Near Infrared Reflectance Spectroscopy as a Tool for the Determination of Dichloromethane Extractable Matter and Moisture Content in Combed Wool Slivers
Bibliographic record
Abstract
In the wool textile field the quantitative determination of solvent extractable matter and moisture content is a crucial analysis for the evaluation of combed sliver quality. The test carried out includes the acquisition of a series of data about dichloromethane soluble matter (according to the International Wool Textile Organisation—IWTO 10–01 specification), and moisture content in combed wool slivers and the search for a correlation between these data and the near infrared spectra of samples. Combed wool slivers tested were of different origins and variable mean diameters and were obtained from different combing mills in the industrial district of Biella, the principal wool textile region of Italy. The spectrophotometer used was a FT-NIR system (Perkin-Elmer Spectrum IdentiCheck). Spectra were collected in the region from 3700 to 10,000 cm −1 in reflection mode. The extraction of greasy matter from wool tops was carried out with a continuous extraction technique on a 10 g wool sample with a total extraction time of about 3 hours in a soxhlet apparatus. The results express the weight (obtained by the mean of two determinations) of the dichloromethane soluble extract as a percentage of the dry weight of the de-greased sample. For the determination of dichloromethane extractable matter, 103 samples were used for calibration. Some wool samples were deliberately under-scoured and others were re-scoured in a combing mill in order to obtain a wide range of data, ranging from 1.15% to 0.21%. Spectra were analysed using Quant+ (Perkin-Elmer Software). The best results were obtained with the PLS1 (Partial Least Square) algorithm when considering the spectral region 9000–3800 cm −1 [Standard Error of Prediction ( SEP) = 0.1042, mean value ( M) = 0.6963%, Coefficient of Determination ( R 2 ) = 0.85]. A cross-validation was used. The determination of moisture content in combed wool sliver was performed by drying wool (80 g for a single determination) in an oven to constant weight using forced air at 105 ± 2°C. 85 samples were used for the calibration. Deliberate variations in regain were induced by exposing the wool to a dry or moist atmosphere. In this way moisture contents ranging from 9% to 15% were obtained. NIR spectra were analysed using Quant+ Software. A cross-validation was used. The best results were obtained with the Principal Component Regression algorithm when considering the spectral region 9000–3800 cm −1 . A SEP of 0.4954 ( M = 11.76%) and a R 2 of 0.90 were found. A limited number of determinations of grease and moisture content were carried out using the models obtained and compared with values determined manually.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".