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Odorous air samples collected from diversified sources were presented to the olfactometry panel, an electronic nose, and hydrogen sulfide (H2S) and ammonia (NH3) detectors. The measurements of the olfactometry (odor concentrations) were used as the expected values while measurements with other instruments as input variables. Both linear regression and artificial neural networks (ANN) are used to test the multiple hypotheses made for surrogating the olfactometry measurements. Principal component analysis (PCA) is utilized to reduce the dimensionality of the electronic nose measurements from 35 to 3 without significant loss of information. The dimensionally reduced datasets were used to train an ANN. As an individual instrument, an electronic nose or H2S detector alone can predict odor concentration measurements with similar accuracy (R2=0.46 and 0.51). Although NH3 detector alone has a very poor relationship with the odor concentration measurements, the integration of H2S detector and NH3 detector can predict odor concentrations more accurately (R2=0.58) than either individual instrument. Data from the integration of the AromaScan electronic nose, a H2S detector, and a NH3 detector produced the best prediction of odor concentrations with a regression determination coefficient of R2=0.75. With this accuracy, odor concentration measurements can be confidently surrogated with the integration of the AromaScan electronic nose, H2S detector, and NH3 detector.