TABLE 3.

Identification results for modular ANNs obtained with the training data set and the test data set for external validation

Net level and microorganism	No. (%) of isolatesa
	Total	Training data set (training/internal validation)b	Test data set (external validation)c	Correct identification	No identification
Top-level netd
P. aeruginosa	17	12	5	5 (100)
BCC	107	78	29	29 (100)
S. maltophilia	26	18	8	8 (100)
A. xylosoxidans	21	15	6	5 (83.3)	1 (16.7)
R. pickettii	2		2	2 (100)
Acinetobacter spp.	11	8	3	3 (100)
Total	184	131	53	52 (98.1)	1 (1.9)
Second-level nete
B. cepacia	10	7	3	3 (100)
B. multivorans	3	2	1	1 (100)
B. cenocepacia	83	58	25	23 (92)	2 (8)
B. stabilis	4	3	1	1 (100)
BCCf	2		2	2 (100)
Total	102	70	32	30 (93.8)	2 (6.2)

^aThere were no misidentifications.

^bThe training data set contained 131 isolates (including reference strains) and was used for training and internal validation. The internal validation, which was performed with the 131 isolates (including reference strains) from the training data set, yielded 100% correct identification for all the species.

^cThe test data set included 53 clinical isolates and was used for external validation.

^dNeuroDeveloper software (version 2.3; Synthon KG) was used to develop and optimize a modular system. The whole database used contained almost 2,000 first-derivative spectra obtained from 169 clinical isolates and 15 reference strains. The topology for the top-level net was 60 wavelengths, two hidden units, and six output neurons.

^eThe topology for the second-level net was 15 wavelengths and two hidden neurons optimized for the four output neurons.

^fBCC corresponded to two clinical isolates (isolates HST 8684 and HC BC05) that were identified as belonging to BCC by recA PCR with primers BCR1 and BCR2 but that could not be identified by PCR with species-specific primers.