METODE SPEKTROSKOPI ATR-FTIR TANDEM PCA UNTUK MENDETEKSI KOPI ROBUSTA SEBAGAI ADULTERAN DALAM SEDIAAN KOPI ARABIKA TORAJA KOMERSIAL
Bibliographic record
Abstract
Toraja arabica coffee is well known for its higher price and quality compared to robusta coffee, but its commercial is often added with other ingredients, including robusta coffee which has low economic value. Visual inspection is unreliable in roasted ground coffee due to the resemblance of its chemical content. The ATR-FTIR method tandem PCA was able to provide an overview of the typical chemical content of the coffee preparation. The purpose of this study was to evaluate the robusta coffee as adulterant in Toraja arabica coffee preparation by using ATR-FTIR. Toraja arabica coffee beans were obtained from three smallholder plantations around Toraja and Robusta coffee beans were obtained from Toraja, Lampung, and West Java coffee plantations. The coffee beans were roasted and then macerated using 96% ethanol for 3×24 hours and concentrated using a rotary evaporator until being thick. The IR spectrum of each extract was measured using the ATR-FTIR spectroscopy at a range of 4000-650 cm-1. The results show there is a similarity in the IR spectrum patterns and there is only a small difference in the transmittance of Toraja arabica coffee and robusta coffee. Furthermore, the IR spectrum is clustered by using PCA in R program. The projection of three commercial samples shows that samples 1 and 2 do not contain robusta coffee while sample 3 shows the presence of robusta coffee. In conclusion, the ATR-FTIR spectroscopic method tandem PCA was able to clustered the presence or absence of robusta coffee content in the Toraja arabica coffee.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".