Near infrared reflectance spectroscopy for the fast identification of PVC-based films
Bibliographic record
Abstract
Near infrared (NIR) reflectance spectroscopy was used to develop a non-destructive and rapid qualitative method for the analysis of plastic films used by the pharmaceutical industry for blistering. Three types of films were investigated: 250 microm PVC [poly(vinyl chloride)] films, 250 microm PVC films coated with 40 g m(-2) of PVDC [poly(vinylidene dichloride)] and 250 microm PVC films coated with 5 g m(-2) of TE (Thermoelast) and 90 g m(-2) of PVDC. Three analyses were carried out using different pre-treatment options and a PLS (partial least squares) algorithm. Each analysis was aimed at identifying one type of film and rejecting all types of false sample (different thickness, colour or layer). True and false samples from four plastics manufacturers were included in the calibration sets in order to obtain robust methods that were suitable regardless of the supplier. Specificity was demonstrated by testing validation sets against the methods. The tests showed 0% of type I (false negative identification) and 1% of type II errors (false positive identification) for the PVC method, 13 and 3%, respectively, for the PVC-PVDC method and no error for the PVC-TE-PVDC method. Type II errors, mostly due to the slight sensitivity of the methods to film thickness, are easily corrected by simple thickness measurements. This study demonstrates that NIR spectroscopy is an excellent tool for the identification of PVC-based films. The three methods can be used by the pharmaceutical industry or plastics manufacturers for the quality control of films used in blister packaging.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".