A guide to raw material analysis using Fourier transform near-infrared spectroscopy

Guide to Raw Material Analysis Using Fourier Transform Near-Infrared Spectroscopy — Summary

Significance of the topic
FT-NIR spectroscopy is a rapid, non-destructive analytical approach widely adopted for raw material identification and qualification across pharmaceutical, polymer, and chemical industries. Its practical importance lies in streamlining incoming-material control, reducing laboratory workload and solvent use, improving operator safety, increasing throughput by shortening quarantine times, and supporting regulatory compliance and zero-defect manufacturing initiatives.

Objectives and overview of the application note

• Describe practical principles for planning, developing, implementing and maintaining FT-NIR spectral libraries for raw material identity confirmation and qualification.
• Provide a stepwise program for establishing a robust raw material library and associated operating procedures.
• Illustrate the approach with an example library and validation challenges using Thermo Scientific Antaris FT-NIR instrumentation and associated software.

Methodology and workflow (12-step program)
The documented workflow emphasizes planning and program design before laboratory work. Key steps are summarized below:

1. Logistics — Define how FT-NIR testing will be used (identity only vs. qualification), testing location (receiving vs. QC lab), throughput needs, security and shift operation.
2. List raw materials — Compile a comprehensive inventory of materials to include in the library; scope can range from tens to thousands depending on industry.
3. Prioritize implementation — For large inventories, stage library development to realize benefits early and reduce project length.
4. Identify potential conflicts — Flag chemically similar or grade-dependent materials (e.g., different lactose grades, fatty acids, counterions, similar oils) that may require special handling or more samples.
5. Decide on qualification needs — Identification typically requires 3–5 lots; qualification demands many more (20+ lots) to represent parametric variance and to enable discrimination between acceptable and unacceptable lots.
6. Choose sampling techniques — Use appropriate sampling hardware (reflectance, transmission, transflectance, fiber probe) rather than forcing one technique for all materials.
7. Set data collection parameters — Typical guidance: 5 co-averaged scans as a minimum, 2/4/8/16 cm^-1 resolution depending on conflict potential; higher resolution (2–4 cm^-1) and longer acquisition for qualification.
8. Collect data and reference analyses — Gather representative lots and perform orthogonal reference testing where applicable to support qualification models.
9. Create chemometric models — Build qualitative classification models using algorithms appropriate to complexity (QC Compare/1‑NN, Distance Match, Discriminant Analysis (DA), SIMCA).
10. Validate the library — Use positive (unseen lots of library materials) and negative challenge samples (materials not in the library but likely to be confused) to test specificity and failure modes. Typical pass threshold for QC Compare is a match score ≥ 90.
11. Establish SOPs — Write SOPs for instrument qualification/maintenance, remedial actions, sample processing, failure handling, library updates, and data archival.
12. Plan library updates and maintenance — Monitor spectral drift, retire outdated samples, add representative new lots as suppliers or grades change, and revalidate after updates.

Instrumentation used

• Thermo Scientific Antaris FT-NIR Analyzer (Antaris II as an upgraded option)
• Antaris MDS Method Development Sampling System
• SabIR fiber optic probe for remote sampling of liquids and solids
• Integrating Sphere module for diffuse reflectance of solids/semi-solids
• Transmission modules for liquids/transparent solids and optional solid transmission accessory
• Thermo Scientific RESULT Software (21 CFR Part 11 compliance) and ValPro System Qualification Software (validation wheel with NIST-traceable standards)
• Thermo Scientific TQ Analyst chemometric software (QC Compare, Distance Match, DA, SIMCA)

Data collection recommendations and chemometrics

• Resolution and scans: Use 2–4 cm^-1 and one-minute acquisition for qualification; 8–16 cm^-1 may suffice for simple ID tasks. Trade-offs exist between resolution and analysis time.
• Sample replication: Duplicate/replicate measurements recommended; triplicate and multiple lots for qualification (20+ lots per class advised).
• Algorithms: QC Compare (1‑NN) for straightforward libraries; Distance Match for grade/particle-size differences; Discriminant Analysis and SIMCA (PCA-based) for complex libraries and qualification.

Main results and discussion (example library and validation)
An illustrative example library of ten common pharmaceutical materials (six sugars, acetylsalicylic acid, acetaminophen, ascorbic acid, citric acid) was constructed using diffuse reflectance (integrating sphere). Untreated spectra for all ten materials were visually distinct. Validation used positive and negative challenge samples: duplicate lots of glucose, fructose, and acetaminophen (positive) and salicylic acid, 2-acetamidophenol, and α‑D‑lactose anhydrous (negative). Key outcomes from the challenge testing (QC Compare average model):

• Positive matches: Glucose 99.9, Fructose 98.0, Acetaminophen 100.0 (match scores)
• Negative matches: Salicylic acid 55.2, 2‑Acetamidophenol 19.3, α‑D‑lactose anhydrous 68.9 (all below typical pass threshold)
• Typical pass threshold: match score ≥ 90 indicates significant similarity.

These results demonstrate that properly planned FT-NIR libraries can reliably identify materials and, when expanded with sufficient lot-to-lot variability, support qualification tasks that detect unsuitable incoming material before production.

Benefits and practical applications

• Rapid identity confirmation at receiving to reduce quarantine time and improve throughput.
• Reduced labor and elimination of solvent-based wet chemistry, lowering cost and hazardous exposure.
• Support for supplier/vendor-specific libraries to ensure materials used in a given facility are correctly identified.
• Material qualification capability to detect out-of-spec raw materials and prevent costly production failures.
• Flexibility to deploy in receiving rooms, labs, or remote sampling contexts (fiber probe), and to handle diverse sample types without hardware changes.

Future trends and possibilities for use

• Increased automation and integration with laboratory information management systems (LIMS) and manufacturing execution systems (MES) for real-time lot control.
• Growth of in-line and at-line FT-NIR process analytical technology (PAT) for continuous quality assurance during manufacturing.
• Improved chemometric methods and machine learning approaches for more robust classification, drift compensation and anomaly detection.
• Cloud-shared and standardized spectral libraries enabling federated or supplier-validated authentication while protecting proprietary grade distinctions.
• Enhanced regulatory frameworks and guidance for validating spectral libraries and qualification models in regulated industries.

Conclusion
FT-NIR with carefully constructed and maintained spectral libraries is an effective tool for incoming raw material identification and, with sufficiently comprehensive datasets, for material qualification. Success depends primarily on upfront planning (material selection, conflict analysis, sampling strategy), appropriate instrument qualification, representative sampling and replication, robust chemometric modeling and validation, and ongoing library maintenance and SOP governance. When implemented correctly, FT-NIR programs deliver faster approvals, reduced costs, improved safety, and stronger supply-chain quality control.

References

• Hirsch J. Thermo Fisher Scientific. Application note: A guide to raw material analysis using Fourier transform near-infrared spectroscopy. AN50785_E. 2022.