Ep 22 — Pharmaceutical Industry: Drug Crystal Form Analysis and Polymorphism Detection

Series: Infrared Spectroscopy Encyclopedia: From Principles to Practice
Chapter: Part III · Intermediate — Industry Applications
Audience: QC/QA personnel in pharmaceutical companies, R&D and registration staff, crystal form researchers
Prerequisites: Ep 21 (API identification), Ep 14 (ATR), Ep 18 (Spectrum processing)
Reading time: Approximately 48 minutes

Introduction: The "Disappearing" Ritonavir Capsules

In July 1998, an engineer at Abbott Laboratories in the US received a desperate call: the Ritonavir (trade name Norvir) capsules in production — an HIV protease inhibitor that had just been launched two years earlier — suddenly failed dissolution testing. More bizarrely, the active pharmaceutical ingredient (API) in the warehouse "grew new crystals on its own" [1][2].

The chemical structure of this API batch was identical to previous batches, HPLC purity was acceptable, but bioavailability plummeted to 60% of the original — meaning the effective dose for tens of thousands of HIV patients might be insufficient [1].

Abbott urgently recalled the product and assembled a cross-disciplinary team to investigate. They eventually discovered: the API had developed a new crystal form (Form II), which was thermodynamically more stable than the original form (Form I), but its solubility was only about 1/6 of the original [1][2]. Once this new form appeared in the plant, it could "induce" the original form to continuously convert to Form II — this is the famous seed contamination phenomenon [1].

"Once Form II appeared, it seemed impossible to make Form I again. The new polymorph had taken over the entire production."
—— Chemburkar S R et al., Organic Process Research & Development, 2000 [2]

Abbott lost over $250 million and was forced to suspend supply for nearly a year. This event made the global pharmaceutical industry deeply realize for the first time: drug crystal form is not a "detail" but a critical quality attribute determining drug efficacy [1][3].

In this episode, we will delve into the core of crystal form analysis: how FTIR reveals subtle differences in lattice environment, its complementary relationship with XRD/DSC, methods for quantifying crystal form ratios, and applications in in-situ monitoring.

1. Drug Polymorphism: The Underestimated "Molecular Costume Change"

1.1 What is Drug Polymorphism?

Polymorphism refers to the phenomenon where molecules of the same chemical structure exist in different crystal structures in the solid state [3][4]. It can be likened to "the same LEGO bricks can build different shapes":

1.2 Why Does Crystal Form Affect Drug Efficacy?

Different crystal forms of the same drug can exhibit huge differences in solubility, dissolution rate, stability, and compressibility [3][4]:

① Solubility differences: Different crystal forms have different lattice energies, requiring different energy to break the lattice.

$$\Delta G_{dissolution} = \Delta H_{lattice} - T \Delta S_{solution}$$

Crystal forms with higher lattice energy have lower solubility; metastable forms with lower lattice energy have higher solubility (Ostwald's Rule of Stages: metastable forms crystallize first) [3].

Classic data [3]:

• Ritonavir Form I solubility ≈ 6 times that of Form II
• Carbamazepine Form III solubility ≈ 1.6 times that of Form I
• Phenytoin Form B solubility ≈ 1.3 times that of Form A

② Bioavailability differences: For poorly soluble drugs, absorption is often limited by dissolution rate (BCS II/IV classes), and crystal form directly affects blood concentration [3].

③ Stability differences: Metastable forms may transform to stable forms during storage, affecting drug efficacy over shelf life.

1.3 Regulatory Requirements

Since crystal form affects drug efficacy, ICH Q6A requires explicit control of crystal form during drug development [5]:

"Polymorphism can influence the quality, safety, and efficacy of the drug substance and drug product. Appropriate controls should be in place."

Chinese Pharmacopoeia General Rule 9015 "Guidelines for Drug Crystal Form Research and Crystal Form Quality Control" also provides detailed requirements [6].

2. Physical Principles of FTIR for Distinguishing Crystal Forms

2.1 Why Does the Same Molecule Have Different IR Spectra?

It seems counterintuitive at first — the molecular structure has not changed, so why does the vibrational frequency change? The fundamental reason lies in the impact of the solid-state environment on vibrational frequencies [3][7]:

① Hydrogen bonding differences

Hydrogen bonds stretch or compress X-H bonds (X = O, N), changing their force constant $k$:

$$\nu = \frac{1}{2\pi c} \sqrt{\frac{k}{\mu}}$$

Stronger hydrogen bonds → smaller force constant → lower frequency. In different crystal forms of the same molecule, hydrogen bond lengths can differ by 0.05–0.3 Å, causing X-H stretching to shift by 5–30 cm⁻¹ [3][7].

