltamethrin degradation rate increased to 98.6%. Moreover, cucumbers treated using ANN-optimized conditions showed a lower color change value of 11.04, indicating less visual alteration, and a higher firmness value of 10.87 N, suggesting better textural retention. The comparison between the two techniques revealed that while both methods were highly effective, ANN offered a slight advantage over RSM by achieving higher pesticide degradation, lower color change, and better firmness retention. This suggests that ANN may be a more precise tool for optimizing complex food processing treatments such as aqueous ozone application for pesticide removal.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimization of aqueous ozone treatment for deltamethrin degradation in cucumbers (Cucumis sativus L.): a comparative study using response surface methodology and artificial neural networks

  • 期刊论文

  • 发表日期：

    2025年4月26日

查看全文
  • K. P. Navya,
  • S. Abdullah,
  • P. Vithu,
  • Berin Pathrose,
  • E. Shahanas,
  • K. P. Sudheer

摘要

In this study, the effectiveness of aqueous ozone treatment in reducing deltamethrin residues from cucumbers was thoroughly analyzed. To optimize the treatment process, two advanced techniques, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN), were employed. Under the conditions determined by RSM (70% ozone concentration and 29 min of treatment time), deltamethrin degradation reached 97.5%. The cucumbers treated under these conditions exhibited a color change value of 15.5 and a firmness value of 9.96 N, indicating the treatment's effect on physical attributes. ANN identified a slightly different set of optimal conditions i.e., 98.23% ozone concentration and 30 min of treatment. Under these conditions, the deltamethrin degradation rate increased to 98.6%. Moreover, cucumbers treated using ANN-optimized conditions showed a lower color change value of 11.04, indicating less visual alteration, and a higher firmness value of 10.87 N, suggesting better textural retention. The comparison between the two techniques revealed that while both methods were highly effective, ANN offered a slight advantage over RSM by achieving higher pesticide degradation, lower color change, and better firmness retention. This suggests that ANN may be a more precise tool for optimizing complex food processing treatments such as aqueous ozone application for pesticide removal.