Scholarlink

<p>This study employed D-optimal mixture design to systematically optimize natural preservative formulations for enhanced antimicrobial efficacy across diverse spoilage organisms. Following initial screening of eight natural extracts against <i>Bacillus subtilis</i>, <i>Pseudomonas fluorescens</i>, <i>Candida sake</i>, and <i>Aspergillus niger</i>, four extracts (guava leaf, peony bark, Chinese gallnut, and brown alga) were selected based on complementary activity profiles and subjected to mixture optimization using 18 experimental runs. Statistical modeling showed target-specific response patterns: bacterial responses were described by reduced quadratic models, the yeast response by a linear model, and the fungal response by a reduced special cubic model. Multi-response desirability optimization targeting minimum inhibitory concentrations (MICs) across all four organisms identified an optimal formulation (guava leaf 5.00%, peony bark 52.95%, Chinese gallnut 38.55%, brown alga 3.50%) with a desirability score of 0.836. Experimental validation confirmed broad-spectrum antimicrobial activity with MIC values of 0.39–0.78&#xa0;mg/mL for bacteria/yeast and 12.50&#xa0;mg/mL for mold, demonstrating reasonable agreement with model predictions. Overall, this study provides a systematic framework for designing natural extract mixtures with balanced antimicrobial performance across multiple microbial targets.</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

Multi-Target Optimization of Natural Extract Mixtures for Antimicrobial Activity Against Bacteria, Yeast, and Mold Using D-Optimal Mixture Design

  • 期刊论文

  • 发表日期：

    2026年5月21日

查看全文
  • Jiwoon Park,
  • Su-Kyung Ku,
  • Yun-Sang Choi,
  • Jung Min Sung

摘要

This study employed D-optimal mixture design to systematically optimize natural preservative formulations for enhanced antimicrobial efficacy across diverse spoilage organisms. Following initial screening of eight natural extracts against Bacillus subtilis, Pseudomonas fluorescens, Candida sake, and Aspergillus niger, four extracts (guava leaf, peony bark, Chinese gallnut, and brown alga) were selected based on complementary activity profiles and subjected to mixture optimization using 18 experimental runs. Statistical modeling showed target-specific response patterns: bacterial responses were described by reduced quadratic models, the yeast response by a linear model, and the fungal response by a reduced special cubic model. Multi-response desirability optimization targeting minimum inhibitory concentrations (MICs) across all four organisms identified an optimal formulation (guava leaf 5.00%, peony bark 52.95%, Chinese gallnut 38.55%, brown alga 3.50%) with a desirability score of 0.836. Experimental validation confirmed broad-spectrum antimicrobial activity with MIC values of 0.39–0.78 mg/mL for bacteria/yeast and 12.50 mg/mL for mold, demonstrating reasonable agreement with model predictions. Overall, this study provides a systematic framework for designing natural extract mixtures with balanced antimicrobial performance across multiple microbial targets.