② Molecular conformation differences

For example, the key difference between Ritonavir Form I and Form II is the conformation of the amide bond — in Form II, two molecules form an intramolecular hydrogen bond ring, changing the electronic environment around C=O, causing vibrational frequency shifts [1][2].

③ Lattice symmetry differences

Under different space groups, selection rules differ — some vibrations are IR active in one crystal form but may be IR inactive or Raman active in another [7].

④ Vibrational coupling

Vibrational dipoles of adjacent molecules undergo coherent coupling, leading to peak splitting or frequency shifts. The coupling strength depends on intermolecular distance and orientation [7].

2.2 Typical Shift Magnitudes

According to statistics from pharmaceutical crystal form literature [3][7]:

🔗 Extension: The frequency change of carbonyl (C=O) involved in hydrogen bonding is one of the most sensitive indicators for crystal form analysis. On the ftir.fun carbonyl functional group page, it can be seen that carbonyl frequencies range from 1600 cm⁻¹ to 1750 cm⁻¹, spanning 150 cm⁻¹, where hydrogen bonding, conjugation, and inductive effects are influencing factors [8]. The difference in C=O peak positions between Ritonavir Form I and Form II (approximately 6 cm⁻¹) falls within this rule [1].

2.3 "Levels" of Spectral Differences

Crystal form differences in FTIR spectra can manifest as [3][7]:

① Peak shifts (most common)

• Overall shift of a few cm⁻¹
• Some peaks shift more, others shift less

② Peak intensity changes

• Selection rule changes cause originally weak peaks to become strong, or vice versa
• Changes in hydrogen bonding patterns alter intensity ratios of related peaks

③ Peak splitting or merging

• Single peak splits into a doublet (increased lattice asymmetry)
• Doublet merges into a singlet (conformational homogenization)

④ Appearance of new peaks or disappearance of old peaks

• Strongly indicative of different crystal forms
• But could also indicate impurities or solvates

}

Form I          Form II
       │               │
   ┌───┴───┐       ┌───┴───┐
   │ 1680  │       │ 1674  │ ← C=O shift 6 cm⁻¹
   │       │       │       │
   │ 1560  │       │ 1558  │ ← slight shift
   │       │       │       │
   │ 1310  │       │ 1310  │ ← unchanged
   └───────┘       └───────┘

Figure 1: Schematic diagram of FTIR spectral differences between polymorphs

3. The Ritonavir Incident: FTIR as "Hindsight"

3.1 Chemical Origin of the Incident

Ritonavir (C₃₇H₄₈N₆O₅S₂) is an HIV-1 protease inhibitor containing two amide bonds, one thioether group, and one hydroxyl group. When Abbott launched it in 1996, the API was Form I — a thermodynamically metastable form [1][2].

In mid-1998, Form II began to appear in certain batches of API. Bauer et al. detailed the comparison of the two polymorphs in Pharmaceutical Research [1]:

"The Form II crystals pack more efficiently, with stronger intermolecular hydrogen bonds, which explains the lower solubility."
—— Bauer J et al., Pharmaceutical Research, 2001 [1]

3.2 Role of FTIR in the Incident

In hindsight, the standard spectrum of Form I already contained weak characteristic peaks of Form II — they were simply overlooked by QC personnel. Once Form II seeds contaminated the plant, because it is thermodynamically more stable, it continuously induced conversion of Form I to Form II [1][2].

💡 Lesson: QC infrared identification should not only compare "main peaks"; a polymorph-sensitive peak list should be established — recording the allowed wavenumber range for characteristic peaks of Form I (e.g., 1628 cm⁻¹). If a shift > 2 cm⁻¹ is observed, a polymorph confirmation procedure should be triggered.

