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<p><i>Aframomum citratum</i>, a little-studied aromatic plant from Cameroon, produces essential oils rich in monoterpenes, yet their industrial use remains limited by low extraction yields. This study investigated spontaneous solid-state fermentation (SSSF) as a pretreatment to enhance essential oil recovery, combined with response surface methodology (RSM via Box-Behnken Design, BBD) and artificial neural networks (ANNs) to model and optimize the extraction yield. A 15-run experimental design was employed to evaluate the effects of fermentation time, moisture content, and hydrodistillation time. The ANN model (3-5-1 architecture using tansig and purelin transfer functions) showed slightly higher predictive performance than the quadratic RSM model (R<sup>2</sup> = 0.98 vs 0.97), reflecting its ability to capture nonlinear relationships among process variables. Model robustness was further evaluated using leave-one-out cross-validation (LOOCV), which confirmed stable predictive behavior for both approaches. Optimization using the RSM desirability function predicted a maximum oil yield of 1.73%, whereas ANN-based numerical optimization using the <i>fmincon</i> algorithm predicted a slightly higher yield of 1.77% at 9.89&#xa0;days fermentation, 42.73% moisture content, and 46.24&#xa0;min hydrodistillation. These results demonstrate that SSSF-assisted extraction coupled with data-driven modelling provides an effective strategy for improving essential oil recovery from <i>A. citratum</i> and supports its potential valorization.</p>

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Maximizing essential oil recovery from Aframomum. citratum via spontaneous solid-state fermentation: a comparative study of RSM and ANN approaches

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Sylvie Kwanga Nguikwie,
  • Steve Olugu Voundi,
  • Doriane Djuffo Tegoundio,
  • Brice Thaddee Mbalale Mbalale,
  • Victor Moussango Davy,
  • Samuel Patrick Malle Moukouri,
  • Olivier Choisi Mayouck Etane,
  • Alex Brandown Wambo Talla,
  • Marlyse Leng,
  • Achille Bissoue Nouga,
  • Frederic Marie Tavea

摘要

Aframomum citratum, a little-studied aromatic plant from Cameroon, produces essential oils rich in monoterpenes, yet their industrial use remains limited by low extraction yields. This study investigated spontaneous solid-state fermentation (SSSF) as a pretreatment to enhance essential oil recovery, combined with response surface methodology (RSM via Box-Behnken Design, BBD) and artificial neural networks (ANNs) to model and optimize the extraction yield. A 15-run experimental design was employed to evaluate the effects of fermentation time, moisture content, and hydrodistillation time. The ANN model (3-5-1 architecture using tansig and purelin transfer functions) showed slightly higher predictive performance than the quadratic RSM model (R2 = 0.98 vs 0.97), reflecting its ability to capture nonlinear relationships among process variables. Model robustness was further evaluated using leave-one-out cross-validation (LOOCV), which confirmed stable predictive behavior for both approaches. Optimization using the RSM desirability function predicted a maximum oil yield of 1.73%, whereas ANN-based numerical optimization using the fmincon algorithm predicted a slightly higher yield of 1.77% at 9.89 days fermentation, 42.73% moisture content, and 46.24 min hydrodistillation. These results demonstrate that SSSF-assisted extraction coupled with data-driven modelling provides an effective strategy for improving essential oil recovery from A. citratum and supports its potential valorization.