Machine Learning-Assisted Multicolor Fluorescence Assay for Visual Data Acquisition and Intelligent Inspection of Multiple Food Hazards Regardless of Matrix Interference DOI

Tong Zhai,

Wentao Gu, Miao Yu

и другие.

ACS Sensors, Год журнала: 2025, Номер unknown

Опубликована: Май 19, 2025

Regarding the significant health risks of pesticide residue in foods, while current sensors still suffer from limited efficiency and stability, as well difficulties qualitative identification quantitative detection mixtures, development innovative techniques combined with advanced methodology holds great research value. Herein, a highly efficient intelligent food risk evaluation system was proposed by integrating multicolor fluorescent responsive assay machine learning (ML) algorithms for quantification multiple pesticides, carbendazim (CBZ), heptachlor (HEP), chlordimeform (CDF), their mixtures. This method leveraged color changes generated interaction between carbon dots (CDs) target molecules. By extracting signal feature values these reactions visual data acquisition ML models, this enables regardless matrix interference through dual-source strategy without large instruments. The developed via ″stepwise prediction″ automatically demonstrated robust capability discrimination accuracy 99.3% categorization achieving prediction (R2 ≥ 0.8946) concentration detection, verified six kinds matrix. significantly improves stability efficiency, providing promising tool safety monitoring.

Язык: Английский

Machine Learning-Assisted Multicolor Fluorescence Assay for Visual Data Acquisition and Intelligent Inspection of Multiple Food Hazards Regardless of Matrix Interference DOI

Tong Zhai,

Wentao Gu, Miao Yu

и другие.

ACS Sensors, Год журнала: 2025, Номер unknown

Опубликована: Май 19, 2025

Regarding the significant health risks of pesticide residue in foods, while current sensors still suffer from limited efficiency and stability, as well difficulties qualitative identification quantitative detection mixtures, development innovative techniques combined with advanced methodology holds great research value. Herein, a highly efficient intelligent food risk evaluation system was proposed by integrating multicolor fluorescent responsive assay machine learning (ML) algorithms for quantification multiple pesticides, carbendazim (CBZ), heptachlor (HEP), chlordimeform (CDF), their mixtures. This method leveraged color changes generated interaction between carbon dots (CDs) target molecules. By extracting signal feature values these reactions visual data acquisition ML models, this enables regardless matrix interference through dual-source strategy without large instruments. The developed via ″stepwise prediction″ automatically demonstrated robust capability discrimination accuracy 99.3% categorization achieving prediction (R2 ≥ 0.8946) concentration detection, verified six kinds matrix. significantly improves stability efficiency, providing promising tool safety monitoring.

Язык: Английский

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