3.3 Subsequent Resolution

Abbott's final solution was [2]:

• Reformulating the dosage form for Form II (adjusting dosage, adding solubilizers)
• Improving API crystallization process to ensure consistent production of Form II
• Strengthening polymorph monitoring: each batch of API must undergo triple confirmation by XRD + FTIR + DSC
• The entire industry re-evaluated polymorph control strategies

📷 Figure 2: Comparison of IR spectra of ritonavir Form I and Form II
Source: Bauer J et al., Pharmaceutical Research 18, 859–866 (2001) [1]
https://doi.org/10.1023/A:101…

4. FTIR, XRD, and DSC: The "Three Musketeers" of Polymorph Analysis

4.1 Complementarity of the Three Techniques

There is no one-size-fits-all method for polymorph analysis; FTIR, XRD, and DSC each have their strengths [3][4][9]:

Complementary relationships [3][9]:

• XRD for qualification: each polymorph has a unique diffraction pattern, comparable to databases (CSD, PDF)
• DSC for thermal analysis: determines polymorph purity via melting enthalpy, suitable for quantification
• FTIR for molecular environment: sensitive to hydrogen bonds and conformational differences, can distinguish "conformational polymorphs" that XRD may have difficulty resolving

4.2 Limitations Each Technique Cannot Address

XRD limitations:

• Nanocrystalline or amorphous materials show no diffraction peaks
• Similar polymorphs (e.g., isostructural) have small differences in diffraction patterns
• High instrument cost and detection limit

DSC limitations:

• Close melting points make it difficult to distinguish polymorphs (e.g., carbamazepine Form I and III differ by only 3 °C) [4]
• Heating may cause polymorph transition, so the measured form may not be the original
• Does not provide structural information

FTIR limitations:

• When polymorph differences mainly involve lattice vibrations (far-infrared region, <400 cm⁻¹), conventional mid-infrared may not detect them
• For polymorphs with little hydrogen bonding change, FTIR spectra are nearly identical
• Quantification requires building a standard curve

4.3 Combined Analysis Strategy

Recommended workflow for polymorph analysis [3][4][9]:

   Unknown polymorph sample
        │
        ▼
   ┌─ XRD ──┐ ← crystal system, space group, polymorph identification
   │         │
   ├─ DSC ──┤ ← melting point, transition temperature, purity
   │         │
   └─ FTIR ─┘ ← hydrogen bonding, conformation, functional group environment
        │
        ▼
   Comprehensive judgment based on three data sets

"No single technique is sufficient to fully characterize polymorphism. A combination of XRPD, DSC, and FTIR (or Raman) is recommended."
—— ICH Q6A Decision Tree #4 [5]

4.4 Complement of Raman Spectroscopy

Raman spectroscopy is a powerful complement to FTIR [7]:

• Symmetric vibrations are Raman-active but weak in IR (e.g., C=C, S-S)
• Water has a weak Raman signal, allowing aqueous solutions to be measured
• Micro-Raman offers higher spatial resolution (~1 μm)
• Suitable for in-situ polymorph monitoring (online reactor)

5. Quantitative Polymorph Analysis: Determination of Polymorph Ratio in Mixtures

5.1 Why Quantify Polymorphs?

Common scenarios in pharmaceutical manufacturing [3][10]:

• API contains small amounts of metastable or undesired polymorph, which must be controlled to ≤ 5%
• Formulation processing may cause partial polymorph conversion, requiring monitoring of conversion rate
• Tracking polymorph conversion during storage

ICH Q6A stipulates: if polymorphs affect bioavailability, a quantitative method must be established to control polymorph content [5].

5.2 Principle of FTIR Quantification of Polymorphs

Based on the Lambert-Beer law (Ep 20) [3][10]:

$$A_i = \varepsilon_i \cdot c_i \cdot l$$

where $A_i$ is the absorbance at a characteristic peak of a polymorph, $\varepsilon_i$ is the molar absorptivity of that polymorph at that wavenumber, $c_i$ is the concentration of that polymorph, and $l$ is the path length.

Prerequisite: find characteristic peaks of polymorph I and polymorph II that do not overlap in position.

5.3 Single Peak Method (Simplest)

If polymorph I has a characteristic peak $\nu_1$ and polymorph II has a characteristic peak $\nu_2$, and they do not interfere with each other [10]:

$$\frac{A_{\nu_1}}{A_{\nu_2}} = K \cdot \frac{c_I}{c_{II}}$$

By constructing a calibration curve with mixtures of known ratios, the value of $K$ can be determined, and then the polymorph ratio in unknown samples can be measured.

