Odorous air samples collected from several sources were presented to an olfactometer, an electronic nose, a hydrogen sulfide (H(2)S) detector and an ammonia (NH(3)) detector. The olfactometry measurements were used as the expected values while measurements from the other instrumentation values became input variables. Five hypotheses were established to relate the input variables and the expected values. Both linear regression and artificial neural network analyses were used to test the hypotheses. Principal component analysis was utilized to reduce the dimensionality of the electronic nose measurements from 33 to 3 without significant loss of information. The electronic nose or the H(2)S detector can individually predict odor concentration measurements with similar accuracy (R(2) = 0.46 and 0.50, respectively). Although the NH(3) detector alone has a very poor relationship with odor concentration measurements, combining the H(2)S and NH(3) detectors can predict odor concentrations more accurately (R(2) = 0.58) than either individual instrument. Data from the integration of the electronic nose, H(2)S, and NH(3) detectors produce the best prediction of odor concentrations (R(2) = 0.75). With this accuracy, odor concentration measurements can be confidently represented by integrating an electronic nose, and H(2)S and NH(3) detectors.