Scholarlink

<p>Gibberellic acid (GA) can be produced sustainably using <i>Zea mays</i> L. (maize) leaves as a substrate for microbial fermentation. Maize leaves, when utilized through eco-friendly practices, contribute to waste reduction and reinforce circular agricultural systems. In this study, maize leaves served as a source for isolating fungal and bacterial strains, which were subsequently screened for their ability to produce GA. Bacterial cultures exhibiting high GA synthesis potential were selected for further optimization. In GA1 media, one-factor-at-a-time (OFAT) experiments evaluated key nutrients and GA production. To maximize GA yield, a Box-Behnken design (BBD) was used in 15 experimental runs to optimize dextrose, peptone, and MgSO₄ concentrations. Peak GA production occurred after 3&#xa0;days of incubation at optimal conditions of 2% dextrose, 3% peptone, and 0.8% MgSO₄. To identify active producers, microbial strains were identified. A 24&#xa0;h bacterial growth curve analysis tracked GA concentration to confirm production trends. The zinc acetate method and UV detection allowed accurate broth GA spectrophotometric quantification. GA bioactivity was tested on paddy crops in germination assays, showing promising applications for crop growth and yield under sustainable agricultural practices. The ANN model moderately predicted GA concentration with a rescaled MAE of 3.97&#xa0;μg/mL, covering 10–15% of the dataset range (19.4–53). The ANN accurately predicted GA concentration, but the error margins were moderate. Linear regression outperformed ANN and decision tree models, achieving nearly perfect fit (<i>R</i><sup>2</sup> = 0.9999). GA yield was primarily affected by absorbance, followed by dextrose and MgSO₄ concentrations, and minimally by peptone. This study shows an effective GA bioprocess with environmental and agricultural benefits. Further testing showed that UMM2 produced enzyme lyases like cellulases and amylases. BBD optimization with renewable resources makes GA production scalable and sustainable, making it useful for agronomic applications and environmentally friendly GA synthesis. It also opens avenues for its potential translational application in biomedical and plant-derived therapeutics.</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

Optimized production of gibberellic acid via Box-Behnken design and artificial neural networks: a sustainable approach for enhancing paddy crop growth

  • 期刊论文

  • 发表日期：

    2025年10月6日

查看全文
  • Omaish S. Alqahtani,
  • Uday M. Muddapur,
  • Srinidhi Kulkarni,
  • Aparna Shenvi,
  • Bassam S. M. Al Kazman,
  • Mohammed A. Alshamrani,
  • Ibrahim Ahmed Shaikh,
  • Aejaz Abdullatif Khan,
  • Basheerahmed Abdulaziz Mannasaheb

摘要

Gibberellic acid (GA) can be produced sustainably using Zea mays L. (maize) leaves as a substrate for microbial fermentation. Maize leaves, when utilized through eco-friendly practices, contribute to waste reduction and reinforce circular agricultural systems. In this study, maize leaves served as a source for isolating fungal and bacterial strains, which were subsequently screened for their ability to produce GA. Bacterial cultures exhibiting high GA synthesis potential were selected for further optimization. In GA1 media, one-factor-at-a-time (OFAT) experiments evaluated key nutrients and GA production. To maximize GA yield, a Box-Behnken design (BBD) was used in 15 experimental runs to optimize dextrose, peptone, and MgSO₄ concentrations. Peak GA production occurred after 3 days of incubation at optimal conditions of 2% dextrose, 3% peptone, and 0.8% MgSO₄. To identify active producers, microbial strains were identified. A 24 h bacterial growth curve analysis tracked GA concentration to confirm production trends. The zinc acetate method and UV detection allowed accurate broth GA spectrophotometric quantification. GA bioactivity was tested on paddy crops in germination assays, showing promising applications for crop growth and yield under sustainable agricultural practices. The ANN model moderately predicted GA concentration with a rescaled MAE of 3.97 μg/mL, covering 10–15% of the dataset range (19.4–53). The ANN accurately predicted GA concentration, but the error margins were moderate. Linear regression outperformed ANN and decision tree models, achieving nearly perfect fit (R2 = 0.9999). GA yield was primarily affected by absorbance, followed by dextrose and MgSO₄ concentrations, and minimally by peptone. This study shows an effective GA bioprocess with environmental and agricultural benefits. Further testing showed that UMM2 produced enzyme lyases like cellulases and amylases. BBD optimization with renewable resources makes GA production scalable and sustainable, making it useful for agronomic applications and environmentally friendly GA synthesis. It also opens avenues for its potential translational application in biomedical and plant-derived therapeutics.