Nanocrystalline Silicon Oxide Impedance Immunosensors for Sub-Femtomolar Mycotoxin Estimation in Corn Samples by Incremental Fuzzy Approach

Detection of trace quantities of common mycotoxins (which have carcinogenic effects) in food samples with sufficient accuracy when present with large quantities of non-target toxins is an existing challenge. To address the problem, in this paper, we have deployed nanocrystalline silicon oxide impedance immunosensor array coupled with incremental fuzzy logic. These sensors have been reported earlier to be more sensitive and specific, compared with others. But toxin estimation becomes sufficiently imprecise especially when the concentration of the non-target toxin is significantly more than that of the target toxin. It is, therefore, expected that this problem will aggravate for real food samples. Thus, to make these sensors commercially viable, the sensor data have been processed by the incremental fuzzy approach. The flexibility in this approach has enabled estimation of three mycotoxins in corn samples procured from two different geographical locations of India. The detection limit achieved is as low as 0.1 fg/ml with 93% accuracy even in presence of large quantities of non-target toxins and with limited calibration data set. In comparison with the existing reports, this is the most sensitive and selective approach for mycotoxin estimation in real food samples with negligible effect of food matrix interference.