Applying Kinetic Spectrophotometric Method and Artificial Neural Network Model for Determination of Metronidazole with Albizia Lebbeck Leaves-capped AgNPs Sensor in Blood and Urine Samples

The environmental pollution caused by drug antibiotic waste presents a foremost concern in the ecosystem, as  high  levels  of  these  antibiotic  drugs  after  consumption  when  released  into  the  ecosystem,  biological samples  are  accumulated  and  are  producing  overall  contamination.  Consequently,  the  need  for  selective, sensitive, fast, easy-to-handle, and low-cost early monitoring detection systems is growing. In this study, we used a prepared Albizia Lebbeck Leaves-capped AgNPs sensor to illustrate examples of friendly biosensors with  their  real  application  fields for  the  sensitive  detection  of the  metronidazole  drug  in  various  matrices such as human fluids by kinetic spectrophotometric method. The calibration curve was linear in the range of (0.02 to 10.0 µg L−1). The standard deviation of less than (3%), and detection limits (3S/m) of the method (0.02 µg L−1in time 8 min, 367 nm) were obtained for sensor level response Albizia Lebbeck Leaves-capped AgNPs with (95%) confidence evaluated. The artificial neural network model was used as a tool very low for determining  mean  square  error  (MSE  0.061)  for  metronidazole  drug  by Albizia  Lebbeck  Leaves-capped AgNPs sensor. The observed outcomes confirmed the suitability of recovery and a very low detection limit for  measuring  the  metronidazole  drug.  The  method  introduced  to  measure  metronidazole  drugs  in  real samples  such  as  urine  and  blood  was  used  and  can  be  used  for  other  drugs  environmental  pollution  and hospital samples.