Using Fourier transform IR spectroscopy to analyze biological materials


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Typical biological spectrum showing biomolecular peak assignments from 3,000–800 cm−1, where ν = stretching vibrations, δ= bending vibrations, s = symmetric vibrations and as = asymmetric vibrations.
Figure 2: The instrumentation underlying the main forms of IR spectroscopic sampling.
Figure 3: FTIR spectroscopy work flow for imaging and diagnosis.
Figure 4: Visual effect of different pre-processing steps on a set of FTIR spectra.
Figure 5: Classification rates (% classification ± s.d.) of all possible combinations between three different pre-processing, three different feature extraction and two different supervised classifier options.
Figure 6: IR image reconstruction of a section of human colon mucosa.


  1. 1

    Bellisola, G. & Sorio, C. Infrared spectroscopy and microscopy in cancer research and diagnosis. Am. J. Cancer Res. 2, 1–21 (2012).

  2. 2

    Diem, M., Romeo, M., Boydston-White, S., Miljkovic, M. & Matthaus, C. A decade of vibrational micro-spectroscopy of human cells and tissue (1994–2004). Analyst 129, 880–885 (2004).

  3. 3

    Griffiths, P. & De Haseth, J.A. Fourier Transform Infrared Spectrometry 2nd edn. (John Wiley & Sons, 2007).

  4. 4

    Walsh, M.J. et al. FTIR microspectroscopy coupled with two-class discrimination segregates markers responsible for inter-and intra-category variance in exfoliative cervical cytology. Biomark. Insights 3, 179–189 (2008).

  5. 5

    Bhargava, R., Wall, B.G. & Koenig, J.L. Comparison of the FT-IR mapping and imaging techniques applied to polymeric systems. Appl. Spectrosc. 54, 470–479 (2000).

  6. 6

    Bhargava, R. Infrared spectroscopic imaging: the next generation. Appl. Spectrosc. 66, 1091–1120 (2012).

  7. 7

    Colarusso, P. et al. Infrared spectroscopic imaging: from planetary to cellular systems. Appl. Spectrosc. 52, 106–120 (1998).

  8. 8

    German, M.J. et al. Infrared spectroscopy with multivariate analysis potentially facilitates the segregation of different types of prostate cell. Biophys. J. 90, 3783–3795 (2006).

  9. 9

    Walsh, M.J. et al. Fourier transform infrared microspectroscopy identifies symmetric PO2 modifications as a marker of the putative stem cell region of human intestinal crypts. Stem Cells 26, 108–118 (2008).

  10. 10

    Kelly, J.G. et al. Derivation of a subtype-specific biochemical signature of endometrial carcinoma using synchrotron-based Fourier-transform infrared microspectroscopy. Cancer Lett. 274, 208–217 (2009).

  11. 11

    Romeo, M.J. et al. in Vibrational Spectroscopy for Medical Diagnosis (eds. Diem, M., Lasch, P., & Chalmers, J.) (John Wiley & Sons, 2008).

  12. 12

    Diem, M. et al. Comparison of Fourier transform infrared (FTIR) spectra of individual cells acquired using synchrotron and conventional sources. Infrared Phys. Technol. 45, 331–338 (2004).

  13. 13

    Kole, M.R., Reddy, R.K., Schulmerich, M.V., Gelber, M.K. & Bhargava, R. Discrete frequency infrared microspectroscopy and imaging with a tunable quantum cascade laser. Anal. Chem. 84, 10366–10372 (2012).

  14. 14

    Bhargava, R. & Levin, I.W. Spectrochemical Analysis Using Infrared Multichannel Detectors (Wiley-Blackwell, 2005).

  15. 15

    Davis, B.J., Carney, P.S. & Bhargava, R. Theory of mid-infrared absorption microspectroscopy: II. Heterogeneous samples. Anal. Chem. 82, 3487–3499 (2010).

