Ep 26 — Food Industry: Food Packaging Migration and Microplastic Detection

Series: Infrared Spectroscopy Encyclopedia: From Principles to Practice
Chapter: Part 3 · Intermediate — Industry Applications
Target Audience: Food testing technicians, environmental chemistry researchers, quality control engineers
Prerequisites: Ep 14 (ATR Attenuated Total Reflection), Ep 19 (Library Search), Ep 20 (Quantitative Analysis)
Reading Time: Approximately 45 minutes

Introduction: The "Invisible Guests" in a Takeout Meal

In 2022, China's annual food delivery orders exceeded 20 billion [1]. Every hot bowl of sour-spicy noodles, malatang, or curry rice is almost always packed in PP (polypropylene) or PS (polystyrene) disposable containers. How many "invisible guests" do these containers release into the food at 80–100 °C, under acidic or oily conditions?

These "guests" mainly fall into two categories [1][2]:

• Chemical migrants: Monomers, additives, and oligomers from packaging materials leaching into food
• Microplastics (MPs): Plastic particles < 5 mm generated by abrasion and degradation of the inner walls of plastic containers

"Microplastics are ubiquitous in the environment and have been detected in food, water, and even human blood."
—— Leslie et al. Environment International 2022 on the detection of microplastics in human blood [3]

Infrared spectroscopy, especially μ-FTIR (micro-FTIR) and FPA-FTIR (focal plane array FTIR) imaging, has become a crucial tool for microplastic identification—it provides the polymer chemical identity of each particle, not just particle size and count [1][2][4].

In this episode, we will delve into the analysis of food packaging migrants and systematically explain mainstream infrared methods for microplastic detection, incorporating recent verifiable cases to build a comprehensive practical capability.

1. FTIR Analysis of Food Packaging Material Migrants

1.1 Migration Phenomena and Regulatory Background

When food packaging comes into contact with food, chemical substances in the packaging material can migrate into the food through diffusion, dissolution, degradation, etc. [5]. EU Regulation (EU) No 10/2011 specifies requirements such as specific migration limits (SML) for plastic food contact materials; China's corresponding system is distributed across GB 4806 series (general/safety standards for food contact materials), GB 31604 (migration test methods), and GB 9685-2016 (additive use standards, including some SML/QM), and it is not appropriate to simply equate GB 9685 with the full text of EU plastics regulations [5].

Main migrant types [5][6]:

Table 1: Main food packaging materials and their migrant infrared characteristics

1.2 FTIR Detection Workflow for Migration Testing

Migration tests simulate actual usage conditions [5][6]:

① Simulant Selection (GB 31604.1-2015) [5]:

• Aqueous foods (pH > 4.5) → 10% ethanol
• Acidic foods (pH ≤ 4.5) → 3% acetic acid
• Alcoholic foods → 20% ethanol
• Fatty foods → isooctane or 95% ethanol

② Migration Conditions:

• Time/temperature simulate actual contact (e.g., 70 °C / 2 h for hot filling)

③ FTIR Analysis:

• Volatile migrants: Headspace injection + gas cell transmission
• Semi-volatiles: Extraction concentration followed by ATR measurement
• Oligomers/particulates: Membrane filtration enrichment followed by μ-FTIR analysis

🔗 Extension: For detailed frequency ranges (2960–2850 cm⁻¹) of polymer alkyl C-H stretching vibrations, see ftir.fun alkyl C-H functional group page. This is a key window for identifying polyolefin packaging materials such as PP and PE.

1.3 Example: Detection of Antioxidant Migration from PP Containers

Polypropylene (PP) commonly uses Irganox 1010 (pentaerythritol tetrakis(3,5-di-tert-butyl-4-hydroxyhydrocinnamate)) as an antioxidant. When in contact with fatty foods at high temperatures, Irganox 1010 migrates into the food [6].

