Pesticide residue screening using a novel artificial neural network combined with a bioelectric cellular biosensor

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

104 HybES: a Hybrid Expert System

Abstract. This paper describes the architecture, representation and operation of a Hybrid Expert System (HybES) in the sense of incorporating methodologies of rule based systems and relational databases within fuzzy logic data sets and criteria. HybES is a Rule Based Expert System, deployed on a relational database with its rules based on the Object-Attribute-Value (O-A-V) triplet representation model. Every rule is valid for a specific time period and, consequently, the system is valid at certain time periods, using different valid-time versions of knowledge. The values of the O-A-V triplets are related to fuzzy sets, handling linguistic variables, therefore these are fuzzy values and each one depends on one or more criteria affecting it. The system infers using either the forward chaining (applying ‘rules forward ’ or the backward chaining (a bottom-up method, applying the rules in the opposite way). Using a simple technique, the working memory of the system is split into two parts (Conditions’ and Rules ’ Working Memory), which are used according to system’s needs.

E-Democracy, Security, Privacy and Trust in a Digital World5th International Conference, E-Democracy 2013, Athens, Greece, December 5-6, 2013, Revised Selected Papers /

XIV, 225 p. 35 illus.online resource

The Use of Artificial Neural Networks as a Component of a Cell-based Biosensor Device for the Detection of Pesticides

AbstractThe present study describes an artificial neural network (ANN) system that uses a cell-based biosensor based on the Bioelectric Recognition Assay (BERA) methodology, for the detection and classification of pesticide residues in food commodities. The insecticidal compounds carbaryl and chlorpyrifos as well as the pyrethroid group were used as models for the training of the ANN. The biosensor was based on neuroblastoma N2a cells, which are targets of the pesticides due to the inhibition of the enzyme acetylcholine esterase by them. The response of the biosensor to different concentrations (samples) of either pesticide was recorded as a time-series of potentiometric measurements (in Volts). The feedforward methodology was used for the development of the ANN, which was trained with the backpropagation training algorithm. The results of the application of the developed system indicate that the novel classification methodology exhibits promising performance as a central component of a rapid, high throughput screening system for pesticide residues