Abstract
The of the quality of odors emitted from automobile cabin interiors is an important element for the design of vehicles that meet prospective customers' expectations. Extending our previous work on machine-versus-human odor assessment for intact automobile cabin interiors, in this paper, we evaluated odors generated from individual interior parts using a human panel and field asymmetric ion mobility spectrometry (FAIMS). We used image processing techniques to extract geometric features from FAIMS dispersion fields, and built the predictive models for three odor assessment parameters (intensity, irritation, and pleasantness) by means of partial least squares regression. The best feature set was chosen by backward sequential feature selection. Using k -fold cross validation, we achieved statistically significant correlation 0.95 between human panel measured and machine olfaction predicted odor assessment scores with a sample set of 48 interior automobile parts. These results, generated using the geometric image processing methods demonstrated herein, further support the feasibility of replacing a human panel by machine olfaction for the assessment of odor quality of interior automobile parts.
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