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

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

Significance of the Topic
Fourier transform near-infrared (FT-NIR) spectroscopy provides a fast, non-destructive approach for confirming identity and qualifying raw materials in pharmaceutical, chemical, and polymer manufacturing. Implementation reduces analyst time, eliminates solvents and associated hazards, improves throughput in receiving operations, and supports regulatory compliance. When extended from simple identity testing to material qualification, FT-NIR can prevent production of out-of-specification product and deliver substantial cost savings.

Objectives and Overview of the Application Note
This application note describes practical principles and a 12-step program for planning, building, validating, implementing, and maintaining FT-NIR spectral libraries for raw material identification and qualification. The document illustrates these principles using a small example library and provides guidance on sampling, data acquisition, chemometric modeling, validation, and long-term library management.

Methodology
Data acquisition and model development are presented as a staged workflow: inventory and prioritization of raw materials; assessment of potential spectral conflicts; selection of sampling modes; definition of data-collection parameters; creation of chemometric models; validation with positive and negative challenge samples; and establishment of SOPs and maintenance procedures. Key methodological considerations include:

• Number of representative lots: identity methods may require 3–5 lots; qualification typically requires ≥20 lots per material to capture variance.
• Sampling strategy: choose the appropriate sampling mode (diffuse reflectance, transmission, transflectance, or fiber-probe sampling) depending on sample type and location of testing (receiving area versus QC lab).
• Acquisition parameters: recommended co-averaged scans (minimum five, triplicate collections for qualification), resolution trade-offs (use 2–4 cm⁻¹ for difficult or qualification tasks, 8–16 cm⁻¹ otherwise).
• Spectral pre-treatments: optional use of Savitzky–Golay smoothing/derivatives, MSC, SNV with de-trending, and Norris smoothing when needed to improve discrimination.

Used Instrumentation

• Thermo Scientific Antaris FT-NIR Analyzer (example work used Antaris MDS; Antaris II mentioned as a higher-performance model).
• Sampling accessories: SabIR fiber-optic probe for remote sampling of liquids/solids, Integrating Sphere for diffuse reflectance of powders and solids, transmission compartments/modules for liquids, semi-solids, and transparent solids; optional solid transmission module for films.
• Software: RESULT Software (21 CFR Part 11 compliance) for acquisition and instrument qualification; ValPro system qualification package (validation wheel with NIST-traceable standards) for DQ/IQ/OQ/PQ; TQ Analyst for chemometric modeling and method building.

Data Collection and Chemometric Modeling
All spectra in the example were collected in diffuse reflectance using the integrating sphere with samples in 2-dram vials, 16 cm⁻¹ resolution, and five co-averaged scans. TQ Analyst supports several qualitative classification algorithms:

• QC Compare (1-nearest-neighbor correlation) for straightforward identity work.
• Distance Match (DM) using class mean ± standard deviation for more challenging distinctions (e.g., particle-size related differences).
• Principal component-based methods accessed via Discriminant Analysis: Discriminant Analysis (DA) using Mahalanobis distance and SIMCA (Soft Independent Modeling of Class Analogies) for complex libraries and qualification tasks.

Main Results and Discussion (Example Library)
An example identification library of ten common materials (six sugars: D-glucose, D-fructose, D-mannitol, D-sorbitol, sucrose, α-D-lactose monohydrate; plus acetylsalicylic acid, acetaminophen, L-ascorbic acid, and citric acid) demonstrated clear spectral uniqueness across FT-NIR. Using QC Compare on the average library, no conflicts were observed visually or algorithmically. Validation employed positive challenge samples (duplicate lots of glucose, fructose, acetaminophen) and negative challenge samples chosen for realistic confusion potential (salicylic acid vs. acetylsalicylic acid; 2-acetamidophenol vs. acetaminophen; α-D-lactose anhydrous vs. α-D-lactose monohydrate). Typical pass threshold used was a match score ≥90. Example validation results: glucose 99.9, fructose 98.0, acetaminophen 100.0 (positive matches); salicylic acid 55.2, 2-acetamidophenol 19.3, α-D-lactose anhydrous 68.9 (negative samples correctly not matched). These results show robust discrimination for identity confirmation with the chosen parameters and algorithms.

Benefits and Practical Applications

• Rapid identity testing at receiving or in-line, enabling faster release of materials and reduced quarantine.
• Reduced laboratory workload, solvent use, and operator exposure compared with wet-chemical methods.
• Support for GMP and regulatory expectations through documented qualification (DQ/IQ/OQ/PQ) workflows and 21 CFR Part 11–capable software.
• Material qualification capability to detect lot-to-lot variability and prevent processing of unsuitable raw materials.

Library Management, SOPs, and Maintenance
Effective long-term performance requires formal SOPs covering instrument qualification and maintenance, remedial actions, routine method performance, sample handling, failure investigation, library updates, and data archival. Libraries must be monitored and periodically updated to account for drift in raw material characteristics; replace outdated reference lots and revalidate changes with positive and negative challenge samples. For large inventories, staged implementation and prioritization reduce time-to-value while preserving validation rigor at each expansion step.

Future Trends and Potential Uses

• Wider adoption of FT-NIR and integrated workflows in supply-chain and vendor auditing to improve upstream quality assurance.
• Increasing use of qualification models (SIMCA, multivariate approaches) for in-process acceptance criteria and predictive quality checks.
• Greater automation and remote sampling (fiber probes, inline modules) enabling real-time monitoring in production environments.
• Improved instrument speed and stability (next-generation FT-NIR hardware and cloud-enabled management) to simplify periodic requalification and support distributed library deployment.

Conclusion
FT-NIR spectroscopy, when implemented with a systematic library development program, validated workflows, and robust maintenance practices, is an efficient and reliable approach for raw material identification and qualification. Proper selection of sampling modes, data-collection parameters, and chemometric algorithms—combined with rigorous validation using positive and negative challenge samples—yields reproducible, regulatory-compliant methods that reduce operational cost and risk.

References
Jeffrey Hirsch. A guide to raw material analysis using Fourier transform near-infrared spectroscopy. Thermo Fisher Scientific application note. AN50785_E 0522. 2022.