Attention has been paid to the early diagnosis of Alzheimer's disease, due to the maximum benefit acquired from the early-stage intervention and treatment. However, the sensing techniques primarily depended upon for neuroimaging and immunological assays for the detection of AD biomarkers are expensive, time-consuming and instrument dependent. Here, we developed a multichannel fluorescent tongue consisting of four fluorescent dyes and GO through electrostatic and π-π interaction. The array distinguished multiple aggregation states of 1 µM Aβ40/Aβ42 with 100% prediction accuracy via 10-channel signal outputs, illustrating the rationality of the array design. Screening vital sensor elements for the simplified sensor array and the optimization of sensing system was achieved by machine learning algorithms. Moreover, our sensing tongue was able to detect the aggregation states of Aβ40/Aβ42 in serum, demonstrating the great potential of multichannel array in diagnosing the Alzheimer's diseases.
Keywords: Alzheimer’s disease; amyloid-β protein; machine learning algorithm; multichannel; sensor array.