How can you identify milk from FTIR?
This page summarizes the recurring FTIR evidence reported for milk, including the most frequent peaks, supporting functional groups, and literature-backed interpretation patterns. It is a structured evidence page, not a claim of automatic single-spectrum certainty.
Backed by 35 cited sources
Quick answer
milk is usually reported with a recurring pattern of peaks and functional-group evidence. The most useful approach is to cross-check at least two characteristic peaks before treating it as a match, then verify whether the full spectrum still fits the same material family.
Peak interpretation
Possible materials / groups
| Functional group | Evidence |
|---|---|
| Alkyl C-H | 11 |
| Hydroxyl (O-H) | 8 |
| Amide | 7 |
| Protein | 6 |
| Carboxyl (COOH) | 5 |
| Methacrylate | 4 |
| Acetate | 4 |
| Ester | 4 |
Spectrum logic
The logic here is evidence aggregation: repeated literature mentions of milk, repeated peak positions, and repeated functional-group associations. A strong material hypothesis should still be supported by multiple peaks that agree with each other, not by one headline band alone.
Real-world usage
This page is designed for polymer identification, incoming-material QC, unknown plastic analysis, recycled-content review, and literature-backed interpretation of reference spectra.
Common mistakes
- Calling a material match too early because one famous peak is present.
- Ignoring sample prep, fillers, oxidation, water, or additives that can change the apparent pattern.
- Using literature evidence without checking whether your own sampling mode and spectrum quality are comparable.
Verification advice
Use DSC, GC-MS, or TGA to validate the material hypothesis when the peak pattern is ambiguous or mixed.
Literature behind this page
-
confidence 0.9
milk
Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms DOI: 10.3390/foods10051111 -
confidence 0.8
milk
Adulteration detection in milk using infrared spectroscopy combined with two-dimensional correlation analysis DOI: 10.1117/12.841580 -
confidence 0.8
milk
Bahadi 等 - 2021 - Fourier Transform Infrared Spectroscopy as a Tool DOI: 10.3390/foods -
confidence 0.8
milk
Eid 等 - 2022 - Identification of milk quality and adulteration by DOI: 10.1007/s00604-022-05393-4 -
confidence 0.8
milk
Chemometric analysis combined with FTIR spectroscopy of milk and Halloumi cheese samples according to species’ origin DOI: 10.1002/fsn3.1603 -
confidence 0.7
milk
Caprine CSN1S1 haplotype effect on gene expression and milk composition measured by Fourier transform infrared spectroscopy DOI: 10.3168/jds.2009-2854 -
confidence 0.7
milk
Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography–based milk fatty acid profiles DOI: 10.3168/jds.2017-13052 -
confidence 0.6
milk
A Study of the Interactions of Heavy Metals in Dairy Matrices Using Fourier Transform Infrared Spectroscopy, Chemometric, and In Silico Analysis DOI: 10.3390/foods12091919 -
confidence 0.5
milk
Application of genetic algorithm and multivariate methods for the detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy DOI: 10.1002/jsfa.10894 -
confidence 0.5
milk
Toledo-Alvarado 等 - 2021 - Association between days open and milk spectral da DOI: 10.3168/jds.2020-19031
Upload your FTIR spectrum
Get AI-assisted polymer analysis and peak-by-peak interpretation from your own spectrum.