Use matrix-appropriate historical batches that your own qualification process already accepts, including suitable protein, fat/oil or mixed-product reference sets.
Halal Origin Pre-screening · Guide
Pre-screening · FTIR.fun
Halal Origin Pre-screening — How to Use
Use this workflow when your laboratory already has qualified origin reference batches and wants an FTIR pre-screen before deciding whether a new material needs further origin investigation or confirmatory testing. It compares like with like; it does not certify halal status or identify species by itself.
The result is a comparative tendency or consistency signal backed by the spectral differences you can inspect.
Keep the reference cohort, batch context and comparison evidence together so QA, suppliers and customers can see why a sample was escalated.
What this workbench does
- Keeps a private, per-team reference library of FTIR spectra from qualified batches you trust.
- Compares a new incoming sample with the selected material-specific reference cohort and reports consistency or origin-related tendency with peak-level difference evidence.
- Supports protein-based, fat/oil-based and mixed-product matrix categories when you provide appropriate qualified references for the material being screened.
- Keeps the screening result and supporting comparison available for batch traceability and internal review.
What it does not do
- It does not issue halal certification or a legal determination.
- It does not treat a public-library identity match as proof of animal origin.
- It does not turn an FTIR similarity result into species identification.
- Critical origin findings should be confirmed with the validated method required by your laboratory, customer or certification procedure.
The workflow in four steps
1Create a reference cohortUpload qualified historical FTIR batches for one material and matrix.
2Record origin contextAdd the declared source and batch information needed to interpret the comparison wording.
3Screen an incoming sampleUpload the new FTIR file against the matching qualified reference cohort.
4Review before actingRead the tendency label together with peak differences, overlays and any available multivariate diagnostics.
Step 1 — Build the reference cohort
A reference cohort is a set of FTIR spectra from batches your normal qualification process already accepts for the same material. The incoming sample is compared with this cohort, not with an arbitrary public database result.
Use the material's normal instrument files
Common supported FTIR and numerical formats can be uploaded directly. Keep acquisition and sampling conditions as consistent as practical across the references and incoming samples because method differences can create apparent spectral differences.
Reference-set size
A very small reference set describes normal material variation poorly, so the workbench warns when fewer than 5 batches are available. Add representative qualified batches as they become available, but do not add a batch simply because it failed the current comparison.
Keep different materials separate
Create a separate reference cohort for each material or grade you intend to screen. Separate supplier-specific cohorts can also be useful when supplier variation is known to be substantial.
Matrix type
Choose Protein-based, Fat/oil-based or Mixed according to the material. This changes the literature reading guides displayed on the page; the comparison itself is not restricted to those literature windows.
Declared source information
Record the declared or qualified source for the reference batch where known. The report wording follows the reference context: a qualified non-porcine cohort can support wording such as “consistent with non-porcine references”, while a porcine positive-control cohort can support “porcine tendency” on a close match. Without appropriate source context, the report should remain a library-consistency statement.
Traceability fields
Supplier, batch number, certification-status notes, scans, resolution and ATR crystal can be retained with reference records so a later reviewer can understand which material and acquisition context produced the comparison.
Step 2 — Screen the incoming sample
- Select the qualified reference cohort that matches the incoming material and matrix.
- Enter the incoming batch and declared-source information needed for traceability; declared source is context, not an analytical result.
- Upload the incoming FTIR file, preferably under comparable acquisition and sampling conditions.
- Run Halal Origin Pre-screening and review the generated comparison rather than acting on the headline label alone.
Credits: each pre-screening consumes 10 credits on a free/trial account. VIP accounts are not charged.
Step 3 — Read the report
The tendency label
The headline label summarizes how the incoming spectrum compares with the selected reference cohort. Its wording depends on the origin context recorded for that cohort:
| Reference context | Sample vs references | Report wording |
|---|---|---|
| Qualified non-porcine references | Consistent | Consistent with non-porcine references |
| Partly consistent | Needs further confirmation | |
| Inconsistent | Non-porcine origin not confirmed by this pre-screen | |
| Porcine positive-control references | Consistent | Porcine tendency — confirm as required |
| Partly consistent | Needs further confirmation | |
| Inconsistent | Does not match the porcine control pattern | |
| No validated origin context | Consistent | Consistent with the reference library |
| Partly consistent | Needs further confirmation | |
| Inconsistent | Inconsistent with the reference library |
The active analysis mode
The default analysis mode changes with the size of the qualified reference cohort; deployments can configure different thresholds:
| Reference batches | Default mode | Evidence shown |
|---|---|---|
| 19 or fewer | Similarity | Spectral similarity measures and the main difference peaks |
| 20 – 49 | PCA | Similarity plus multivariate projection and distance diagnostics |
| 50 or more | SIMCA-style class model | PCA plus class-model residual and conformity diagnostics |
Literature discriminant regions
The literature panel lists spectral windows reported in published origin-screening studies for the selected matrix category. Treat them as reading guides that help prioritize review of relevant differences, not as universal species markers.
