Time Intensity tests are designed to measure the temporal evolution of a single attribute.
Results are plotted as time-intensity curves and key parameters of the curves are calculated. Results can be averaged per panellist or per product.
Time - intensity (T-I) studies address the relationship between the onset, intensity, and duration of perception of a sensory attribute. In practice, the time and intensity are initially set at zero, subjects are exposed to a stimulus, and the perceived intensity is then recorded with the corresponding time coordinates until the intensity returns to zero again, or the allocated time for the assessment has ended. These relationships are commonly illustrated in the form of Time Intensity curves of perceived intensity versus time. [from Lui and MacFie paper]
The key parameters of the time-intensity curves are calculated in the analysis and are presented in the results.
The time-intensity curve can be divided into four distinct
phases. In practice a curve may not
conform strictly to these phases:
When the curves are averaged, across the Panel or across Panellists, and the Standardize option is not selected, the raw curves are simply averaged - so the average curve is the average intensity taken at each time point.
Where the curves need to be averaged, and the Standardize option is selected, the method in the 1990 Liu and MacFie paper ‘Methods for averaging time-intensity curves’ are followed.
The standardization method works as follows:
If the Correction option is selected, then outliers and anomalies are recoded. And anomaly is defined as, for example, an increase during the decreasing phase. If an intensity point detected at time (t+1) named as It+1 is detected as larger than It and It+1 is larger than It+2 then It+1 is considered an outlier and is replaced by the average between It and It+2.
When the “By Replica” option is selected, users can also use the Results table to run ANOVA, for example, on the parameters. The Excel export file of the results table needs to be imported as a dataset in EyeOpenR to run an ANOVA. In order to make the table compatible with EyeOpenR Row 1 (with the table title) and Column A (with the row numbers) needs to be removed so that the Assessor column starts in cell A1.
Y.H. Liu and H.J.H MacFie, methods for averaging time-intensity curves. Chemical Senses, Vol 15, pp471-484, 1990.