  16. 16

    Davis, B.J., Carney, P.S. & Bhargava, R. Theory of mid-infrared absorption microspectroscopy: I. Homogeneous samples. Anal. Chem. 82, 3474–3486 (2010).

  17. 17

    Filik, J., Frogley, M.D., Pijanka, J.K., Wehbe, K. & Cinque, G. Electric field standing wave artefacts in FTIR micro-spectroscopy of biological materials. Analyst 137, 853–861 (2012).

  18. 18

    Bassan, P., Sachdeva, A., Lee, J. & Gardner, P. Substrate contributions in micro ATR of thin samples: Implications for analysis of cells, tissue and biological fluids. Analyst 38, 4139–4146 (2013).

  19. 19

    Miljković, M., Bird, B., Lenau, K., Mazur, A.I. & Diem, M. Spectral cytopathology: new aspects of data collection, manipulation and confounding effects. Analyst 138, 3975–3982 (2013).

  20. 20

    Cao, J. et al. Fourier transform infrared microspectroscopy reveals that tissue culture conditions affect the macromolecular phenotype of human embryonic stem cells. Analyst 38, 4147–4160 (2013).

  21. 21

    Chan, K.L.A. & Kazarian, S.G. Aberration-free FTIR spectroscopic imaging of live cells in microfluidic devices. Analyst 138, 4040–4047 (2013).

  22. 22

    Kallenbach-Thieltges, A. et al. Immunohistochemistry, histopathology and infrared spectral histopathology of colon cancer tissue sections. J. Biophotonics 6, 88–100 (2013).

  23. 23

    Lasch, P., Haensch, W., Naumann, D. & Diem, M. Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis. Biochim. Biophys. Acta 1688, 176–186 (2004).

  24. 24

    Bassan, P. et al. Whole organ cross-section chemical imaging using label-free mega-mosaic FTIR microscopy. Analyst 138, 7066–7069 (2013).

  25. 25

    Nakamura, T. et al. Microspectroscopy of spectral biomarkers associated with human corneal stem cells. Mol. Vis. 16, 359–368 (2010).

  26. 26

    Hammiche, A., German, M.J., Hewitt, R., Pollock, H.M. & Martin, F.L. Monitoring cell cycle distributions in MCF-7 cells using near-field photothermal microspectroscopy. Biophys. J. 88, 3699–3706 (2005).

  27. 27

    Walsh, M.J. et al. Tracking the cell hierarchy in the human intestine using biochemical signatures derived by mid-infrared microspectroscopy. Stem Cell Res. 3, 15–27 (2009).

  28. 28

    Wood, B.R., Bambery, K.R., Evans, C.J., Quinn, M.A. & McNaughton, D. A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections. BMC Med. Imaging 6, 12 (2006).

  29. 29

    Bird, B. et al. Cytology by infrared micro-spectroscopy: Automatic distinction of cell types in urinary cytology. Vib. Spectrosc. 48, 101–106 (2008).

  30. 30

    Baker, M.J. et al. FTIR-based spectroscopic analysis in the identification of clinically aggressive prostate cancer. Br. J. Cancer 99, 1859–1866 (2008).

  31. 31

    Bird, B. et al. Infrared micro-spectral imaging: distinction of tissue types in axillary lymph node histology. BMC Clin. Pathol. 8, 8 (2008).

  32. 32

    Bird, B. et al. Detection of breast micro-metastases in axillary lymph nodes by infrared micro-spectral imaging. Analyst 134, 1067–1076 (2009).

  33. 33

    Naumann, D. et al. Cells and biofluids analyzed in aqueous environment by infrared spectroscopy. Biomed. Opt. 10, 609301 (2005).

  34. 34

    Walsh, M.J., Holton, S.E., Kajdacsy-Balla, A. & Bhargava, R. Attenuated total reflectance Fourier-transform infrared spectroscopic imaging for breast histopathology. Vib. Spectrosc. 60, 23–28 (2012).