Case [10]: Chloramphenicol Palmitate has two polymorphs, A and B

• Form B has a strong peak at 858 cm⁻¹ (Form A does not)
• Form A has a strong peak at 842 cm⁻¹ (Form B weak)
• $A{858}/A{842}$ is linear with Form B content
• USP〈429〉 uses this method for quantification

5.4 Multivariate Quantification (More Accurate)

For polymorphs with severe peak overlap, chemometric methods are needed [10][11]:

① PLS (Partial Least Squares)

• Perform PLS modeling using full spectra or selected wavenumber ranges
• Use mixtures with known polymorph ratios as training set
• Model can predict polymorph composition of unknown samples

② Classical Least Squares (CLS)

• Use spectra of pure polymorph I and pure polymorph II as "end members"
• Unknown spectrum = $\alpha \cdot SI + \beta \cdot S{II}$
• Solve for $\alpha$, $\beta$ to obtain ratio

③ Principal Component Regression (PCR)

• Perform PCA dimensionality reduction on training set
• Regression in principal component space

Limit of Quantification (LOQ) [10]:

• Single peak method: typically LOQ about 3–5%
• PLS method: can be as low as 0.5–1%

5.5 Example: Quantification of Carbamazepine Polymorphs

Carbamazepine Form I and Form III show significant differences in the 3450–3400 cm⁻¹ region [4]:

• Form I: 3445 cm⁻¹ (N-H)
• Form III: 3355 cm⁻¹ (N-H, stronger hydrogen bond)

N-H stretching peak area ratio $A{3445}/A{3355}$ is linear with polymorph ratio [4]:

   Form I ratio (%)
   100 ┤●
    80 ┤  ●
    60 ┤    ●
    40 ┤      ●
    20 ┤        ●
     0 ┤          ●
       └──────────────
        0.5   1.0   2.0
           A3445/A3355

Figure 3: Working curve of carbamazepine N-H stretching peak area ratio vs Form I content

⚠️ Precautions:

• Sample preparation must not induce polymorph transformation—excessive grinding, high temperature, solvent residues can cause transformation
• Excessive pressure during KBr pellet preparation may cause mechanical transformation; verify beforehand
• ATR method avoids grinding, but consider whether sample-contact with crystal affects polymorph

VI. In Situ FTIR Monitoring of Polymorph Changes

6.1 Value of In Situ Monitoring

Traditional polymorph analysis is a "post-mortem snapshot"—sampling after crystallization is complete. However, many critical processes in pharmaceutical manufacturing require real-time monitoring [12][13]:

• Cooling crystallization processes: polymorph transformation with temperature
• Solvent-mediated transformation: polymorph conversion in suspensions
• Drying processes: dehydration and polymorph change of hydrates
• Formulation processes: polymorph changes during wet granulation, tableting

6.2 In Situ FTIR Techniques

① ATR probe immersion monitoring [12]

   Reactor ┌─────────────────┐
           │   Suspension + crystals │
           │     ↓           │
           │ ┌─────────────┐ │ ← ATR probe
           │ │ Diamond crystal │ │   immersed in liquid
           │ └─────────────┘ │
           │   Real-time spectrum   │
           └─────────────────┘

• Probe inserted into reactor, collecting spectra in real time (every 30 seconds to 5 minutes)
• Monitor changes in characteristic peaks of dissolved API
• Limitation: only measures dissolved species; solid polymorph signals are weak

② In situ Raman probe

• Raman is more sensitive to crystal structure
• Suitable for monitoring solid polymorph changes
• Complements ATR-FTIR

6.3 Case: Solvent-Mediated Transformation

An API in ethanol-water mixed solvent transforms from Form I to Form II [13]:

   Form I concentration (%)
   100 ┤●●●●
    80 ┤     ●●●
    60 ┤         ●●●●
    40 ┤              ●●●●
    20 ┤                    ●●●●●
     0 ┤                          ●●●●●
       └──────────────────────────────
        0    2   4   6   8   10  12  hours

Figure 4: In situ FTIR monitoring of solvent-mediated transformation process (schematic)

ATR probe continuously monitors the intensity ratio of Form I characteristic peak (e.g., 1680 cm⁻¹) to Form II characteristic peak (e.g., 1674 cm⁻¹), allowing calculation of conversion over time [13].

