The extraction yield of Juglans nigra L. leaves was assessed at different ethanol concentrations (0–96% (v/v)) and solvent-to-solid ratios (5–20 kg kg⁻¹). The response surface methodology (RSM) and artificial neural network with genetic algorithms (ANN-GA) were developed to optimize the extraction variables. The RSM and ANN-GA models determined 50% (v/v) ethanol concentration and 20 kg kg⁻¹ solvent-to-solid ratio as optimal conditions, ensuring an extraction yield of 27.69 and 27.19 g 100 g⁻¹ of dry leaves. The phenolic compounds in optimal extract were quantified: 3-O-caffeoylquinic acid (2.27 mg g⁻¹of dry leaves), quercetin-3-O-galactoside (10.99 mg g⁻¹ of dry leaves) and quercetin-3-O-rhamnoside (15.07 mg g⁻¹of dry leaves) using high-performance liquid chromatography (HPLC). The minerals in optimal extract were quantified: macro-elements (the relative order by content was: K > Mg > Ca) using inductively coupled plasma optical emission spectrometry (ICP-OES) and micro-elements (the relative order by content was: Zn > Rb > Mn > I>Sr > Ni > Cu > Co > V > Ag > Se) using inductively coupled plasma mass spectrometry (ICP-MS). The extraction coefficients for minerals were determined and were highest for K (64.3%) and I (53.5%). Optimization of extraction process resulted in high extraction yield from J. nigra leaves and optimal extract containing different phytochemical compounds.

在不同的乙醇浓度（0–96％（v / v））和溶剂与固体的比率（5–20 kg kg ^-1）下评估胡桃叶的提取率。开发了响应面方法（RSM）和带有遗传算法的人工神经网络（ANN-GA）以优化提取变量。RSM和ANN-GA模型确定了50％（v / v）的乙醇浓度和20 kg kg ^-1的溶剂固比作为最佳条件，确保了100 g ^-1干叶的提取量为27.69和27.19 g 。定量提取最佳提取物中的酚类化合物：3-O-咖啡酰奎尼酸（2.27 mg g ^-1的干叶），槲皮素-3-O-半乳糖苷（10.99 mg g ^-1使用高效液相色谱（HPLC）分析槲皮素3-O-鼠李糖苷（15.07 mg g ^-1干燥叶子）。定量提取最佳提取物中的矿物质：使用电感耦合等离子体发射光谱法（ICP-OES）定量分析宏观元素（相对含量的顺序为：K> Mg> Ca）和微量元素（相对含量的顺序为：Zn） > Rb> Mn> I> Sr> Ni> Cu> Co> V> Ag> Se）使用电感耦合等离子体质谱（ICP-MS）。确定了矿物质的提取系数，其中K（64.3％）和I（53.5％）最高。提取工艺的优化导致黑黑麦草叶片的高提取率和含有不同植物化学化合物的最佳提取物。