ABSTRACT
Polydiacetylene (PDA) vesicles are of interest as biosensors, particularly for pathogenic bacteria. As part of a food monitoring system, interaction with food sanitizers/surfactants was investigated. PDA vesicles were prepared by inkjet-printing, photopolymerized and characterized by dynamic light scattering (DLS) and UV/Vis spectroscopy. The optical response of PDA vesicles at various concentrations verses a fixed sanitizer/surfactant concentration was determined using a two variable factorial design. Sanitizer/surfactant response at various concentrations over time was also measured. Results indicated that only Vigilquat and TritonX-100 interacted with PDA vesicles giving visible colour change out of 8 sanitizers/surfactants tested. PDA vesicle concentration, sanitizer/surfactant concentration, and time all had a significant (P<0.0001) effect on colour change. As they are highly sensitive to the presence of Vigilquat and TritonX-100, PDA sensors could be used to detect chemical residues as well as for detection of various contaminants in the food industry.
Subject(s)
Biosensing Techniques/methods , Food Analysis/methods , Food Preservatives/metabolism , Polymers/metabolism , Polyynes/metabolism , Surface-Active Agents/metabolism , Disinfectants/analysis , Disinfectants/metabolism , Food Preservatives/analysis , Polyacetylene Polymer , Polymers/analysis , Polyynes/analysis , Sanitation/methods , Sodium Hypochlorite/analysis , Sodium Hypochlorite/metabolism , Surface-Active Agents/analysisABSTRACT
When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal's performance. We present experimental evidence consistent with this prediction and discuss other predictions applicable to more general settings.