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Membrane engineering is a generic methodology for increasing the selectivity of a cell biosensor against a target molecule, by electroinserting target-specific receptor-like molecules on the cell surface. Previous studies have elucidated the biochemical aspects of the interaction between various analytes (including viruses) and their homologous membrane-engineered cells. In the present study, purified anti-biotin antibodies from a rabbit antiserum along with in-house prepared biotinylated bovine serum albumin (BSA) were used as a model antibody-antigen pair of molecules for facilitating membrane engineering experiments. It was proven, with the aid of fluorescence microscopy, that (i) membrane-engineered cells incorporated the specific antibodies in the correct orientation and that (ii) the inserted antibodies are selectively interacting with the homologous target molecules. This is the first time the actual working concept of membrane engineering has been visualized, thus providing a final proof of the concept behind this innovative process. In addition, the fluorescence microscopy measurements were highly correlated with bioelectric measurements done with the aid of a bioelectric recognition assay.

We developed a novel artificial neural network (ANN) system able to detect and classify pesticide residues. The novel ANN is coupled, in a customized way, to a cellular biosensor operation based on the bioelectric recognition assay (BERA) and able to simultaneously assay eight samples in three minutes. The novel system was developed using the data (time series) of the electrophysiological responses of three different cultured cell lines against three different pesticide groups (carbamates, pyrethroids, and organophosphates). Using the novel system, we were able to classify correctly the presence of the investigated pesticide groups with an overall success rate of 83.6%. Considering that only 70,000–80,000 samples are annually tested in Europe with current conventional technologies (an extremely minor fraction of the actual screening needs), the system reported in the present study could contribute to a screening system milestone for the future landscape in food safety control.

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