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Applied and Environmental Microbiology, February 2000, p. 694-699, Vol. 66, No. 2
0099-2240/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.

Application of Neural Computing Methods for Interpreting Phospholipid Fatty Acid Profiles of Natural Microbial Communitiesdagger

Peter A. Noble,1,* Jonas S. Almeida,2 and Charles R. Lovell3

Belle W. Baruch Institute for Marine Biology and Coastal Research1 and Department of Biological Sciences,3 University of South Carolina, Columbia, South Carolina 29208, and Chemistry Department, FCT/UNL, and ITQB/UNL, 2780-901 Oeiras, Portugal2

Received 27 August 1999/Accepted 28 October 1999

The microbial community compositions of surface and subsurface marine sediments and sediments lining burrows of marine polychaetes and hemichordates from the North Inlet estuary (near Georgetown, S.C.) were analyzed by comparing ester-linked phospholipid fatty acid (PLFA) profiles with a back-propagating neural network (NN). The NNs were trained to relate PLFA inputs to sediment type outputs (e.g., surface, subsurface, and burrow lining) and worm species (e.g., Notomastus lobatus, Balanoglossus aurantiacus, and Branchyoasychus americana). Sensitivity analysis was used to determine which of the 60 PLFAs significantly contributed to training the NN. The NN architecture was optimized by changing the number of hidden neurons and calculating the cross-validation error between predicted and actual outputs of training and test data. The optimal NN architecture was found to be four hidden neurons with 60-input neurons representing the 60 PLFAs, and four output neurons coding for both sediment types and worm species. Comparison of cross-validation results using NNs and linear discriminant analysis (LDA) revealed that NNs had significantly fewer incorrect classifications (2.7%) than LDA (8.4%). For the NN cross-validation, both sediment type and worm species had 3 incorrect classifications out of 112. For the LDA cross-validation, sediment type and worm species had 7 and 12 incorrect classifications out of 112, respectively. Sensitivity analysis of the trained NNs revealed that 17 fatty acids explained 50% of variability in the data set. These PLFAs were highly different among sediments and burrow types, indicating significant differences in the microbiota.

* Corresponding author. Mailing address: Belle W. Baruch Institute for Marine Biology and Coastal Research, University of South Carolina, Columbia, SC 29208. Phone: (803) 777-3928. Fax: (803) 777-3935. E-mail: noble{at}biol.sc.edu.

dagger This is contribution number 1192 of the Belle W. Baruch Institute for Marine Biology and Coastal Research.

Applied and Environmental Microbiology, February 2000, p. 694-699, Vol. 66, No. 2
0099-2240/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.


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