Method for the spectral identification of microorganisms

Abstract

A rapid method for characteterizing and identifying micsoorganisims using Focal-Plane Array (FPA)-Fourier Transform Infrared (FTIR) spectroscopy is disclosed. Multi-pixels spectral images of unknown microorganisms spectra are analyzed and compared to spectra of reference microorganisms in databases. The method allows rapid and highly reliable identification of unknown microorganisms for the purpose of medical diagnosis, food and environmental control.

1 . A method for characterizing a microorganism said method comprising:
 a) obtaining at least one multi-pixels spectral image of said microorganism;   b) selecting one or more spectra from said multi-pixels spectral image based on pre-determined spectral characteristics wherein the selected spectra comprise spectral information characteristics of the microorganism.   
     
     
         2 . The method as claimed in  claim 1  further comprising the step of identifying said microorganism by comparing said one or more selected spectra of said microorganism with spectra of reference microorganisms in a database to determine an identity of said microorganism. 
     
     
         3 . The method as claimed in  claim 2  wherein said database is established by:
 a) obtaining at least one multi-pixels spectral image for each of a plurality of reference microorganisms, each pixel exhibiting a signal corresponding to a spectrum of a reference microorganism; and 
 b) selecting spectra from said multi-pixel spectral images based on pre-determined spectral characteristics to establish said database, wherein said database comprises at least one spectrum for each of said reference microorganisms. 
 
     
     
         4 . The method as claimed in  claim 1  wherein said predetermined spectral characteristics are selected from: signal-to-noise, spectral intensity and spectral variability. 
     
     
         5 . The method as claimed in of  claim 1  wherein said step of selecting comprises identifying chemical inhomogeneities and using the chemical inhomogeneities as a basis for selecting. 
     
     
         6 . The method as claimed in of  claim 1  wherein said step of selecting comprises comparing said pre-determined spectral characteristics with spectral characteristics of a reference spectrum. 
     
     
         7 . The method as claimed in  claim 6  wherein said reference spectrum is an average spectrum obtained by averaging spectra of selected pixels of said multi-pixels spectral image of said reference microorganism. 
     
     
         8 . The method as claimed in  claim 7  wherein said selected pixels comprise all the pixels from said image. 
     
     
         9 . The method as claimed in  claim 1  further comprising the step of processing said selected spectra to optimize spectral comparison between spectra of the database and said one or more spectra of said unknown microorganism. 
     
     
         10 . The method as claimed in  claim 9  wherein said step of processing is selected from adjusting a baseline, obtaining a spectral derivative, normalizing peak height or intensity, smoothing, data interpolation, resolution enhancement and combination thereof. 
     
     
         11 . The method as claimed in  claim 3  further comprising the step of generating at least one sub-database comprising partial or transformed spectral data from said selected spectra or said processed selected spectra to reduce dimensionality of the data space and wherein said step of comparing is performed using said at least one sub-database. 
     
     
         12 . The method as claimed in  claim 11  wherein said partial spectral data is selected based on distinguishing spectral features of said reference microorganisms. 
     
     
         13 . The method as claimed in  claim 12  wherein said distinguishing spectral features are identified using a method selected from: principal component analysis, wavelet transform, cluster analysis, multivariate statistics, artificial neural networks, support vector machines, genetic algorithms, and grid and greedy search algorithms or combinations thereof. 
     
     
         14 . The method as claimed in  claim 2  wherein said step of comparing further comprises co-adding spectra from said selected spectra to create subsets of spectra and comparing one or more of said subsets with spectra of said reference microorganism. 
     
     
         15 . The method as claimed in  claim 1  wherein said microorganisms are selected from bacteria, viruses and unicellular eukaryotes. 
     
     
         16 . The method as claimed in  claim 1  wherein said selected spectra are obtained from predetermined pixels. 
     
     
         17 . The method as claimed in  claim 2  wherein said step of comparing is performed using a method selected from K-nearest-neighbor algorithm, artificial neural networks, multivariate statistics, support vector machines and hierarchical database distribution and combination thereof. 
     
     
         18 . The method as claimed in  claim 1  wherein said multi-pixels spectral image is obtained using a array detector 
     
     
         19 . The method as claimed in  claim 18  wherein said array detector is selected from a linear array and a multiple arrays detector. 
     
     
         20 . The method as claimed in  claim 19  wherein the array is an infrared Focal Plane Array. 
     
     
         21 . The method as claimed in  claim 1  wherein multiple samples of microorganisms are included simultaneously in one image. 
     
     
         22 . A database comprising reference spectra of microorganisms said database obtained by the method of  claim 3 .