Subjectivity in testing for automotive validation processes is typically a sticking point for many suppliers and OEMs. For vehicle interiors, in addition to the laboratory testing, human panels of “trained noses” are used to judge the different components and the completed vehicle.
In the automotive industry, there is no standardization as each OEM has their own testing specifications, rating scale and methods. In addition to the variation in OEM specification, there are also global specifications issued by SAE, VDA, and ISO. This lack of unified quantitative norms leads to increased costs and timing for suppliers, lab-to-lab variations in results and ultimately longer development times for the OEMs.
With the advances in sensors, biochemistry and machine learning, odor detection and classification can be achieved with the use of a digital olfaction device, or “electronic nose”. We will demonstrate the ability to identify different types of plastics, the ability to differentiate between compound variants of the same material and a proof of concept for detection of odors in the automotive domain based on the ISO 12219-7 and VDA270 standards. We will also propose a quantifiable rating of odor which mimics the human panel established norms, in a way which is reproducible and independent of variation between individual testers. Ultimately, standardization of odor testing with consistent, repeatable and cost-effective digital olfaction methods can bring traceability and credibility to Tier 1 suppliers that translate to end-product quality for OEMs.