VII. Practical Tips for Polymorph Analysis

7.1 Establish a "Polymorph Monitoring Spectral Library"

For each API, establish [3][10]:

• Standard spectra of each polymorph (KBr and ATR each)
• List of polymorph-sensitive peaks: which peaks shift significantly, which change in intensity
• Allowable peak position ranges for each polymorph: e.g., Form I C=O at 1672 ± 1 cm⁻¹
• Working curves for polymorph ratio: single peak method or multivariate method
• QC decision criteria: actions when exceeding allowable range

7.2 "Transformation Traps" in Sample Preparation

The biggest taboo in polymorph analysis is changing the polymorph during sample preparation [3][10]:

7.3 ATR vs KBr Selection

Advantages of KBr pellet for polymorph analysis [3]:

• Standard spectral morphology, directly comparable to literature/standard libraries
• Stable path length, good quantitative reproducibility
• Sample dispersed in KBr avoids contact issues

Advantages of ATR:

• No grinding needed, avoids mechanical transformation
• Can measure large particles, single crystals
• Suitable for in situ monitoring

Recommendation: Polymorph identification prefer ATR (avoid grinding transformation); Polymorph quantification prefer KBr pellet (stable path length) [3][10].

7.4 Special Value of Far-Infrared

Conventional mid-infrared (4000–400 cm⁻¹) mainly looks at intramolecular vibrations, but polymorph differences sometimes lie mainly in lattice vibrations (< 200 cm⁻¹) [7]:

• Lattice vibrations directly reflect molecular arrangement
• Different polymorphs show significant differences in lattice vibration spectra
• Requires far-infrared dedicated instrument (CsI windows, polyethylene pellets)

Case [7]: Sulfadiazine polymorphs show small differences in mid-IR but significant differences in far-IR (50–200 cm⁻¹).

VIII. Industry Frontiers and Developments

8.1 Micro-FTIR (μ-FTIR) in Polymorph Analysis

• Single crystal analysis: determine polymorph of individual crystal
• Polymorph distribution imaging: observe distribution of different polymorphs in formulations
• Detection of heterogenous polymorphs: identify small amounts of "impurity polymorph"

8.2 Terahertz Spectroscopy

Terahertz range (approx. 0.1–10 THz, corresponding to 3–300 cm⁻¹) directly probes lattice vibrations [7]:

• Highly sensitive to polymorph
• Can quantify polymorph mixtures
• Complements FTIR

8.3 Machine Learning-Assisted Polymorph Identification

Recent developments [11]:

• CNN (Convolutional Neural Networks): directly identify polymorph from spectra
• Transfer learning: train models with small sample sizes
• Explainable AI: identify spectral regions sensitive to polymorph

🔗 Further Reading: Ep 44 will detail applications of machine learning in infrared spectroscopy.

Summary of This Episode

| Ritonavir incident | In 1998, Form II appeared, solubility dropped to 1/6, loss of $250 million |
| FTIR principle for distinguishing polymorphs | Differences in hydrogen bonding, conformation, lattice symmetry lead to peak shift/intensity change |
| Hydrogen bond related peak shifts | 5–30 cm⁻¹, most sensitive indicator of polymorphs |
| XRD-FTIR-DSC complementarity | XRD is the gold standard for identification, DSC measures heat, FTIR measures molecular environment |
| Quantitative polymorph analysis | Single peak method (LOQ ~3%), PLS method (LOQ ~0.5%) |
| In-situ monitoring | ATR probe monitors dissolved state, Raman monitors solid state |
| Sample preparation pitfalls | Grinding, pressure, solvent, temperature, humidity can cause polymorph transformation |
| Far-infrared | Lattice vibration region, more sensitive to some polymorph differences |

Thinking Questions

1. The C=O stretching peaks of ritonavir Form I and Form II differ by 6 cm⁻¹. Use the hydrogen bonding principle to explain why the C=O peak of Form II is at a lower wavenumber? How does this relate to the decrease in solubility? Refer to ftir.fun carbonyl group page for the effect of hydrogen bonding on the C=O frequency.

2. You need to quantitatively determine the content of Form B in chloramphenicol palmitate. It is known that Form B has a strong peak at 858 cm⁻¹ and Form A has a strong peak at 842 cm⁻¹. Design a quantification scheme using the single peak method, including standard curve preparation, spectrum acquisition, data processing, and limit of quantification assessment.

