Environmentally\nSustainable Detection of Arsenic using\nConvolutional Neural Networks and Imidazole-Based Organic Probes:\nApplication in Food Samples and Arsenic Album

Arsenic contamination poses a significant health risk,\nparticularly\nwhen it infiltrates water supplies. While current detection methods\noffer precise analysis, they often involve complex instrumentation\nnot suitable for field use. This study presents a novel approach by\ndeveloping two probes, A1 and A2, based on 4-diethylaminosalicyladehyde,\n2-hydroxy-1-naphthaldehyde, and 1,2-diaminoanthraquinone. These probes\nare highly sensitive and selective for detecting arsenite (As(III))\nand arsenate (As(V)) in water, food samples, and homeopathic medicine\nwith limits of detection in the nanomolar range. To elaborate our\ncontribution to on-site arsenic detection, we introduce a convolutional\nneural network-based image recognition system. This system interprets\nimages of the probes’ colorimetric response, effectively categorizing\ndifferent ranges of arsenic concentrations in parts per million (ppm).\nOur approach offers a real-time, cost-effective, and user-friendly\nsolution for arsenic detection, extending its applicability from scientific\nlaboratories to in-field conditions and even household monitoring.\nThe findings fill critical research gaps in real-time detection methods,\npotentially revolutionizing the way we monitor environmental contaminants\nlike arsenic.