Machine-learning-aided identification of steroid hormones based on the anisotropic galvanic replacement generated sensor array
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...
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| Published in | Sensors and actuators. B, Chemical Vol. 370; p. 132470 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Lausanne
Elsevier B.V
01.11.2022
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-4005 1873-3077 |
| DOI | 10.1016/j.snb.2022.132470 |
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| Abstract | 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 h and low-cost strategy, its further application in the field of cosmetic safety is highly expected.
•A sensor array consisting of 3 anisotropic Cu-based nanomaterials was constructed.•k-nearest neighbors (k-NN) algorithm was used to improve the analytical performance•Five kinds of steroid hormones can be accurately identified and determined using k-NN assisted sensor array.•A quick analysis of hormones in real samples was realized with 1.5 h as a sample to result time. |
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| AbstractList | 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 h and low-cost strategy, its further application in the field of cosmetic safety is highly expected. 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 h and low-cost strategy, its further application in the field of cosmetic safety is highly expected. •A sensor array consisting of 3 anisotropic Cu-based nanomaterials was constructed.•k-nearest neighbors (k-NN) algorithm was used to improve the analytical performance•Five kinds of steroid hormones can be accurately identified and determined using k-NN assisted sensor array.•A quick analysis of hormones in real samples was realized with 1.5 h as a sample to result time. |
| ArticleNumber | 132470 |
| Author | Chen, Huiting Lin, Peiru Zeng, Ying Tao, Jia Ouyang, Sixue Chen, Yuying Zhao, Peng Zou, Xun Liu, Lina Ren, Qingfan |
| Author_xml | – sequence: 1 givenname: Yuying surname: Chen fullname: Chen, Yuying organization: School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China – sequence: 2 givenname: Peiru surname: Lin fullname: Lin, Peiru organization: NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China – sequence: 3 givenname: Xun surname: Zou fullname: Zou, Xun organization: NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China – sequence: 4 givenname: Lina surname: Liu fullname: Liu, Lina organization: School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China – sequence: 5 givenname: Sixue surname: Ouyang fullname: Ouyang, Sixue organization: School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China – sequence: 6 givenname: Huiting surname: Chen fullname: Chen, Huiting organization: School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China – sequence: 7 givenname: Qingfan surname: Ren fullname: Ren, Qingfan organization: School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China – sequence: 8 givenname: Ying surname: Zeng fullname: Zeng, Ying organization: School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China – sequence: 9 givenname: Peng surname: Zhao fullname: Zhao, Peng email: smuzp@smu.edu.cn organization: NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China – sequence: 10 givenname: Jia surname: Tao fullname: Tao, Jia email: cejtao@scut.edu.cn organization: School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China |
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| Keywords | Anisotropic galvanic replacement Sensor array Steroid hormones Machine learning |
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| SubjectTerms | Algorithms Anisotropic galvanic replacement Copper Copper oxides Cost analysis Gold Hormones Hydrogen peroxide Machine learning Palladium Sensor array Sensor arrays Sensors Steroid hormones Steroids Substrates |
| Title | Machine-learning-aided identification of steroid hormones based on the anisotropic galvanic replacement generated sensor array |
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