Does Data Collection Device Affect Sensory Descriptive Analysis Results?
Bibliographic record
Abstract
Abstract The objective of this study was to determine if data capture device type had a significant influence on sensory descriptive analysis results. 12 trained assessors evaluated 4 snack bar products in triplicate on each of three devices (iPod, iPad, external monitor). Four‐way univariate analysis of variance detected no significant product by device interaction in 19 of 20 attributes. Products were ranked in a similar manner with regards to attribute intensity on all devices. Both Principal Component Analysis and Generalized Procrustes Analysis multivariate configurations showed very similar arrangements for all products and devices. The input device is identified as a potential factor in the trend toward lower absolute scale values with increasing screen sizes. Practical Applications The computerized devices that are available for sensory data collection have changed over the years. Research facilities often compare historical data to newly obtained data sets; however, the question remains: can data be compared when different data collection devices were used? This study determined that descriptive analysis test results are comparable if data are collected on an iPod, an iPad and a laptop with a monitor display.
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 | 0.043 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".