FTIR Detection Method [6]:

1. Extract the migration solution with isooctane
2. Concentrate the extract and drop onto a KBr disc
3. Measure by transmission FTIR
4. Detect characteristic peaks at 3640 cm⁻¹ (O-H stretch) and 1740 cm⁻¹ (C=O stretch)
5. Quantification: Establish a calibration curve using the peak area at 1740 cm⁻¹

  Migration test: PP container + simulant (isooctane)
                ↓ 70°C, 2h
  Extract concentration
                ↓
  KBr smear → Transmission FTIR → Quantification at 3640/1740 cm⁻¹

2. Overview of Microplastic Detection Methods

2.1 Definition and Hazards of Microplastics

Microplastics (MPs) are defined as plastic particles with a particle size < 5 mm [2][3][4]. By origin, they are classified as:

• Primary microplastics: Small particles manufactured directly (e.g., cosmetic microbeads)
• Secondary microplastics: Fragments from degradation and breakage of larger plastics

In 2022, Leslie et al. reported the detection and quantification of plastic particles in human blood [3]; subsequently, microplastics have been reported in lung tissue, placenta, and other samples. Research on their health risks is ongoing, including hypotheses related to inflammation, oxidative stress, and endocrine disruption, and should be interpreted cautiously.

2.2 Comparison of Microplastic Detection Technologies

Table 2: Comparison of mainstream microplastic detection techniques (Data sources: Zhou et al. [1]; Cordeiro et al. [2])

2.3 μ-FTIR Micro-Infrared Method

μ-FTIR combines an infrared spectrometer with an optical microscope to achieve chemical analysis of micro-areas (10–100 μm) [1][4].

① Sampling and Pretreatment [1][2]:

• Water samples: Filtration → Digestion (H₂O₂ to remove organic matter) → Membrane enrichment
• Food samples: Digestion (Fenton's reagent) → Density separation (NaCl or ZnCl₂) → Membrane
• Sediments: Density separation → Digestion → Membrane

② Membrane Selection (Critical!) [1][2][4]:

• Silver membrane (Ag membrane): Pore size 0.45 μm, good IR transmission, low background interference — preferred for μ-FTIR transmission mode
• Aluminum oxide membrane (Anodisc): Pore size 0.2 μm, good transmission but fragile
• PC polycarbonate membrane: Suitable for optical observation, but has IR absorption itself
• Glass fiber membrane: Not suitable for infrared analysis

⚠️ Note: Filter membrane selection is key to successful microplastic analysis. Incorrect filters (e.g., nylon, cellulose acetate) can introduce polymer interference, leading to false positives [1][2].

③ Transmission vs Reflection vs ATR mode [4]:

• Transmission mode: Particles on silver membrane, IR light passes through particles and filter—preferred, best spectral quality
• Reflection mode: Particles on reflective substrate—suitable for large particles, but with distortion
• ATR mode: Microscope ATR objective contacts particles—high resolution, but may move particles

④ Imaging parameters [1]:

• Aperture size: ~10–20 μm (near diffraction limit)
• Spectral range: 4000–1000 cm⁻¹ (avoid filter absorption below 1000 cm⁻¹)
• Resolution: 4–8 cm⁻¹
• Scan number: background 64, sample 32–128
• Step scan: one point every 10–20 μm

  μ-FTIR transmission imaging workflow:

  Water sample/Food → Digestion → Filtration onto silver membrane
                          ↓
              Microscope scan (point-by-point transmission)
                          ↓
              FPA detector / MCT single point
                          ↓
              Each pixel = one IR spectrum
                          ↓
              Automatic library search → Polymer identification

📷 Figure 1: Principle of μ-FTIR transmission imaging and microplastic identification workflow
Source: Agilent μ-FTIR application note [4]
https://www.agilent.com/en/pr…

2.4 FPA Focal Plane Array Imaging: High-Throughput Whole-Filter Analysis

FPA (Focal Plane Array) detector is a high-throughput upgrade for μ-FTIR [1][7].

Principle: FPA is a 2D array detector (e.g., 64×64, 128×128 pixels), each pixel simultaneously acquires a full IR spectrum, eliminating point-by-point mechanical scanning [1][7].

