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Sensory evaluation is where food science meets human perception—and on your exam, you're being tested on more than just knowing test names. You need to understand when to use each technique, what kind of data it produces, and why one method works better than another for a given research question. These techniques fall into distinct categories based on their purpose: some detect differences, some measure preferences, and some create detailed sensory profiles.
The key to mastering this topic is recognizing the underlying logic behind each method. Are you trying to find out if two products differ? How much consumers like something? What specific attributes define a product's sensory fingerprint? Don't just memorize technique names—know what question each one answers and what type of panelist (trained expert vs. average consumer) it requires.
These tests answer a simple but critical question: Can people tell these samples apart? They use forced-choice designs where panelists must pick an answer, eliminating "I don't know" responses. The statistical power comes from comparing results against chance probability.
Compare: Triangle Test vs. Duo-Trio Test—both detect differences between products, but the duo-trio provides a reference sample that reduces panelist confusion. If an FRQ asks about testing reformulated products with subtle differences, the triangle test offers more statistical power per panelist.
These methods capture how much people like products or which they prefer. Unlike discrimination tests, affective tests require untrained consumers—you want real-world reactions, not expert analysis. The data drives marketing and product development decisions.
Compare: Hedonic Scale vs. JAR Scale—both measure consumer response, but hedonic tells you how much they like it while JAR tells you what to fix. Use hedonic for go/no-go decisions; use JAR for product optimization.
These techniques create detailed "fingerprints" of products using trained panelists who function like calibrated instruments. The goal isn't preference—it's objective measurement of what's there and how much.
Compare: Descriptive Analysis vs. QDA—descriptive analysis identifies what attributes exist; QDA measures how much of each attribute is present. Think of descriptive analysis as creating the vocabulary and QDA as using that vocabulary to generate data.
Threshold tests determine the minimum detectable or distinguishable concentration of a stimulus. These methods reveal how sensitive human perception is to specific compounds—essential for quality control and off-flavor detection.
Compare: Absolute vs. Difference Threshold—absolute threshold asks "can you detect anything?" while difference threshold asks "can you tell these two apart?" Difference thresholds are typically 1-2% of the baseline concentration for most tastants.
| Concept | Best Examples |
|---|---|
| Discrimination (difference detection) | Triangle Test, Duo-Trio Test, Paired Comparison |
| Affective (consumer preference) | Hedonic Scale, Preference Ranking |
| Diagnostic (optimization) | JAR Scale, Penalty Analysis |
| Descriptive (profiling) | Descriptive Analysis, QDA |
| Temporal dynamics | Time-Intensity Evaluation |
| Sensitivity measurement | Threshold Tests (absolute, difference) |
| Trained panel required | QDA, Descriptive Analysis, Threshold Tests |
| Untrained consumers required | Hedonic Scale, Preference Ranking, JAR Scale |
A company reformulates their cookie recipe to reduce sugar by 15%. Which discrimination test would provide the most statistical power to determine if consumers can detect the change, and why?
Compare and contrast the Hedonic Scale and JAR Scale: What type of data does each produce, and how would you use them together in product development?
Which two techniques require trained panelists to function as "calibrated instruments," and what makes training essential for their validity?
If an FRQ asks you to design a study measuring how mint flavor perception changes from the moment of consumption through 60 seconds later, which technique would you select and what parameters would you measure?
A beverage company wants to know both whether consumers prefer their new formula and what specific attributes to adjust if they don't. Identify two complementary techniques and explain how their data would work together.