Authentication of cinnamon spice samples using FT-IR spectroscopy and chemometric classification
Bibliographic record
Abstract
Cinnamon is a popular spice with a lengthy overseas supply chain. C. cassia is commonly traded as cinnamon, but the use of rapid methods to detect its adulteration has not yet been fully addressed. This work explores the use of FT-IR spectroscopy for the detection of adulteration in the cinnamon supply chain by several lower value ingredients. Two species of cinnamon (C. verum and C. cassia) and an adulterant (cinnamon spend, n = 2) were used to create 110 different in-house admixtures. Two different replacement fraud experiments were designed: C. cassia replaced with spend (Scenario A) and C. verum replaced with both C. cassia and spend (Scenario B). Initial analysis by GC-IMS showed promising differences between samples. The FT-IR spectra confirmed significant raw differences in absorbance. PCA for Scenario A demonstrated better separation than in Scenario B. The detection of adulteration of C. cassia (Scenario A) and C. verum (Scenario B) were equality accurate. Classification results showed that the PLS-DA technique was superior to SIMCA for both types of adulteration (PLS-DA: 94-90%; SIMCA: 83-79%, respectively). This demonstrates the potential of FT-IR as a screening method to identify cinnamon adulteration in supply chains and to provide accurate and rapid results without sample preparation.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".