MCT single-point scanning vs FPA imaging [1][7]:

Table 3: MCT single-point vs FPA array imaging comparison

Bruker Hyperion 3000 + 64×64 FPA is a typical configuration for whole-filter microplastic analysis [1][7]:

• Field of view: ~170×170 μm per frame (5.5 μm/pixel)
• Whole filter (47 mm): ~280,000 frames, automatic stitching
• Data cube: x × y × wavenumber → chemical imaging

"FPA-FTIR allows for the analysis of whole filters in a few hours, identifying thousands of microplastic particles automatically."
—— Zhou X et al., Molecules 2022 [1]

2.5 Quantification Methods: Particle Counting and Polymer Type Identification

The core of quantitative microplastic analysis is chemical identification of each particle [1][2][4]:

① Particle identification:

• Chemical imaging → threshold segmentation (e.g., 2920 cm⁻¹ C-H peak intensity) → identify particle boundaries
• Exclusion: size < 10 μm (detection limit), irregularly shaped non-plastic substances

② Polymer identification:

• Average spectrum of each particle → library search matching (e.g., SiMPle, OpenSpecy) [1][2]
• Matching threshold: HQI > 0.7 (Pearson correlation coefficient) considered positive

③ Quantitative report [1][2]:

• Total particle count / volume or area (e.g., particles/L, particles/m²)
• Classification by polymer type (PE, PP, PS, PET, PA, PVC, etc.)
• Particle size distribution
• Morphology classification (fiber, fragment, film, spheroid)

🔗 Extension: The identification of all polyolefins (PE, PP) is based on alkyl C-H stretching vibrations at 2960–2850 cm⁻¹, see ftir.fun alkyl C-H functional group page. The aromatic ring features of PS (3025, 1600, 1493, 696 cm⁻¹) and ester features of PET (1715, 1240 cm⁻¹) are key to distinguishing different polymers.

III. Case Study 1: FPA-FTIR Detection of Microplastics from Takeout Containers

3.1 Background

Zhou X et al., Molecules 2022, 27(9):2646 [1]

This study is the first systematic large-scale survey of microplastic contamination in takeout food containers in China. The research team collected 30 takeout container samples (PP, PS, PET, paper-plastic composites) from 6 cities and quantified microplastic release using FPA-FTIR whole-filter analysis.

3.2 Experimental Methods

① Sample preparation [1]:

• Rinse container inner walls with deionized water
• Digest water samples with 30% H₂O₂ for 48 h (remove organic matter)
• Filter onto silver membrane (0.45 μm pore size)

② FPA-FTIR imaging [1]:

• Instrument: Bruker Hyperion 3000 + 64×64 FPA
• Mode: Transmission
• Spectral range: 3600–1000 cm⁻¹
• Resolution: 8 cm⁻¹
• Whole-filter imaging time: ~2.5 hours/sample

③ Data analysis [1]:

• Automatic particle identification using siMPle software
• Matching with self-built polymer reference library (HQI > 0.7)
• Report: particles/container, polymer type, particle size distribution

3.3 Key Results

Table 4: Key results of FPA-FTIR detection of microplastics from takeout containers (data source: Zhou et al. [1])

3.4 Methodological Insights

This study established a standardized workflow for FPA-FTIR whole-filter analysis [1]:

• Silver membrane is the best filter choice for transmission μ-FTIR
• FPA imaging reduced analysis time from tens of hours to a few hours
• Automatic library search enables high-throughput polymer identification
• HQI threshold of 0.7 balances sensitivity and false positive rate

📷 Figure 2: Example of FPA-FTIR whole-filter chemical imaging and microplastic identification
Source: Zhou X et al., Molecules 2022, 27(9):2646 [1]
https://www.mdpi.com/1420-304…

IV. Case Study 2: μ-FTIR Validation of Microplastics in Drinking Water

4.1 Background

Cordeiro RDM et al., Environ Sci Pollut Res 2025, 32(28):16823 [2]

This study is a method validation for microplastic monitoring in Portuguese drinking water, focusing on verifying the accuracy, repeatability, and recovery of the μ-FTIR method for low-concentration water samples (< 10 particles/L).

4.2 Validation Parameters

The study followed Eurachem validation guidelines and systematically evaluated the following parameters [2]:

Table 5: μ-FTIR drinking water microplastic method validation results (Data source: Cordeiro et al. [2])

4.3 Key Findings

① Filter membrane selection validation [2]:

• Comparison of silver membrane vs Anodisc vs PC membrane
• Silver membrane: cleanest background, highest recovery (95%), recommended
• Anodisc: fragile, recovery 80%
• PC membrane: self-polymer interference, not recommended

② Lower size limit [2]:

• Detection reliability decreases for particles < 10 μm
• Recommend reporting microplastics ≥ 10 μm

③ Real sample detection [2]:

• Tap water from 5 Portuguese cities: detection rate 80%, average 4.2 particles/L
• Main polymers: PE, PP, PET (consistent with water pipes and bottled water contact)

📷 Figure 3: μ-FTIR methodology validation workflow and spike recovery experiment
Source: Cordeiro RDM et al., Environ Sci Pollut Res 2025 [2]
https://link.springer.com/art…

V. Case Study 3: Detection of Microplastics in Carbonated Beverages by LDIR

5.1 Research Background

Wang Y, Wang Y. Heliyon 2024, 10(12):e32805 [8]

This study employed LDIR (Laser Direct Infrared) technology—a novel microplastic analysis method based on quantum cascade lasers (QCL)—to detect microplastics in carbonated beverages from 15 brands across 5 Chinese cities.

5.2 Introduction to LDIR Technology

Differences between LDIR and conventional FTIR [8]:

Table 6: Comparison of μ-FTIR vs LDIR technologies

The core advantages of LDIR are speed and resolution—the high brightness of QCL allows detection of smaller particles (< 10 μm), and analysis speed is an order of magnitude faster than FPA-FTIR [8].

5.3 Key Results

The study by Wang & Wang [8] found:

Table 7: LDIR detection results of microplastics in carbonated beverages (Data source: Wang & Wang [8])

5.4 Comparison of Three Case Studies

Table 8: Horizontal comparison of three microplastic detection case studies

💡 Key insight: PET dominates in carbonated beverages, consistent with PET bottle packaging; PP/PS dominate in food containers, consistent with container material—microplastic polymer types often "trace back" to the packaging material itself. This is the unique value of FTIR chemical fingerprinting in source tracing analysis.

VI. Practical Experience in Microplastic Detection

6.1 Contamination Prevention Measures

The biggest challenge in microplastic analysis is background contamination—laboratory air, reagents, and operator clothing can introduce plastic particles [1][2]:

① Laboratory cleanliness [1][2]:

• Dedicated cleanroom (ISO 7 or higher)
• Cotton lab coat (avoid synthetic fiber clothing)
• Operate in fume hood
• Wipe work surfaces regularly (ultrapure water + lint-free cloth)

② Reagent blanks [1][2]:

• Include 3 reagent blanks per batch
• Blank value must be < 10% of sample value
• Otherwise, trace the contamination source

③ Equipment selection [1][2]:

• Glassware primarily (avoid plastic beakers, graduated cylinders)
• Rinse glassware with ultrapure water 3 times before use
• Avoid plastic pipette tips (use glass pipettes)

6.2 Spectrum Interpretation Pitfalls

Pitfall 1: Cellulose mistaken for plastic [1][2]

• Cellulose (paper, cotton fibers) has a strong C-O peak at 1030 cm⁻¹
• Similar to some polyester features
• Differentiation: cellulose lacks strong alkyl C-H peaks at 2920/2850 cm⁻¹

Pitfall 2: Protein interference [1]

• Incomplete digestion of food may leave protein residues
• Amide I/II bands (1650/1550 cm⁻¹) interfere with PA (nylon) identification
• Solution: extend H₂O₂ digestion time

Pitfall 3: Poor spectrum quality for small particles [8]

• SNR decreases for particles < 20 μm
• Peak distortion near diffraction limit
• Recommendation: report results for ≥ 10 μm; < 10 μm for reference only

6.3 Open-Source Analysis Tools

① siMPle [1][2]:

• Open-source tool designed for μ-FTIR/FPA microplastic data
• Automated particle identification + polymer library matching
• Supports Bruker OPUS data format

② OpenSpecy [9]:

• Online microplastic spectral analysis platform
• Built-in polymer reference library
• Supports user data upload
• GitHub: https://github.com/wincowgerD…

🔗 Further reading: The chemical basis of microplastic polymer identification lies in characteristic functional groups of various polymers—alkyl C-H (PE, PP), aromatic rings (PS), ester groups (PET), amides (PA), etc. It is recommended to systematically study the infrared fingerprints of various polymers in conjunction with ftir.fun alkyl C-H functional group page, ester group page, and amide page.

