The conveniently simultaneous identification of steroid hormones is challenging due to their similar chemical structures. Herein, we have established a machine-learning-aided sensor array for accurate discrimination and determination of steroid hormones based on Cu@Cu₂O (CC), Cu@Cu₂O@Pd (CCP) and Cu@Cu₂O@PdAu (CCPA). The Michaelis-Menten constant of CC, CCP and CCPA toward H₂O₂ were calculated as 2.61, 1.11 and 2.03 mM, respectively, indicating their different catalytic activities that were obtained from the anisotropic galvanic replacement of Cu@Cu₂O with Pd (II) and Au (III). Five kinds of steroid hormones were selected as model targets and reacted with the sensor array before chromogenic reaction. The absorption of chromogenic substrate was used as learning data to train the k-nearest neighbors algorithm, the discrimination confidence was from 88.9% to 100% for different mixtures, and 100% for betamethasone of 1-50 μM in real sample. This work provides a quick analysis of steroid hormones in 1.5 hours and low-cost strategy, its further application in the field of cosmetic safety is highly expected.

由于类固醇激素的化学结构相似，因此方便地同时鉴定类固醇激素具有挑战性。在此，我们建立了一种基于 Cu@Cu ₂ O (CC)、Cu@Cu ₂ O@Pd (CCP) 和 Cu@Cu ₂ O@PdAu的机器学习辅助传感器阵列，用于准确区分和测定类固醇激素。 (CCPA)。CC、CCP 和 CCPA 对 H ₂ O ₂的 Michaelis-Menten 常数分别计算为 2.61、1.11 和 2.03  mM，表明它们通过各向异性的电流置换 Cu@Cu _{2获得了不同的催化活性}O 与 Pd (II) 和 Au (III)。选择五种类固醇激素作为模型靶点，在显色反应前与传感器阵列发生反应。以显色底物的吸收作为学习数据训练k-最近邻算法，不同混合物的鉴别置信度为88.9%~100%，真实样品中1~50 μM倍他米松的鉴别置信度为100%。该工作提供了1.5小时内类固醇激素的快速分析 和低成本策略，其在化妆品安全领域的进一步应用备受期待。