Odor in textiles: A review of evaluation methods, fabric characteristics, and odor control technologies
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".