1. A method of distinguishing Alzheimer's disease from Parkinson's disease and neither Alzheimer's disease nor Parkinson's disease in a subject, the method comprising the steps of: (a) providing a system comprising an apparatus comprising at least two sensors, a first of said sensors comprising nanomaterials selected from the group consisting of single walled carbon nanotubes (SWCNTs) coated with β-cyclodextrin SWCNTs coated with carboxy-methylated β-cyclodextrin, SWCNTs coated with hydroxypropyl-β-cyclodextrin, and SWCNTs coated with heptakis(2,3,6-tri-O-methyl)-β-cyclodextrin, and a second of said sensors comprising nanomaterials selected from the group consisting of Au nanoparticles capped with 3-mercapto-propionate, and Au nanoparticles capped with 2-mercapto-benzoazole; and a processing unit comprising a learning and pattern recognition analyzer; (b) exposing the at least two sensors to a test exhaled breath sample; (c) measuring at least one response induced parameter from each of the at least two sensors upon exposure to the test sample to obtain a response pattern, wherein the response pattern is formed by the sensor detection of at least one volatile biomarker which is indicative of Alzheimer's disease; (d) analyzing the response pattern obtained in step (c) using the learning and pattern recognition analyzer by comparing the response pattern stored data obtained from a control sample and a Parkinson's disease sample, whereby a significantly different response pattern of the test sample as compared to the control sample and the Parkinson's disease sample is indicative of Alzheimer's disease, and (e) presenting the resulting information on a display or transmitting said information to a host computer using the processing unit.