Noise spectroscopy as an efficient tool for impedance based sub-femtomolar toxin detection in complex mixture using nanoporous silicon oxide.

Abstract

In this paper we demonstrate an efficient and non-interfering computational method for sub-femtomolar food toxin detection in complex mixture based on nanoporous silicon oxide impedance immunosensor by employing noise spectroscopy analysis at the peak frequency. It has been observed that the peak frequency (fp) values obtained from steady state impedance measurements cannot distinguish between solution with only the specific toxin, which is aflatoxin B1 (AfB1) and mixture of AfB1 with other non-specific toxins (NSTs), thus leading to erroneous quantification of AfB1 in complex mixture. On the other hand, the first cut-off frequency (fc) ranges obtained from noise spectroscopy analysis can qualitatively differentiate between solution containing only AfB1, AfB1 and NSTs and no AfB1. However fc values being very close for different concentration of AfB1 in pure solution and being overlapping for different mixtures cannot quantify AfB1 either in pure solution or in complex mixture. To address this problem, the proposed computational method first clusters the fp and fc values in 11 categories each using k-means clustering algorithm and then applies a simple combinational digital logic on the clusters of fps and fcs to obtain the final output, realizable with standard NAND-NOR gates. The output digital word differs only with AfB1 concentration and not with concentration of NSTs and is found to be capable of detecting sub-femtomolar AfB1 range down to 0.1 fg/ml not only in pure solution but also in complex mixture with as high as 1000 ng/ml NSTs. This is the most sensitive and selective report so far on electrochemical food toxin immunosensors.