PENENTUAN LOGAM BESI DAN SENG TOTAL DALAM PRODUK PERIKANAN MENGGUNAKAN FLAME ATOMIC ABSORPTION SPECTROMETRY DAN PENGUKURAN NILAI KETIDAKPASTIANNYA
Bibliographic record
Abstract
Besi (Fe) dan Seng (Zn) merupakan unsur yang berguna bagi manusia. Keberadaan logam Fe dan Zn dalam produk perikanan yang cukup kecil (trace), mudah tekontaminasi oleh kondisi lingkungan, dan metoda preparasinya yang komplek menyebabkan penentuan logam Fe dan Zn ini cukup sulit, sehingga perlu dicari suatu metoda uji yang valid dan akurat. Dalam penelitian ini dilakukan pengembangan metoda standar American of Analytical Chemistry (AOAC) tahun 2005 no. 999.10 dengan menggunakan bahan acuan bersertifikat DORM 3 (Fish Protein Certified Reference Material for Trace Metal) dari National Research Council of Canada (NRCC) untuk menguji keakuratan dan ketertelusuran hasil ke Standard Internasional (SI). Metoda ini sudah divalidasi berdasarkan parameter-parameter kimia analitik. Hasil penelitian menunjukkan rata-rata kadar Fe dan Zn dalam sampel perikanan sebesar 178 ± 14 mg.Kg-1 dan 59,8 ± 6,6 mg.Kg-1 (berat kering) dengan faktor cakupan 2 dan tingkat kepercayaan 95%, yang berada pada rentang yang ditentukan 183,5 ± 4,3 mg Kg-1 and 60 ± 1,1 mg Kg-1.Kata kunci : Fe, Zn, trace, CRM, perikanan Iron (Fe) and Zink (Zn) are essential elements for human being. Determination of this elements in fish products is quite difficult because of Fe and Zn content in trace level, easy to be contaminated by the environmental conditions, and, complex preparation methods so that it is needed to find a good and accurate method. In this paper, we have developed a standard method from American of Analytical Chemistry (AOAC), 2005, no. 999. using DORM 3 as Certified Reference Materials (CRMs) to check accuracy and traceability’s results to Standard International (SI). The method has been validated according to analytical parameters. The results showed that means of Fe and Zn concentration in the investigated fish product were 178 ± 14 mg.Kg-1 and 59.8 ± 6,6 mg.Kg-1 respectively with coverage factor 2 and 95% level of confident, and in range of expected mass were 183,5 ± 4,3 mg.Kg-1 and 60 ± 1,1 mg.Kg-1in dry basis.Keywords : Fe, Zn, trace, CRM, fisher
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".