  35. 35

    Ooi, G.J. et al. Fourier transform infrared imaging and small angle X-ray scattering as a combined biomolecular approach to diagnosis of breast cancer. Med. Phys. 35, 2151–2161 (2008).

  36. 36

    Nallala, J. et al. Infrared imaging as a cancer diagnostic tool: Introducing a new concept of spectral barcodes for identifying molecular changes in colon tumors. Cytometry Part A 83, 294–300 (2013).

  37. 37

    Bird, B. et al. Infrared spectral histopathology (SHP): a novel diagnostic tool for the accurate classification of lung cancer. Lab. Invest. 92, 1358–1373 (2012).

  38. 38

    Baker, M.J. et al. Investigating FTIR-based histopathology for the diagnosis of prostate cancer. J. Biophotonics 2, 104–113 (2009).

  39. 39

    Gazi, E. et al. A correlation of FTIR spectra derived from prostate cancer biopsies with Gleason grade and tumour stage. Eur. Urol. 50, 750–761 (2006).

  40. 40

    Walsh, M.J. et al. IR microspectroscopy: potential applications in cervical cancer screening. Cancer Lett. 246, 1–11 (2007).

  41. 41

    Gajjar, K. et al. Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer. Analyst 138, 3917–3926 (2013).

  42. 42

    Ollesch, J. et al. FTIR spectroscopy of biofluids revisited: an automated approach to spectral biomarker identification. Analyst 138, 4092–4102 (2013).

  43. 43

    Scaglia, E. et al. Noninvasive assessment of hepatic fibrosis in patients with chronic hepatitis C using serum Fourier transform infrared spectroscopy. Anal. Bioanal. Chem. 401, 2919–2925 (2011).

  44. 44

    Hands, J. et al. Investigating the rapid diagnosis of gliomas from serum samples using infrared spectroscopy and cytokine and angiogenesis factors. Anal. Bioanal. Chem. 405, 7347–7355 (2013).

  45. 45

    Munro, K.L. et al. Synchrotron radiation infrared microspectroscopy of arsenic-induced changes to intracellular biomolecules in live leukemia cells. Vib. Spectrosc. 53, 39–44 (2010).

  46. 46

    Tobin, M.J. et al. FTIR spectroscopy of single live cells in aqueous media by synchrotron IR microscopy using microfabricated sample holders. Vib. Spectrosc. 53, 34–38 (2010).

  47. 47

    Whelan, D.R., Bambery, K.R., Puskar, L., McNaughton, D. & Wood, B.R. Synchrotron Fourier transform infrared (FTIR) analysis of single living cells progressing through the cell cycle. Analyst 138, 3891–3899 (2013).

  48. 48

    Kuimova, M.K., Chan, K.L.A. & Kazarian, S.G. Chemical imaging of live cancer cells in the natural aqueous environment. Appl. Spectrosc. 63, 164–171 (2009).

  49. 49

    Llabjani, V. et al. Polybrominated diphenyl ether-associated alterations in cell biochemistry as determined by attenuated total reflection Fourier-transform infrared spectroscopy: a comparison with DNA-reactive and/or endocrine-disrupting agents. Environ. Sci. Technol. 43, 3356–3364 (2009).

  50. 50

    Malins, D.C. et al. Biomarkers signal contaminant effects on the organs of English sole (Parophrys vetulus) from Puget Sound. Environ. Health Perspect. 114, 823–829 (2006).

  51. 51

    Cakmak, G., Togan, I. & Severcan, F. 17β-Estradiol induced compositional, structural and functional changes in rainbow trout liver, revealed by FT-IR spectroscopy: a comparative study with nonylphenol. Aquat. Toxicol. 77, 53–63 (2006).

  52. 52

    Llabjani, V. et al. Alterations in the infrared spectral signature of avian feathers reflect potential chemical exposure: A pilot study comparing two sites in Pakistan. Environ. Int. 48, 39–46 (2012).

  53. 53

    Trevisan, J. et al. Syrian hamster embryo (SHE) assay (pH 6.7) coupled with infrared spectroscopy and chemometrics towards toxicological assessment. Analyst 135, 3266–3272 (2010).

