colorimetric-based artificial tongue for discrimination of neurodegenerative disorders biomarkers in the urine sample

Catecholamine neurotransmitters (cns), including dopamine (da), epinephrine (ep), and norepinephrine (ne) are known as the main index of various neurological diseases such as parkinson's [1]. on the other, levodopa (ld), the precursor of these biomarkers, is prescribed to slow the progression of parkinson's disease [2]. therefore, concurrent discrimination of da, ep, nep, and ld are extremely recommended in early-stage clinical diagnosis and evaluation of disease response to treatment. in this respect, in this research, a high-performance colorimetric artificial tongue has been proposed for the identification and discrimination of catecholamines and levodopa based on their ionic states in various buffering conditions and involvement in the aggregation of capped-gold nanoparticles. the amassed aunps led to a redshift in the localized surface plasmon resonance (lspr) peak of aunps, altering the color of the solution, and creating a unique visual pattern for each analyte. diverse gold-nanoparticle (aunp)-aggregation behaviors and distinct fingerprint response patterns were checked out by linear discriminate analysis (lda) in order to classify da, ep, nep, and ld. the proposed colorimetric array was successfully able to differentiate among solitary neurotransmitters as well as their mixtures with acceptable accuracy, sensitivity, and specificity. besides, partial least square regression (plsr), a multivariate calibration method, was applied for quantitation modeling. then, the analytical figures of merit verified that the designed sensor array is precise and accurate in both the discrimination and quantification of the analytes by the linear range of 0.6-9 µm (r2 = 0.99) for da, 0.1-10 µm (r2 = 0.99) for ep, 0.1-9 µm (r2 = 0.99) for nep and 1-70 µm (r2 = 0.99) for ld. finally, the feasible potential of the array was validated in the human urine sample as one of the complex biological fluids.