Bacteria. Detection using Optical Scattering Technology (BARDOT) Modified approach in light scattering project

Transcription

1 Project Start Date: Feb Duration years Optical forward scattering for colony identification and differentiation of bacterial species Investigators: Arun K. Bhunia, E. D. Hirleman, J.P. Robinson, B. Rajwa Center for Food Safety and Engineering, Purdue University Collaborator: Gary Richards, USDA-ARS, DE CFSE Annual Review Meeting Oct -, Optical forward scattering for rapid identification and differentiation of bacterial species..an interdisciplinary approach Light scattering Prof. A. Bhunia Dr. P. Banada Microbiology Karleigh Huff Abrar Adil (UG) Amanda Lathrop Dept of Food Science Image analysis Prof. D. Hirleman Dr. Euiwon Bae S. Guo Dr. Nebeker, Ben Buckner School of Mechanical Engineering USDA-ARS Support/ collaboration Prof. J. P. Robinson Dr. B. Rajwa Bulent Bayraktar Dept of Basic Biomedical Sciences E Light scattering sensor for bacterial detection/identification Scatter-images of representative Listeria species C cytogenes ATCC L. ivanovii ATCC L. innocua F B D A L. seeligeri LA L. welshimeri ATCC L. grayi LM Schematic representation of the laser scatterometer used to perform analysis of Banada et al.. Biosensors and Bioelectronics, Published online (Sept ) bacterial colonies. A -nm diode laser, B Petri dish containing bacterial colonies, Bayraktar et al.. Journal of Biomedical Optics (): C CCD camera, D Petri-dish holder, and E detection screen. Modified approach in light scattering project Objectives Bacteria Rapid Detection using Optical Scattering Technology (BARDOT) Laser Improve BARDOT (BA( BActeria Rapid Detection using Optical Scattering Technology) design (Hirleman) Generate scatter images of bacterial colonies of different genera: stress; food testing (Bhunia) Image analysis (Robinson) Petri dish with colonies CCD chip Scatter image Computer

2 Bacteria Rapid Detection using Optical Scattering Technology (BARDOT) BARDOT automation Integrating mechanical part (XY stage) and detector part (sensor) ) with algorithm Mirror Red diode laser Petri dish CCD camera Generate scatter images of pure cultures of different bacteria (Genus) to create image libraries Listeria Escherichia Staphylococcus Bacillus Miscellaneous Liquid cultures diluted, spread on BHI agar ( ml) plate to obtain ~~ colonies. Colony size~..-mm Incubation temp (- C) and time (- h) variable cytogenes V (/a) L. welshimeri L. grayi L. seeligeri V L. innocua F (d) F (/b) F (b) (/c) L. ivanovii C Nonpathogenic Escherichia coli Sal. Enteritidis EHEC E. coli K EPEC Pattern I Pattern II Typhimurium - E. coli O:H E E. coli O:H E. coli O:H K Sal. Typhimurium copenhagen PT E. coli E O:H ETEC E. coli O:HSEA A E. coli O:H EDL Pattern III Tennessee PT E. coli O:K:NM E. coli O:H B E. coli O:H G Kentucky E. coli O:H E. coli O:H K E. coli O:H G Agona PT

3 Staphylococcus S. aureus ATCC S. aureus isolate cincinnatiensis S. aureus S- S. aureus ATCC alginolyticus campbelli fluvialis S. epidermidis ATCC S. epidermidis isolate mimicus parahaemolyticus anguillarum S. epidermidis isolate S. hyicus T holisae orientalis metchinovic S. xylosus VA S. lentus VB vulnificus Opaque (pathogenic!) Transparent (nonpathogenic!) MLT MLT Light scattering image of stress-exposed exposed bacteria BARDOT analysis of viable but not culturable after resuscitation Control Resuscitation after VBNC state MLT MLT V. vulnificus VBNC V. vulnificus MLT MLT MLT *Pathogens are typically opaque. V. vulnificus VBNC V. vulnificus LL V. vulnificus VBNC V. vulnificus Osmotic stress on cincinnati (Cont) % NaCl broth (Cont) % NaCl broth Osmotic stress Heat Stress Heat shock: Lm V at C for h - plated on BHI agar Heat and acid stress Acid Stress ph stress: Lm F grown in BHI broth with ph of. % NaCl broth % NaCl broth Control F % NaCl broth % NaCl broth Control - Lm V Control Lm F % NaCl Agar Plate % NaCl Agar Plate Osmotic stressed F No survivors on % NaCl agar cytogenes F grown in BHI broth with.% NaCl for h then plated on BHI agar Heat stress recovered Lm V Acid-stressed Lm F

4 Food testing with BARDOT g meat spiked with cfu of bacteria ml of enrichment broth (UVM for Listeria; mec for E. coli O:H) incubated for h BHI agar: Incubate - - h for Listeria; - h for E. coli O:H BARDOT analysis with spiked meat sample Ground beef spiked with cytogenes F Isolate Isolate Isolate Isolate BARDOT Huff et al (unpublished) cytogenes F CONTROL BARDOT analysis with spiked meat sample Modeling the Colony x e Ground beef spiked with E. coli O:H EDL Ä n Σ Σ n Σ w b R Isolate Isolate Isolate Isolate? n n Ä Morganella morganelli E. coli O:H EDL Control Huff et al (unpublished) Phase Contrast ( hr) Confocal Microscope BARDOT Specifications/Parameters λ Beam diameter Stage holder. nm mm XY stage CCD pixel Unit pixel mm/.mm x µm Modeling I Applying Rayleigh Sommerfeld diffraction theorem, we model the diffraction using amplitude and phase modulation y s y a y i xs xa xi Laser Diode Mirror XY stage r sa r ai θ z Bacteria sample CCD Image sensor Source z = Bacteria colony z = z a Image ( xa + ya ) E( xi, yi ) C t( xa, ya ) exp exp[ ik ( xa, ya )] φ w ( z ) Σ exp[ iπ ( f xxa + f y ya )] dxadya