  54. 54

    Ahmadzai, A.A. et al. The Syrian hamster embryo (SHE) assay (pH 6.7): mechanisms of cell transformation and application of vibrational spectroscopy to objectively score endpoint alterations. Mutagenesis 27, 257–266 (2012).

  55. 55

    Ami, D., Natalello, A., Zullini, A. & Doglia, S.M. Fourier transform infrared microspectroscopy as a new tool for nematode studies. FEBS Lett. 576, 297–300 (2004).

  56. 56

    Hobro, A.J. & Lendl, B. Fourier-transform mid-infrared FPA imaging of a complex multicellular nematode. Vib. Spectrosc. 57, 213–219 (2011).

  57. 57

    Mariey, L., Signolle, J.P., Amiel, C. & Travert, J. Discrimination, classification, identification of microorganisms using FTIR spectroscopy and chemometrics. Vib. Spectrosc. 26, 151–159 (2001).

  58. 58

    Gómez-De-Anda, F. et al. Determination of Trichinella spiralis in pig muscles using mid-Fourier transform infrared spectroscopy (MID-FTIR) with attenuated total reflectance (ATR) and soft independent modeling of class analogy (SIMCA). Meat Sci. 91, 240–246 (2012).

  59. 59

    Kazarian, S.G. & Chan, K.A. ATR-FTIR spectroscopic imaging: recent advances and applications to biological systems. Analyst 138, 1940–1951 (2013).

  60. 60

    Glassford, S.E., Byrne, B. & Kazarian, S.G. Recent applications of ATR FTIR spectroscopy and imaging to proteins. Biochim. Biophys. Acta 1834, 2849–2858 (2013).

  61. 61

    Stuart, B. Infrared Spectroscopy: Fundamentals and Applications. John Wiley and Sons (2005).

  62. 62

    Mantsch, H.H. & Chapman, D. Infrared Spectroscopy of Biomolecules (Wiley-Liss, 1996).

  63. 63

    Miller, L.M. & Dumas, P. Chemical imaging of biological tissue with synchrotron infrared light. Biochim. Biophys. Acta 1758, 846–857 (2006).

  64. 64

    Liu, J.-N., Schulmerich, M.V., Bhargava, R. & Cunningham, B.T. Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared spectroscopy. Opt. Express 19, 24182–24197 (2011).

  65. 65

    Miller, L.M. & Smith, R.J. Synchrotrons versus globars, point-detectors versus focal plane arrays: Selecting the best source and detector for specific infrared microspectroscopy and imaging applications. Vib. Spectrosc. 38, 237–240 (2005).

  66. 66

    Lasch, P., Boese, M., Pacifico, A. & Diem, M. FT-IR spectroscopic investigations of single cells on the subcellular level. Vib. Spectrosc. 28, 147–157 (2002).

  67. 67

    Duncan, W. & Williams, G.P. Infrared synchrotron radiation from electron storage rings. Appl. Optics 22, 2914–2923 (1983).

  68. 68

    Pijanka, J.K. et al. Spectroscopic signatures of single, isolated cancer cell nuclei using synchrotron infrared microscopy. Analyst 134, 1176–1181 (2009).

  69. 69

    Dumas, P., Sockalingum, G.D. & Sule-Suso, J. Adding synchrotron radiation to infrared microspectroscopy: what's new in biomedical applications? Trends Biotechnol. 25, 40–44 (2007).

  70. 70

    Martin, F.L. Shining a new light into molecular workings. Nat. Methods 8, 385–387 (2011).

  71. 71

    Menzel, L. et al. Spectroscopic detection of biological NO with a quantum cascade laser. Appl. Phys. B 72, 859–863 (2001).

  72. 72

    Valle, J.J. et al. Free electron laser-Fourier transform ion cyclotron resonance mass spectrometry facility for obtaining infrared multiphoton dissociation spectra of gaseous ions. Rev. Sci. Instrum. 76, 023103 (2005).

