Discovering the unknown: Detection of emerging pathogens using a label‐free light‐scattering system

Abstract

A recently introduced technique for pathogen recognition called BARDOT (BActeria Rapid Detection using Optical scattering Technology) belongs to the broad class of optical sensors and relies on forward-scatter phenotyping (FSP). The specificity of FSP derives from the morphological information that bacterial material encodes on a coherent optical wavefront passing through the colony. The system collects elastically scattered light patterns that, given a constant environment, are unique to each bacterial species and serovar. The notable similarity between FSP technology and spectroscopies is their reliance on statistical machine learning to perform recognition. Currently used methods utilize traditional supervised techniques which assume completeness of training libraries. However, this restrictive assumption is known to be false for most experimental conditions, resulting in unsatisfactory levels of accuracy, poor specificity, and consequently limited overall performance for biodetection and classification tasks. The presented work demonstrates application of the BARDOT system to classify bacteria belonging to the Salmonella class in a nonexhaustive framework, that is, without full knowledge about all the possible classes that can be encountered. Our study uses a Bayesian approach to learning with a nonexhaustive training dataset to allow for the automated detection of unknown bacterial classes.

Biophotonics: a European perspective

“…The use of labels strongly hampers the use of the technologies for monitoring and real-time control application. Numerous projects are trying to study diverse modalities of imaging and sensing (labeled for higher sensitivity, label-free for high frequency analysis) [8], [9], [10]. During these break-out sessions, followed-up by more than 50 experts in Biophotonics and components manufacturers, discussions have been held to answer questions about the future evolution of markets or technologies, about critical components and about the importance of photonics in the development of new markets and technologies.…”

Development of a microbial high-throughput screening instrument based on elastic light scatter patterns

“…19,[34][35][36][37] Briefly, the scatter patterns are saved as bitmaps and subsequently analyzed utilizing image-processing approaches, including the use of pseudo-Zernike moments and Haralick texture features. 36,37 Since their introduction by Hu, moments have been employed in numerous applications ranging from optical character recognition and face recognition to image registration.…”

Virulence Gene-Associated Mutant Bacterial Colonies Generate Differentiating Two-Dimensional Laser Scatter Fingerprints

“…A total of 60 scatter images per strain from two independent experiments with two technical replicates were acquired to train the scatter image libraries of the mutant and WT strains. Scatter patterns were analyzed by using a custom-developed pattern recognition algorithm (image classifier) that relies on machine-learning algorithms and training with scatter images of reference bacteria, and data are presented as classification accuracies or positive predictive values (PPVs) (34,35). Various factors, including the rotation parameter invariant, are characterized by the Zernike moment (order of 8) and the textures that are used in feature extraction.…”