5 Modeling II Phase Contrast Micrograph of Listeria ivanovii showing microstructure Modeling III Listeria ivanovii, z= mm BARDOT Measurement Model Prediction Modeling IV Listeria innocua, z= mm Zernike orthogonal moments Ensemble of features In pattern classification problems, the choice of features to include in the feature vector is a difficult one. Tchebyshev orthogonal moments Feature selection (e.g. by stepwise discriminant analysis) Ensemble of features BARDOT Measurement Model Prediction Haralick texture descriptors The feature selection step can be used to choose a subset of features which gave performance equivalent to the entire set of candidate features, while utilizing less computational resources. Feature selection can be also used to boost the performance of the classifier Image analysis using D radial Zernike polynomials New set of features Haralick texture descriptors In many images, structures are discernible visually based on textural rather than brightness differences. Texture operators are able to enhance and quantify this differences Frits Zernike The Nobel Prize in Physics The Zernike polynomials are a set of orthogonal polynomials that arise in the expansion of a wavefront function for optical systems with circular pupils. They were introduced by F. Zernike in : Zernike, F. "Beugungstheorie des Schneidenverfahrens und seiner verbesserten Form, der Phasenkontrastmethode." Physica, -,. Graphical representation of radial Zernike polynomials Z n,m in D (image size x pixels), and their magnitudes: A real part Z, ; B imaginary part Z, ; C magnitude Z, ; D real part Z, ; E imaginary part Z, ; F magnitude Z,. The larger the n- m difference, the more oscillations are present in the shape. Features used in this study are the magnitudes of Zernike polynomials. One may note that the values of the magnitudes do not change when arbitrary rotations are applied. Scatter image of L. Ivanovii processed with three different Haralick operators

6 Method: Automated classification using support vector machines The SVM algorithm creates a hyperplane that separates the data into two classes with the maximum-margin. margin. The SVM idea was proposed by Vladimir Vapnik in For categorical variables a dummy variable is created with case values as either or. Thus, a categorical dependent variable consisting of three levels, say (A, B, C), is represented by a set of three dummy variables: A: { }, B: { }, C: { } Automated classification of Listeria spp. L. welshimeri ATCC L. innocua V PC PC PC PC PC PC PC PC PC PC L. ivanovi ATCC L. ivanovi SE PC PC PC PC PC PC PC PC PC PC cytogenes ATCC cytogenes V PC PC PC PC PC PC PC PC PC PC Canonical PC PC PC PC PC Canonical PC Canonical and PCA plots illustrating features extracted from scatter patterns produced by colonies belonging to six different strains of Listeria. Legend: L. welshimeri, L. innocua V, L. ivanovi, L. ivanovi SE, cytogenes cytogenes V PC Classification success rate for Listeria spp. BARDOT system, SVM algorithm L. welshimeri L. welshimeri.%.%.%.%.%.% L. innocua V.%.%.%.%.%.% L. ivanovi.%.%..%.%.% L. ivanovi SE.%.%.%.%.%.% cytogenes.%.%.%.%.%.% cytogenes V.%.%.%.%.%.% Confusion matrix showing classification success for mixture of colonies belonging to four species (six strains) of Listeria. Classification rates have been established using x crossvalidation. L. innocua V L. ivanovi L. ivanovi SE cytogenes cytogenes V Training and testing mixture of species Only examples shown Failure of clustering we need supervised machine learning Canonical Canonical Training set Canonical Canonical Canonical Canonical Test set Canonical - - Listeria - - Canonical Training set Testing set Canonical Non-supervised learning Canonical Ground truth (visualization of the a priori information about the test set)

7 Visualization of the classification results Canonical Only examples shown Classification example mixture of species Mixture of Listeria,, and Canonical -D visualization of the test set Canonical CV=.% Listeria CV=.% CV=.% Canonical - S. Agona CV=. S. Ent. CV=. Listeria Listeria.%.%.%.%.%.%.%.%.% Canonical Classification result (SVM) Listeria Canonical Misclassified colonies are shown as red crosses Bootstrap error estimate % S. Ent. S. Agona S. Ent..%.% S. Agona.%.% Overview of the classification system (in development) Currently classification system works offline. Data set have to be transferred to a separate computer workstation for classification. Next goal: real-time classification Collect your data Collect training dataset Select features Model-based selection Statistics-based selection Build the classifier Train and validate the classifier Summary - BARDOT Detects viable cells Virtually reagentless and involves minimal steps Noninvasive and nondestructive method and colonies can be used for further confirmation/verification Actual detection time: Seconds-minutes Classify in real-time Summary- BARDOT Scatter patterns are distinct and unique for each genus (Listeria, and ) ) and species analyzed so far.. Patterns are highly variable among strains of vulnificus Thank You Overall classification success: Differentiation among Listeria, and CV=-% % ( % bootstrap error) Listeria species CV=-% ( % bootstrap error) species CV=-% % ( % bootstrap error) species CV=-% % ( % bootstrap error) BARDOT based detection was successful with stress exposed and spiked food samples