Odor in textiles: A review of evaluation methods, fabric characteristics, and odor control technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During use, textile items can develop unpleasant odors that arise from many different sources, both internal and external to the human body. Laundering is not always effective at removing odors, with odor potentially building up over time due to incomplete removal of soils and odorous compounds and/or malodors transferred during the laundering process. Textile odor can lead to consumer dissatisfaction, particularly as there are high expectations that clothing and textile products meet multiple aesthetic and functional needs. The problem of odor in textiles is complex and multi-faceted, with odorous volatile compounds, microorganisms, and precursors to odor, such as sweat, being transferred to, and retained by, fabrics. This article reviews the literature that specifically relates to odor within textiles. Methods for evaluating odor in textiles, including methods for collecting odor on textile substrates, as well as sensory and instrumental methods of odor detection, were reviewed. Literature that examined differences among fabrics that varied by fabric properties were reviewed. As well, the effectiveness of specific odor controlling finishing technologies to control malodor within textiles was also examined.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it