Amide A3600–3000 cm⁻¹N–H/O–H stretch; principal gelatin discriminant window (Hashim 2010).
Amide I1700–1600 cm⁻¹C=O stretch; porcine vs bovine/fish gelatin main peak (Sci Rep 2021).
Amide II / III1550–1240 cm⁻¹N–H bend + C–N stretch; species-discriminant envelope (Sci Rep 2021).
Porcine-gelatin signature1475–1470 cm⁻¹Dominant wavenumber reported for porcine gelatin by c-FACS (Sci Rep 2021); bovine 1444–1450, fish 1496–1500.
DA window2000–1501 cm⁻¹Standard ATR + discriminant-analysis window in animal-gelatin studies (Nazri 2024).
C–H stretch3030–2785 cm⁻¹Total fat C–H region; SIMCA discriminant region for lard in ghee (Int J Mol Sci 2020).
Ester C=O1786–1680 cm⁻¹Triglyceride ester carbonyl; SIMCA region separating lard from other fats (2020 review).
Triglyceride fingerprint1490–919 cm⁻¹SIMCA discriminant region (2020 review); lard C–O pair reported near 1117/1097.
Lard in VCO region3020–3000 · 1120–1000 cm⁻¹Combined region that quantified lard in virgin coconut oil by PLSR+DA, R²=0.999 (2020 review).
cis / trans markers3006 · 968 cm⁻¹cis =C–H near 3006 and trans/CH₂ rocking near 968 support fat-type separation.
Where these windows come from
The displayed windows are drawn from published studies and reviews on origin or adulteration screening for specific matrices. Their relevance depends on material type, sample preparation, acquisition conditions and the study population, so a reported literature band should not be treated as a standalone origin test for a different material.
The comparison uses the spectrum as a whole; the literature windows are contextual evidence for review.
Peak differences and charts
The main-difference table identifies spectral regions where the incoming sample departs most from the reference pattern. The overlay shows the incoming spectrum against the reference behaviour, and multivariate plots are shown when the active mode supports them. Use these together rather than treating one number or one peak as the entire decision.
AI interpretation
When available, the AI panel summarizes differences and suggests plausible follow-up questions. It is explanatory decision support only; verify chemical claims and do not use AI wording as the certification or species-identification result.
FAQ
- My sample came back “non-porcine origin not confirmed”. Does that mean it contains pork?
- No. It means this FTIR pre-screen did not show the expected consistency with your qualified non-porcine references. Differences can arise from supplier, formulation, moisture, processing, acquisition conditions or other batch variation. Investigate the cause and use an appropriate validated species-identification or confirmatory method when origin must be established.
- Can I screen oils and fats with this?
- Yes, when you have an appropriate qualified fat/oil reference cohort for the material being screened. Choose the fat/oil matrix so the page shows relevant literature reading guides, then interpret those guides in the context of your own reference data.
- Why does the page warn when there are fewer than 5 reference batches?
- A handful of spectra cannot describe the normal variation of a material very well. The comparison can still be useful as directional evidence, but the result should be interpreted cautiously until the reference cohort better represents qualified production variation.
- Is my spectral data shared?
- No. Reference libraries are private to your account and are not used as testing references for anyone else. You can remove batches or whole libraries at any time.
- Do I need one reference cohort per supplier or per material?
- Keep different materials or grades separate. Supplier-specific cohorts may also be useful when supplier-related variation is large enough to affect the comparison.
- How is this different from similarity search in a large public spectral library?
- A public-library search asks which reference spectra an unknown resembles. This pre-screen asks whether an incoming batch behaves like the qualified origin reference cohort selected for that material, while retaining the batch and comparison evidence behind the result.
- Can I turn a screened sample into a reference batch?
- Yes, after the batch has passed your normal qualification process. Do not promote it merely because the pre-screening result was favourable; reference status should follow your independent qualification decision.
- What does the certification-status field mean outside Malaysia?
- Use it to record the certification body or status relevant to your own organization, or leave it unknown when that information is not available. It is a traceability field, not a determination made by FTIR.fun.