Algorithm for recognition of optical spectra in an environment containing interferants

Wade, B.; Estes, C.; Lloyd, Christopher R.; Powers, Linda S.

doi:10.1109/iembs.2001.1017442

Cited by 3 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our prototype devices use multiple intrinsic fluorescence markers [6], [7], [9]- [11] including those of metabolites which are present only during cellular respiration [15]. Fuzzy algorithms and neural networks are used with these multiple markers to distinguish the presence of microbial contamination from environmental interferences [7], [9]- [11], [16]. Measurement of the background in the off cycle of the pulsed source (e.g., xenon flask lamp) allows contributions from other light sources to be subtracted from each measurement pulse.…”

Section: Detection Technologymentioning

confidence: 99%

Is what you eat and drink safe? detection and identification of microbial contamination in foods and water

Lloyd¹,

Cleary

Kim

et al. 2003

Proc. IEEE

View full text Add to dashboard Cite

Current technologies for assessing the microbial load in food and water require cellular outgrowth to increase the cell count to a detectable level. This step therefore makes real-time assessment of microbial loads impossible. To circumvent this problem, we have developed a high-sensitivity, multiwavelength fluorescence detector and signal-collection and processing software. This detection system enables one to distinguish between abiotic matter, sporulated bacteria, vegetative bacteria, dead bacteria, and other biomaterials containing aromatic amino acids. Our detection limit is in the 10-100 cell/cm 2 range (or per cm 3 if bulk aqueous samples are used), and there is no sample contact. To supplement this detection system, we have developed a variety of ligands, including hemin derivatives, peptides, glycoconjugates, and siderophores, for cell capture to circumvent problems associated with antibodies. These materials are tethered to disposable surfaces (glass, plastics) in arrays for use in capturing biomaterials (e.g., live bacterial cells, dead cells, spores, and toxins); identification is based on which sector of an array it binds to. The detection system can serve as a reader for these coated materials, and variants have been tested for use in scanning food surfaces and food wash water.

show abstract