We hereby report the design and implementation of an Autonomous Microbial Cell Culture and Classification (AMC(3)) system for rapid detection of food pathogens. Traditional food testing methods require multistep procedures and long incubation period, and are thus prone to human error. AMC(3) introduces a "one click approach" to the detection and classification of pathogenic bacteria. Once the cultured materials are prepared, all operations are automatic. AMC(3) is an integrated sensor array platform in a microbial fuel cell system composed of a multi-potentiostat, an automated data collection system (Python program, Yocto Maxi-coupler electromechanical relay module) and a powerful classification program. The classification scheme consists of Probabilistic Neural Network (PNN), Support Vector Machines (SVM) and General Regression Neural Network (GRNN) oracle-based system. Differential Pulse Voltammetry (DPV) is performed on standard samples or unknown samples. Then, using preset feature extractions and quality control, accepted data are analyzed by the intelligent classification system. In a typical use, thirty-two extracted features were analyzed to correctly classify the following pathogens: Escherichia coli ATCC#25922, Escherichia coli ATCC#11775, and Staphylococcus epidermidis ATCC#12228. 85.4% accuracy range was recorded for unknown samples, and within a shorter time period than the industry standard of 24 hours.

The integration of biological building-blocks with synthetic nanomaterials may permit unprecedented ability to detect, disinfect and completely remove pathogens in water. We hereby described the synthesis of biodegradable, interpenetrating polymeric networks of poly (amic) acid (PAA), glutaraldehyde-derivatized PAA (PAA-GA) and chitosan-modified poly (amic) acid (PAA-CS) using phase-inversion procedures. The characterization data from NMR, FT-IR, SEM and cyclic voltammetry confirmed the successful formation of electroactive, bifunctional, glutaraldehyde-linked PAA membranes. Toxicological, electrochemical and mechanical characterization data showed the successful formation of non-toxic, biodegradable, porous, free-standing and mechanically strong membranes. PAA-GA showed the highest modulus of 568.1 Mpa followed by PAA-CS-GA (495.0 Mpa). The optimized membranes were tested against three of the most common drinking water contaminants, namely Escherichia coli, Citrobacter freundii and Staphylococcus epidermidis with 100% removal achieved using dead end filtration and tangential flow filtration.

Bacterial biofilms can be beneficial or detrimental, and are capable of forming on virtually any surface. There is a great need for an in situ sensor able to detect and characterize the developmental stages of bacterial biofilms. We have developed an electrochemical approach to detect and characterize bacterial biofilms using polypyrrole (PPy) enhanced flexible biofilm sensors based on organic substrates of Polyethylene terephthalate (PET). PPy films act as a functionalization material on gold electrodes to reduce their electrical impedance thereby enhancing the detection of electrochemical signals. Flexible PET substrates enable sensors to be placed in systems with complex geometries and to be produced using low cost roll-to-roll manufacturing. Measurements of electrochemical impedance spectroscopy (EIS) using the PET flexible biofilm sensors were correlated with fluorescence microscopy using bacteria that express green fluorescent protein (GFP). The biofilm sensors successfully detected the changes of charge transfer resistance and capacitance corresponding to the maturing stages of biofilm development. The charge transfer resistance increases during the early stages of biofilm maturation and decreases in the later stages of development.

The objective of this research is to develop a complex adaptive piecewise linear regression/probabilistic neural network (PNN) intelligent system for the rapid detection and classification of Escherichia coli (E.coli). The rapid detection and classification of E.coli is important because current methods require a long period of analysis before a classification can be determined. The objective of this paper is to describe the design and preliminarily evaluate an Intelligent Decision Support System (IDSS) that will validate the following hypotheses: an intelligent decision support system (IDSS) to allow the rapid collection and classification of E.coli can be designed and preliminarily evaluated, which will significantly decrease detection and classification times for E.coli bacteria, thereby addressing the food spoilage problem. The research in this paper provides a preliminary answer to: What performance improvement percentage can be realized against the 16 to 48 hours required for the conventional multistep methods of detection of microorganisms (using E.coli data as a baseline)? For the 16 hour period we have a 6.7% reduction in the time-to-detect period ((16-15)/15 × 100% = 6.7%) and for the 48 hour period we have a 220% reduction in time ((48- 15)/15×100% = 220%).

Recent outbreaks of listeria, salmonella, and other pathogens have reinforced the need for more rigorous testing of food products. Millions are spent each year testing food. Certifying the safety of the food is a challenging task using traditional testing methods. Current methods require long incubation times before the first results are observed and still only represent a small fraction of the food that is sold. Long analysis methods also lead to loss of consumables. 18.9 billion pounds of produce are lost a year to spoilage. A fast and effective method is needed to decrease the amount of time necessary to test the safety of food. The goal is to provide accurate sample classification as quickly as possible, thus allowing pathogen-free product to be shipped to market with the shortest delay possible. An autonomous electrochemical sensor was combined with a powerful multi-class Probabilistic Neural Network (PNN) system to classify four species of organisms (E. Coli #25922, E. Coli # 11775, S. Epidermis #12228, or C. Albicans #10231). We used an evolutionary based kernel optimization algorithm to optimize the kernel parameters, and trained the system on data sampled from four different organisms. The trained and optimized model was validated on a set containing several samples that were not used to train the network. We showed that the network was able to correctly classify unknown samples in a shorter period than the industry standard of 24 hours, thus providing a potential benefit to the agriculture industry.

We describe the development of an electrochemical sensor for early detection of biofilm using Candida albicans. The electrochemical sensor used the ability of biofilms to accept electrons from redox mediators relative to the number of metabolically active cells present. Cyclic voltammetry and differential pulse voltammetry techniques were used to monitor the redox reaction of K3Fe(CN)6 at porous reticulated vitreous carbon (RVC) (238.7cm2) working electrodes versus Ag/AgCl reference. A shift in the peak potential occurred after 12h of film growth, which is attributed to the presence of C. albicans. Moreover, the intensity of the ferricyanide reduction peak first increased as C. albicans deposited onto the porous electrodes at various growth times. The peak current subsequently decreased at extended periods of growth of 48h. The reduction in peak current was attributed to the biofilm reaching its maximum growth thickness, which correlated with the maximum number of metabolically active cells. The observed diffusion coefficients for the bare RVC and biofilm-coated electrodes were 2.2×10−3 and 7.0×10−6cm2/s, respectively. The increase in diffusivity from the bare electrode to the biofilm-coated electrode indicated some enhancement of electron transfer mediated by the biofilm to the porous electrode. Verification of the growth of biofilm was achieved using scanning electron microcopy and laser scanning confocal imaging microscopy. Validation with conventional plating techniques confirmed that the correlation (R2=0.9392) could be achieved between the electrochemical sensors data and colony-forming units.