  73. 73

    Llabjani, V. et al. Differential effects in mammalian cells induced by chemical mixtures in environmental biota as profiled using infrared spectroscopy. Environ. Sci. Technol. 45, 10706–10712 (2011).

  74. 74

    Schubert, J.M., Mazur, A.I., Bird, B., Miljković, M. & Diem, M. Single point vs. mapping approach for spectral cytopathology (SCP). J. Biophotonics 3, 588–596 (2010).

  75. 75

    Carter, E.A., Tam, K.K., Armstrong, R.S. & Lay, P.A. Vibrational spectroscopic mapping and imaging of tissues and cells. Biophys. Rev. 1, 95–103 (2009).

  76. 76

    Nasse, M.J. et al. High-resolution Fourier-transform infrared chemical imaging with multiple synchrotron beams. Nat. Methods 8, 413–416 (2011).

  77. 77

    Fernandez, D.C., Bhargava, R., Hewitt, S.M. & Levin, I.W. Infrared spectroscopic imaging for histopathologic recognition. Nat. Biotechnol. 23, 469–474 (2005).

  78. 78

    Chan, K. & Kazarian, S. New opportunities in micro-and macro-attenuated total reflection infrared spectroscopic imaging: spatial resolution and sampling versatility. Appl. Spectrosc. 57, 381–389 (2003).

  79. 79

    Holton, S.E., Walsh, M.J. & Bhargava, R. Subcellular localization of early biochemical transformations in cancer-activated fibroblasts using infrared spectroscopic imaging. Analyst 136, 2953–2958 (2011).

  80. 80

    Bassan, P. et al. The inherent problem of transflection-mode infrared spectroscopic microscopy and the ramifications for biomedical single point and imaging applications. Analyst 138, 144–157 (2013).

  81. 81

    Goormaghtigh, E., Raussens, V. & Ruysschaert, J.M. Attenuated total reflection infrared spectroscopy of proteins and lipids in biological membranes. Biochim. Biophys. Acta 1422, 105–185 (1999).

  82. 82

    Wehbe, K., Filik, J., Frogley, M.D. & Cinque, G. The effect of optical substrates on micro-FTIR analysis of single mammalian cells. Anal. Bioanal. Chem. 405, 1311–1324 (2013).

  83. 83

    Vaccari, L. et al. Synchrotron radiation infrared microspectroscopy of single living cells in microfluidic devices: advantages, disadvantages and future perspectives. J. Phys.: Conf. Ser. 359, 012007 (2012).

  84. 84

    Chan, K.L.A. & Kazarian, S.G. FT-IR spectroscopic imaging of reactions in multiphase flow in microfluidic channels. Anal. Chem. 84, 4052–4056 (2012).

  85. 85

    Marcsisin, E.J., Uttero, C.M., Miljkovic, M. & Diem, M. Infrared microspectroscopy of live cells in aqueous media. Analyst 135, 3227–3232 (2010).

  86. 86

    Nasse, M., Ratti, S., Giordano, M. & Hirschmugl, C. Demountable liquid/flow cell for in vivo infrared microspectroscopy of biological specimens. Appl. Spectrosc. 63, 1181–1186 (2009).

  87. 87

    Holman, H.-Y.N., Bechtel, H.A., Hao, Z. & Martin, M.C. Synchrotron IR spectromicroscopy: chemistry of living cells. Anal. Chem. 82, 8757–8765 (2010).

  88. 88

    Bhargava, R. & Levin, I.W. Fourier transform infrared imaging: theory and practice. Anal. Chem. 73, 5157–5167 (2001).

  89. 89

    Bhargava, R. & Levin, I.W. Effective time averaging of multiplexed measurements: A critical analysis. Anal. Chem. 74, 1429–1435 (2002).

  90. 90

    Bhargava, R., Ribar, T. & Koenig, J.L. Towards faster FT-IR imaging by reducing noise. Appl. Spectrosc. 53, 1313–1322 (1999).

