The espresso protocol as a tool for sensory quality evaluation
Bibliographic record
Abstract
Espresso is prepared differently from filter coffee as pressure extracts flavor components from ground coffee. Nowadays it is enjoyed by many as it is or in espresso-based drinks. The Espresso Protocol (TEP) is a new method for assessing the quality of espressos by utilizing sensory evaluation techniques, such as the just-about-right (JAR) scale and the check-all-that-apply (CATA). This study aimed to evaluate the discriminability of TEP. Coffee experts from the US/Canada (n=32) and Australia/New Zealand (n=31) participated in the study. Twelve coffees were shipped for evaluation using espresso machines in their respective homes. As a result of the response analysis using the frequency of CATA to identify the participants' coffee culture differences, no significant cultural differences were identified in the two groups, the US/Canada and Australia/New Zealand. CATA results enabled discrimination among samples and were able to indicate characteristics associated with high quality coffee and able to identify 'defect' in samples. Defect due to container contamination was perceived from flavor evaluation only. There was no significant difference between the initial quality score and overall quality scores evaluated at this tool's beginning and end, except for the defective coffee sample. Between the percentages of participants who were willing to use the bean for espresso extraction and overall quality scores, there was a high correlation. Penalty analysis coupling overall quality score and just-about-right evaluations of each category indicated their influence on quality perception. Furthermore, no significant differences between blind duplicate coffee samples confirmed consistent measurement of this tool. TEP can be used to evaluate the quality of coffee beans for espresso by coffee experts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".