Summary of This Episode

| LDIR | QCL laser direct infrared, fast, resolution ~1 μm |
| Filter selection | Silver membrane preferred (clean background, high recovery); PC membrane prohibited |
| Quantification method | Particle counting + polymer library matching (HQI > 0.7) |
| Contamination prevention | Cotton clothing, glassware, reagent blanks, clean room |
| Zhou 2022 case | Takeaway containers FPA-FTIR, 100% detection, mainly PP |
| Cordeiro 2025 case | Drinking water μ-FTIR method validation, LOD 3 particles/L |
| Wang 2024 case | Carbonated beverages LDIR, 93% detection, mainly PET |
| Source tracing value | Microplastic polymer type "traced" to packaging material |

Review Questions

1. A takeaway container sample was analyzed by FPA-FTIR and found a large number of particles with strong absorption at 2920/2850 cm⁻¹. Infer the possible polymer type and provide the basis.
2. Why is silver membrane recommended over polycarbonate (PC) membrane as filter for microplastic analysis? Explain from an infrared spectroscopy perspective.
3. PET microplastics account for 72% in carbonated beverages, while PE/PP/PET are all distributed in drinking water. Analyze the cause of this difference from the perspective of packaging materials.
4. In μ-FTIR method validation, the spiked recovery rate is 85–105%. Discuss the main factors that may affect recovery.
5. The data volume of FPA-FTIR whole filter analysis can reach GB level. Design a data management plan including storage, retrieval, and long-term archiving.
6. In microplastic spectrum interpretation, how to distinguish cellulose fibers (paper residues) from polyester fibers? List key discriminating peaks.
7. A laboratory found high microplastic blank values (5 particles/filter). Design a troubleshooting process to locate the contamination source.

References

Case Studies

[1] Zhou X, Wang J, Ren J. "Analysis of Microplastics in Takeaway Food Containers in China Using FPA-FTIR Whole Filter Analysis." Molecules, 2022, 27(9):2646. DOI:10.3390/molecules27092646.
https://www.mdpi.com/1420-304…

[2] Cordeiro RDM et al. "Validation of an FT-IR Microscopy Method for the Monitorization of Microplastics in Water for Human Consumption in Portugal." Environmental Science and Pollution Research, 2025, 32(28):16823–16844. DOI:10.1007/s11356-024-33966-8.
https://link.springer.com/art…

[8] Wang Y, Wang Y. "Assessing Microplastic Contamination in Soda Beverages." Heliyon, 2024, 10(12):e32805. DOI:10.1016/j.heliyon.2024.e32805.
https://www.sciencedirect.com…

Reviews & Methodology

[3] Leslie HA et al. "Discovery and Quantification of Plastic Particle Pollution in Human Blood." Environment International, 2022, 163:107199. DOI:10.1016/j.envint.2022.107199.

[4] Agilent Technologies. "8700 LDIR Laser Direct Infrared Analyzer for Microplastics Analysis." Application Note, 2023.
https://www.agilent.com/en/pr…

[5] National Health and Family Planning Commission of the People's Republic of China. GB 31604.1-2015 National Food Safety Standard General Rules for Migration Test of Food Contact Materials and Articles. China Standards Press, 2015.

[6] Peltzer MA et al. "Determination of Migration of Antioxidants from Polypropylene into Food Simulants." Packaging Technology and Science, 2018, 31(2): 71–80. DOI:10.1002/pts.2356.

[7] Primpke S et al. "Critical Evaluation of a Particle-Image-Recognition Tool Using FPA-Based Micro-FTIR Imaging for Microplastic Analysis." Analytical and Bioanalytical Chemistry, 2019, 411: 7129–7141. DOI:10.1007/s00216-019-02055-2.

Open Source Tools

[9] OpenSpecy. "Open-Source Spectral Analysis Platform for Microplastics." GitHub Repository.
https://github.com/wincowgerD…

Database Resources

[ftir.fun] ftir.fun Infrared Spectroscopy Database. Alkyl C-H functional group page:
https://ftir.fun/ir/group/alk…

Next Episode Preview: Ep 27 — Environmental Monitoring: Detection of Pollutants in Water and Soil
We will move from food to the environment, explaining FTIR applications in water and soil monitoring — extraction and enrichment detection of organic pollutants in water, characteristic peaks of soil humus for assessment, portable FTIR on-site monitoring, and experience in eliminating matrix effects in environmental samples.

This article is licensed under CC BY-NC-SA 4.0. Images come from the public domain or web resources with attributed sources; copyright belongs to the original authors.