  91. 91

    Bhargava, R., Schaeberle, M.D., Fernandez, D.C. & Levin, I.W. Novel route to faster Fourier transform infrared spectroscopic imaging. Appl. Spectrosc. 55, 1079–1084 (2001).

  92. 92

    Gazi, E. et al. Fixation protocols for subcellular imaging by synchrotron-based Fourier transform infrared microspectroscopy. Biopolymers 77, 18–30 (2005).

  93. 93

    Bretzlaff, R. & Bahder, T. Apodization effects in Fourier transform infrared difference spectra. Rev. Phys. Appl. 21, 833–844 (1986).

  94. 94

    Tahtouh, M., Despland, P., Shimmon, R., Kalman, J.R. & Reedy, B.J. The application of infrared chemical imaging to the detection and enhancement of latent fingerprints: method optimization and further findings. J. Forensic Sci. 52, 1089–1096 (2007).

  95. 95

    Lasch, P. & Naumann, D. Spatial resolution in infrared microspectroscopic imaging of tissues. Biochim. Biophys. Acta 1758, 814–829 (2006).

  96. 96

    Sun, D.-W. Infrared Spectroscopy for Food Quality Analysis and Control (Academic Press, 2009).

  97. 97

    Snook, R.D., Harvey, T.J., Faria, E.C. & Gardner, P. Raman tweezers and their application to the study of singly trapped eukaryotic cells. Integr. Biol. 1, 43–52 (2009).

  98. 98

    Bhargava, R., Fernandez, D.C., Schaeberle, M.D. & Levin, I.W. Effect of focal plane array cold shield aperture size on Fourier transform infrared micro-imaging spectrometer performance. Appl. Spectrosc. 54, 1743–1750 (2000).

  99. 99

    Bruun, S.W. et al. Correcting attenuated total reflection-Fourier transform infrared spectra for water vapor and carbon dioxide. Appl. Spectrosc. 60, 1029–1039 (2006).

  100. 100

    Lasch, P. & Petrich, W. Data acquisition and analysis in biomedical vibrational spectroscopy. Biomed. Appl. Sync. Infrared Microspec. 11, 192–225 (2011).

  101. 101

    Trevisan, J., Angelov, P.P., Carmichael, P.L., Scott, A.D. & Martin, F.L. Extracting biological information with computational analysis of Fourier-transform infrared (FTIR) biospectroscopy datasets: current practices to future perspectives. Analyst 137, 3202–3215 (2012).

  102. 102

    Martin, F.L. et al. Distinguishing cell types or populations based on the computational analysis of their infrared spectra. Nat. Protoc. 5, 1748–1760 (2010).

  103. 103

    Duda, R.O., Hart, P.E. & Stork, D.G. Pattern Classification 2nd edn. (Wiley Interscience, 2001).

  104. 104

    Goodacre, R. Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules. Vib. Spectrosc. 32, 33–45 (2003).

  105. 105

    Naumann, D. FTIR spectroscopy of microorganisms at the Robert Koch Institute: experiences gained during successful project. Proc. SPIE 6853, Biomedical Optical Spectroscopy 68530G (2008).

  106. 106

    Lasch, P. & Petrich, W. in Biomedical Applications of Synchrotron Infrared Microspectroscopy: a Practical Approach, RSC Analytical Spectroscopy Series Vol. 11, (ed. D. Moss), 192–225 (RSC Analytical Spectroscopy Series, 2011).

  107. 107

    Lasch, P. Spectral pre-processing for biomedicalvibrational spectroscopy and microspectroscopicimaging. Chemom. Intell. Lab. Syst. 117, 100–114 (2013).

  108. 108

    Bhargava, R., Wang, S.-Q. & Koenig, J.L. Route to higher fidelity FT-IR imaging. Appl. Spectrosc. 54, 486–495 (2000).

