Home > Research > Publications & Outputs > Using Fourier transform IR spectroscopy to anal...
View graph of relations

Using Fourier transform IR spectroscopy to analyze biological materials

Research output: Contribution to journalJournal article


  • Matthew J. Baker
  • Paul Bassan
  • Rohit Bhargava
  • Konrad M. Dorling
  • Simon W. Fogarty
  • Caryn Hughes
  • Peter Lasch
  • Ganesh D. Sockalingum
  • Josep Sulé-Suso
  • Rebecca J. Strong
  • Michael J. Walsh
  • Bayden R. Wood
  • Peter Gardner
<mark>Journal publication date</mark>08/2014
<mark>Journal</mark>Nature Protocols
Issue number8
Number of pages21
Pages (from-to)1771-1791
Early online date3/07/14
<mark>Original language</mark>English


IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.