Multivariate Image Regression for Quality Control of Natural Fiber Composites
Bibliographic record
Abstract
This study reports on using near-infrared (NIR) hyperspectral imaging for nondestructive spectral-spatial characterization of natural fiber composites. To illustrate the approach, maleic anhydride grafted polyethylene (MAPE)/hemp fiber composites were produced with different filler contents between 0 and 60%. Two different chemometrics methods based on (1) the traditional multivariate PLS calibration and (2) multivariate image analysis and regression (MIA/MIR) were tested to predict tensile properties using NIR images. The results show good agreement between the measured properties and their predictions by both of these methods. The MIR ability to map chemical composition was compared to that of multivariate curve resolution (MCR) and the results were found similar. The proposed MIR approach was found very promising for quality control of polymer composites because of its combined ability to quantify filler content and dispersion within the material, to distinguish compositional variations from physical defects, and to predict the end-user properties of the product all with a single MIR model.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".