  109. 109

    Reddy, R.K. & Bhargava, R. Accurate histopathology from low signal-to-noise ratio spectroscopic imaging data. Analyst 135, 2818–2825 (2010).

  110. 110

    van Dijk, T., Mayerich, D., Bhargava, R. & Carney, P.S. Rapid spectral-domain localization. Opt. Express 21, 12822–12830 (2013).

  111. 111

    van Dijk, T., Mayerich, D., Carney, P.S. & Bhargava, R. Recovery of absorption spectra from Fourier transform infrared (FT-IR) microspectroscopic measurements of intact spheres. Appl. Spectrosc. 67, 546–552 (2013).

  112. 112

    Martens, H. & Stark, E. Extended multiplicative signal correction and spectral interference subtraction - new pre-processing methods for near-infrared spectroscopy. J. Pharmaceut. Biomed. 9, 625–635 (1991).

  113. 113

    Bassan, P. et al. Resonant Mie scattering in infrared spectroscopy of biological materials - understanding the 'dispersion artefact'. Analyst 134, 1586–1593 (2009).

  114. 114

    Bassan, P. et al. Resonant Mie scattering (RMieS) correction of infrared spectra from highly scattering biological samples. Analyst 135, 268–277 (2010).

  115. 115

    Bassan, P. et al. RMieS-EMSC correction for infrared spectra of biological cells: extension using full Mie theory and GPU computing. J. Biophotonics 3, 609–620 (2010).

  116. 116

    Opus 5 Reference Manual (Bruker Optik, 2004).

  117. 117

    Ly, E. et al. Combination of FTIR spectral imaging and chemometrics for tumour detection from paraffin-embedded biopsies. Analyst 133, 197–205 (2008).

  118. 118

    Beier, B.D. & Berger, A.J. Method for automated background subtraction from Raman spectra containing known contaminants. Analyst 134, 1198–1202 (2009).

  119. 119

    Baker, M. et al. An investigation of the RWPE prostate derived family of cell lines using FTIR spectroscopy. Analyst 135, 887–894 (2010).

  120. 120

    Guyon, I., Gunn, S., Nikravesh, M. & Zadeh, L. Feature Extraction, Foundations and Applications (Springer, 2006).

  121. 121

    Udelhoven, T., Novozhilov, M. & Schmitt, J. The NeuroDeveloper (R): a tool for modular neural classification of spectroscopic data. Chemometr. Intell. Lab. 66, 219–226 (2003).

  122. 122

    Kwak, J.T., Reddy, R., Sinha, S. & Bhargava, R. Analysis of variance in spectroscopic imaging data from human tissues. Anal. Chem. 84, 1063–1069 (2011).

  123. 123

    Bhargava, R., Fernandez, D.C., Hewitt, S.M. & Levin, I.W. High throughput assessment of cells and tissues: Bayesian classification of spectral metrics from infrared vibrational spectroscopic imaging data. Biochim. Biophys. Acta 1758, 830–845 (2006).

  124. 124

    Naumann, D. in Encyclopedia of Analytical Chemistry (John Wiley & Sons, 2000).

  125. 125

    Hughes, C. et al. FTIR microspectroscopy of selected rare diverse sub-variants of carcinoma of the urinary bladder. J. Biophotonics 6, 73–87 (2013).

  126. 126

    Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction 2nd edn. (Springer, 2009).

  127. 127

    Bhargava, R. Towards a practical Fourier transform infrared chemical imaging protocol for cancer histopathology. Anal. Bioanal. Chem. 389, 1155–1169 (2007).

  128. 128

    Berenbaum, M.C. The histochemistry of bound lipids. Q. J. Microsc. Sci. s3-99, 231–242 (1958).

  129. 129

    Wigglesworth, V.B. Bound lipid in the tissues of mammal and insect: a new histochemical method. J. Cell Sci. 8, 709–725 (1971).

  130. 130

    Stitt, D.M. et al. Tissue acquisition and storage associated oxidation considerations for FTIR microspectroscopic imaging of polyunsaturated fatty acids. Vib. Spectrosc. 60, 16–22 (2012).

