Development of a Novel Test Method to Collect and Detect Axillary Odor within Textiles
Bibliographic record
Abstract
A preliminary study to guide development of a new method to generate and detect axillary odor in vitro was based on the incubation of ‘fresh’ sweat solution on fabrics and compared to the in vivo wear trial method. Samples of cotton and polyester knit fabrics, untreated and antimicrobial-treated with polyhexamethylene biguanide (PHMB) or zinc pyrithione (ZP), were sewn into the axillary area of T-shirts worn during vigorous exercise or incubated with composite sweat solutions from exercise participants. A trained sensory panel used a 150 mm line scale to measure odor intensity of fabric samples following wear or incubation with sweat solution. Compared with the in vivo wear trial method, the in vitro method allowed a comparison of a greater number of fabrics in a single session and limited intrapersonal and interpersonal variability in human participant odor intensity. Despite some inconsistencies between the methods, the in vitro method has potential applications for screening and evaluating textiles in a controlled laboratory environment. The addition of a trained panel sensory evaluation of odor intensity to the assessment of antimicrobial efficacy was found to be beneficial for the evaluation of antimicrobial treatments designed to reduce malodor within clothing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".