Raw-Material Authentication Using a Handheld Raman Spectrometer - Pharmaceutical Technology

Latest Issue

Latest Issue
PharmTech Europe

Raw-Material Authentication Using a Handheld Raman Spectrometer
Using a handheld Raman spectrometer, the authors developed methods for 28 commonly used excipients and active ingredients.

Pharmaceutical Technology
Volume 3, Issue 32


Table I: TruScan instrument specifications.
Handheld Raman material identity verification systems were used in this work (TruScan, Ahura Scientific, Wilmington, MA). Six units manufactured at different times from different runs of parts were used in this study (details provided in subsequent paragraphs in this article). This instrumentation uses a 785-nm NIR external cavity-stabilized, cooled laser as a light source. The laser has a maximum power consumption of 1 W, a maximum output of 400 mW, and typically operates at 300 mW output. A single-dispersive spectrometer, cooled CCD, and dielectric notch filters for Rayleigh rejection make up the rest of the instrument hardware. The device also has an integrated barcode reader for capturing sample lot–batch–other identifiers and selecting methods. Detailed specifications are listed in Table I.

Materials tested
Three TruScan devices (hereafter referred to as the "test devices") were tested with each of the 32 common pharmaceutical materials listed in the sidebar "Materials tested" (Sigma-Aldrich, St. Louis, MO) to evaluate the applicability of the systems for incoming inspection.

To create methods, reference spectra first had to be acquired. The reference spectra for the materials were taken on three TruScan devices (hereafter referred to as "reference devices") using identical data collection software as the test devices described above. In the context of TruScan, methods are analytical tasks based on stored reference spectra that the instrument's software executes to determine whether a material's identity can be verified. For each material, a single reference spectrum was acquired by one of the three reference devices.

To collect each reference spectrum, a sample of the material was placed in a borosilicate glass vial (VWR, West Chester, PA) and the data were collected through the wall of the vial. The laser aperture of the instrument was placed at the appropriate distance from the vial using either the nose-cone or vial-holder attachment for correct spacing. For most materials, the acquisition process was initiated and simply allowed to continue until terminated automatically by the unit's library acquisition software. In contrast, cellulose, dextrin, trimagnesium phosphate, zinc sulfate, and calcium sulfate are slightly fluorescent, so special care had to be taken to avoid photobleaching the sample during reference measurement. This can be achieved by terminating the reference scan before the software automatically discontinues measurement or by periodically moving the sample throughout data collection. Finally, while reference spectra were collected, it was determined that colloidal silica, talc, sodium carboxy methyl cellulose, and hydroxy propyl methyl cellulose did not provide an adequate signal to be measured in a practical period of time for handheld deployment. In particular, colloidal silica had a very weak Raman signal, and the other three materials were too fluorescent to allow reliable determinations to be made.

Methods were created from the reference spectra by the associated web-based software utility and then loaded into the test instruments. To prepare test samples to challenge the methods, samples of approximately 2 g each of the 28 remaining materials were sealed in 2-m-thick polyethylene bags in an effort to emulate expected-use scenarios (measurement through plastic bags) for incoming material inspection. Three measurements of each sample were made, one measurement with each of the three test devices. Each test measurement was made using the automatic (or "auto") mode, where the unit's software controls data acquisition parameters to achieve the necessary spectral signal-to-noise ratio (SNR) in the shortest measurement time possible. The auto-mode measurements used when running a method compensate for differences in operator positioning, stray-light, lot-to-lot sample fluorescence, and so forth. Each "unknown" measured spectrum (three for each material) was evaluated against each method for the test materials using the probability based approach described previously, resulting in a p-value for each unknown-method pair.


blog comments powered by Disqus
LCGC E-mail Newsletters

Subscribe: Click to learn more about the newsletter
| Weekly
| Monthly
| Weekly

FDASIA was signed into law two years ago. Where has the most progress been made in implementation?
Reducing drug shortages
Breakthrough designations
Protecting the supply chain
Expedited reviews of drug submissions
More stakeholder involvement
Reducing drug shortages
Breakthrough designations
Protecting the supply chain
Expedited reviews of drug submissions
More stakeholder involvement
View Results
Eric Langerr Outsourcing Outlook Eric LangerRelationship-building at Top of Mind for Clients
Cynthia Challener, PhD Ingredients Insider Cynthia ChallenerRisk Reduction Top Driver for Biopharmaceutical Raw Material Development
Jill Wechsler Regulatory Watch Jill Wechsler Changes and Challenges for Generic Drugs
Faiz Kermaini Industry Insider Faiz KermainiNo Signs of a Slowdown in Mergers
From Generics to Supergenerics
CMOs and the Track-and-Trace Race: Are You Engaged Yet?
Ebola Outbreak Raises Ethical Issues
Better Comms Means a Fitter Future for Pharma, Part 2: Realizing the Benefits of Unified Communications
Better Comms Means a Fitter Future for Pharma, Part 1: Challenges and Changes
Source: Pharmaceutical Technology,
Click here