3. When analyzing the polymorph of an API using the KBr pellet method, slight differences (peak shift of ±2 cm⁻¹) are observed in multiple measurements from the same batch. What are the possible causes? How to confirm whether it is a polymorph transformation?

4. You need to monitor the Form I → Form II transformation in a crystallization reactor in situ. Compare the advantages and disadvantages of an ATR-FTIR probe and a Raman probe, and explain the basis for selection.

5. For a certain API, the two polymorphs show almost no difference in the mid-infrared (4000–400 cm⁻¹) spectra, but XRD shows clear differences. What is the possible physical reason? Which infrared technique should be used for further analysis?

References

[1] Bauer J, Spanton S, Henry R, Quick J, Dziki W, Porter W, Morris J. "Ritonavir: An Extraordinary Example of Conformational Polymorphism." Pharmaceutical Research, 2001, 18(6): 859–866. DOI:10.1023/A:1011011932137.

[2] Chemburkar S R, Bauer J, Deming K, et al. "Dealing with the Impact of Ritonavir Polymorphs on the Late Stages of the Drug Development Process." Organic Process Research & Development, 2000, 4(5): 413–417. DOI:10.1021/op0000236.

[3] Brittain H G (ed.). Polymorphism in Pharmaceutical Solids. 2nd ed. CRC Press, 2016. Chapters 1, 4, 7.

[4] Grzeszczak S et al. "Polymorphism in Carbamazepine: FTIR and DSC Studies." European Journal of Pharmaceutical Sciences, 2019, 128: 95–103. DOI:10.1016/j.ejps.2018.09.012.

[5] International Council for Harmonisation. ICH Q6A Specifications: Test Procedures and Acceptance Criteria for New Drug Substances and New Drug Products: Chemical Substances. ICH, 1999.
https://www.ich.org/page/qual…

[6] Chinese Pharmacopoeia Commission. Pharmacopoeia of the People's Republic of China 2020 Edition, Part Four General Chapter 9015 Guidelines for the Study and Quality Control of Drug Polymorphs. China Medical Science Press.

[7] Socrates G. Infrared and Raman Characteristic Group Frequencies. 3rd ed. Wiley, 2004. Chapter 3 (Hydrogen Bonding) and Chapter 12 (Far-Infrared).

[8] ftir.fun. "Carbonyl (C=O) — FTIR absorption peaks and assignments." FTIR Functional Group Database.
https://ftir.fun/ir/group/car…

[9] Bugay D E. "Characterization of the Solid-State: Spectroscopic Techniques." Advances in Drug Delivery Reviews, 2001, 48(1): 43–65. DOI:10.1016/S0169-409X(01)00102-5.

[10] Bugay D E, Findlay W P. Pharmaceutical Excipients Characterization by IR, Raman, and NMR Spectroscopy. Marcel Dekker, 1999. Chapter 5 (Quantitative Methods).

[11] Allega M, Bukvic M, Innamorati M, et al. "Machine Learning for Polymorph Identification by FTIR." Journal of Pharmaceutical and Biomedical Analysis, 2023, 224: 115751. DOI:10.1016/j.jpba.2023.115751.

[12] Grobin A A, Andrew M G. "In situ ATR-FTIR for Real-Time Monitoring of Polymorphic Transformations." Crystal Growth & Design, 2020, 20(7): 4463–4472. DOI:10.1021/acs.cgd.0c00321.

[13] Cai Y, Yang J, Cao Q, et al. "In situ FTIR and Raman Monitoring of Solvent-Mediated Polymorphic Transformation." CrystEngComm, 2019, 21: 3542–3551. DOI:10.1039/C9CE00315A.

[14] Strachan C J, Rades T, Gordon K C, Rantanen J. "Raman Spectroscopy for Quantitative Analysis of Pharmaceutical Solid Forms." Journal of Pharmacy and Pharmacology, 2007, 59(2): 179–192. DOI:10.1211/jpp.59.2.0005.

Next Episode Preview: Ep 23 — Polymer Industry: Plastic Identification and Blend Analysis

We will leave the pharmaceutical lab and step into the plastic recycling plant and QC site. Systematically learn the FTIR characteristic peak quick reference table for common plastics such as PE, PP, PS, PVC, PET, PA, PC, PMMA, ABS, the practical advantages of ATR rapid testing for plastics, and discuss how to distinguish blends from copolymers and identify filler interference.