Chapter 6. Conclusions and Future Work
6.3 Wear Trial
The wear trial was considered a success despite some setbacks throughout the process. The air sampling and GC-FID methods were used in conjunction with established odor panel techniques to assess human-worn footwear both subjectively and objectively. The data from the two assessment styles were then correlated to provide line of best fit equations which, in theory, could be used to predict expected average odor scores for a specific population based on detected concentrations.
The original idea was to establish a link between the changes in perception to the changes in sampled odor concentrations. By comparing the average odor scores from a single point in
time to the concentrations detected at the same point in time, the data sets seemed relatable, therefore accomplishing the original plan for correlation.
The subjective data provided the odor perceived by a human population on average for two shoe structures, one of which included a variation that was treated with an antimicrobial finish. The objective data provided the proportionate concentrations of the odor compounds which were present in the shoes during the time of the odor panel. The subjective data needs to be used in conjunction with the objective data because it gives the much needed perspective of end users. This same perspective cannot be observed by quantitative data alone.
The odor panel portion of the wear trial had shown that there was no perceivable difference between the treated and control versions of the prototype shoe, however the prototype as whole was perceivably less odorous than the commercially available shoe throughout the wear trial. Further investigation resulted in seeing a plateauing effect on observed odor in the prototype, but a continued increase in the commercially available shoe (within the timeframe of the study). The plateauing may have been the result of odor equilibrium within the shoes. The equilibrium was possibly the result of odor compounds leaving the shoes at the same rate that they were created and accumulated. The data suggests that the treatment may not have a significant effect on perceived odor, but the difference in structure of the shoes did.
The objective data results generally followed the same trend over time as odor scores did. The similarity between the two makes sense because theoretically the presence of more odor compounds present should correspond to increased odor which is perceived. Further investigation had shown that concentrations sampled for Shoe B were actually lower than Shoe A throughout the study. The data suggests that the shoes simply sample differently and one
cannot solely use detected concentrations of the odors to predict odor perception with the current method. Shoe B sampled less odor compounds theoretically because of the structure once again. Shoe B had more material and considerably less ventilation than Shoe A, thus making it more difficult for airflow carrying odor compounds to reach the silica gel. The method proves to be fine for comparing the same shoe structure against one another (helpful when testing a treatment or finish), however, at the moment it may not accurately compare differing shoe structures. One should keep in mind that differing shoe structure may sample differently until the method is improved to be unaffected by the build of the product.
The subjective data and objective data were then plotted against one another for comparison to provide correlating data. The results from comparing the average group odor score, at a given point in time, to the average detected concentration, at that same point in time, show the overall effect that each odor compound concentration has on the perception of odor scores. The comparison provides a line of best fit equation which can potentially allow for the prediction of an average odor score of a population based on the concentration of a specific compound in a particular shoe.
The ability to predict average group odor scores with odor concentration data can move research in the field towards decreasing the constant reassembly of odor panels. After initial testing is conducted for a particular shoe, one can predict the average human response with the line of best fit equation provided by the relationship between detected concentrations and original odor panel scores. The possibility exists, as well, that the values can be used in relation to a list of independent variables such as the distance ran in the shoes or even the amount of time one only ran or only walked in the shoes. From there, one can potentially determine the
odor-based lifetime of a shoe. If an odor score is determined to be the point by which most of the tested population sees that the shoe is worth throwing out, one can predict at which point the shoe will reach that odor score based on the rise of the concentration of odors over time or distance.
The initial testing needed to reach these conclusions must be on a very large scale with a much grander population than the one used for this study. The amount of time by which the shoes are observed should also be dramatically increased to reach a sufficient amount of data points necessary for an accurate representation of what is actually happening. Had the time and resources been available to do so in this study, the data may have been able to show at which point the shoes would reach an odor equilibrium (if they were ever going to).
After this round of testing, it was also determined that the test population should be more well trained for the odor panel aspect of the methodology and that more defined, predetermined odor scores should be used in that training process. The odor scores are based on the perception of individuals. With lack of basis for how something should be scored, their answers may vary week to week. Although subjects were trained on previously worn shoes with somewhat predetermined odor scores attached to them, the process could stand to be improved for more consistent scoring by the population. Subjects could have been provided with better predetermined odor descriptors as well. When asked to describe the odors they smelled in the shoes, subjects were not directed to use specific words (i.e. vinegar-like, cheesy, and sweaty), although some were suggested for use. The lack of direction led to a large variation in descriptions for the same shoe, making it difficult to see if subjects were detecting higher amounts of one of the three tested compounds.