Advancing recycling efficiency through hyperspectral identification of fabric blends
Bibliographic record
Abstract
Textile sorting for recycling and revalorisation is a multifaceted challenge that requires accurate material classification. We demonstrate the effectiveness of short-wave infrared (SWIR) hyperspectral imaging as a method employed to address this challenge. The utilization of various data processing strategies enables us to ascertain the accuracy of textile blends. Employing the Multivariate Curve Resolution-Alternating Least Square algorithm, we establish an uncertainty range of ±2.7 - 5.0% using pure elements as a training set. To achieve this, we employ multiple pre-processing methods to enhance the spectrum and assess alternative regression algorithms, such as Multivariate Regression-Partial Least Square and Principal Component Algorithm. Additionally, we conducted tests using two hyperspectral systems with distinct spectral ranges: one extending up to 2500 nm and the other up to 1700 nm. Furthermore, a study on the influence of fabric color on regression and textile spectra was conducted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".