This study underlines the biotechnical valorization of the accumulated and unusable remains of agro-industrial orange fruit peel waste to produce α-amylase under submerged conditions by Streptomyces sp. KP314280 (20r). The response surface methodology based on central composite design (RSM-CCD) and artificial neural network coupled with a genetic algorithm (ANN-GA) were used to model and optimize the conditions for the α-amylase production. Four independent variables were evaluated for α-amylase activity including substrate concentration, inoculum size, sodium chloride powder (NaCl), and pH. A ten-fold cross-validation indicated that the ANN has a greater ability than the RSM to predict the α-amylase activity (R²_ANN = 0.884 and R²_RSM = 0.725). The analysis of variance indicated that the aforementioned four factors significantly affected the α-amylase activity. Additionally, the α-amylase production experiments were conducted according to the optimal conditions generated by the GA. The results indicated that the amylase yield increased by 4-fold. Moreover, the α-amylase production (12.19 U/mL) in the optimized medium was compatible with the predicted conditions outlined by the ANN-GA model (12.62 U/mL). As such, the ANN and GA combination is optimizable for α-amylase production and exhibits an accurate prediction which provides an alternative to other biological applications.

这项研究强调了通过淹没条件下农业产业橙果皮垃圾的积累和不可用的遗体，产生α淀粉酶的生物技术稳定物价链霉菌属。KP314280 (20r)。基于中心复合设计 (RSM-CCD) 和人工神经网络结合遗传算法 (ANN-GA) 的响应面方法用于建模和优化 α-淀粉酶生产条件。评估了 α-淀粉酶活性的四个自变量，包括底物浓度、接种物大小、氯化钠粉末 (NaCl) 和 pH。十倍交叉验证表明，ANN 比 RSM 具有更大的预测 α-淀粉酶活性的能力（R ² _ANN  = 0.884 和 R ²_RSM  = 0.725)。方差分析表明，上述四个因素对α-淀粉酶活性有显着影响。此外，α-淀粉酶生产实验是根据 GA 产生的最佳条件进行的。结果表明淀粉酶产量增加了4倍。此外，优化培养基中的 α-淀粉酶产量 (12.19 U/mL) 与 ANN-GA 模型 (12.62 U/mL) 概述的预测条件兼容。因此，ANN 和 GA 组合可针对 α-淀粉酶生产进行优化，并表现出准确的预测，为其他生物应用提供了替代方案。