  131. 131

    Owens, G.L. et al. Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients. J. Biophotonics 7, 200–209 (2014).

  132. 132

    Dorling, K.M. & Baker, M.J. Highlighting attenuated total reflection Fourier transform infrared spectroscopy for rapid serum analysis. Trends Biotechnol. 31, 327–328 (2013).

  133. 133

    Chang, C.-C. & Lin, C.-J. LIBSVM: a library for support vector machines. ACM TIST 2, 27 (2011).

  134. 134

    Trevisan, J. et al. Measuring similarity and improving stability in biomarker identification methods applied to Fourier-transform infrared (FTIR) spectroscopy. J. Biophotonics 7, 254–265 (2014).

  135. 135

    Lasch, P., Fabian, H., Thi, N.A.N. & Naumann, D. Infrarot-bildgebung für die pathohistologische diagnostik. Laborwelt 2, 8–12 (2004).

  136. 136

    Lasch, P. & Kneipp, J. Biomedical Vibrational Spectroscopy. (Wiley-Blackwell, 2008).

  137. 137

    Lyng, F., Gazi, E. & Gardner, P. in Preparation of Tissues and Cells for Infrared and Raman Spectroscopy (RSC Analytical Spectroscopy Monographs, No. 11, ed. Moss, D.) 147–185 (Royal Society of Chemistry, 2011).

  138. 138

    Whelan, D.R., Bambery, K.R., Puskar, L., McNaughton, D. & Wood, B.R. Quantification of DNA in simple eukaryotic cells using Fourier transform infrared spectroscopy. J. Biophotonics 6, 775–784 (2013).

  139. 139

    Whelan, D.R. et al. Monitoring the reversible B- to A-like transition of DNA in eukaryotic cells using Fourier transform infrared spectroscopy. Nucleic Acids Res. 39, 5439–5448 (2011).

  140. 140

    Rahmelow, K. & Hubner, W. Phase correction in Fourier transform spectroscopy: subsequent displacement correction and error limit. Appl. Optics 36, 6678–6686 (1997).

  141. 141

    Gazi, E. et al. The combined application of FTIR microspectroscopy and ToF-SIMS imaging in the study of prostate cancer. Faraday Discuss. 126, 41–59 (2004).

  142. 142

    Patel, I.I. et al. Isolating stem cells in the inter-follicular epidermis employing synchrotron radiation-based Fourier-transform infrared microspectroscopy and focal plane array imaging. Anal. Bioanal. Chem. 404, 1745–1758 (2012).

  143. 143

    Bassan, P. et al. FTIR microscopy of biological cells and tissue: data analysis using resonant Mie scattering (RMieS) EMSC algorithm. Analyst 137, 1370–1377 (2012).

  144. 144

    Kastyak-Ibrahim, M. et al. Biochemical label-free tissue imaging with subcellular-resolution synchrotron FTIR with focal plane array detector. NeuroImage 60, 376–383 (2012).

  145. 145

    Gajjar, K. et al. Diagnostic segregation of human brain tumours using Fourier-transform infrared and/or Raman spectroscopy coupled with discriminant analysis. Anal. Methods 5, 89–102 (2013).

  146. 146

    Kelly, J.G., Martin-Hirsch, P.L. & Martin, F.L. Discrimination of base differences in oligonucleotides using mid-infrared spectroscopy and multivariate analysis. Anal. Chem. 81, 5314–5319 (2009).

Download references


Over several years, work in F.L.M.'s laboratories has been funded by the UK Engineering and Physical Sciences Research Council (EPSRC), the Rosemere Cancer Foundation and the UK Biotechnology and Biological Sciences Research Council (BBSRC). Work in R.B.'s laboratories has been funded by the US National Institutes of Health (grants R01CA138882 and R01EB009745). M.J.W. acknowledges the Department of Pathology, University of Illinois at Chicago for funding. P.R.F. and P.G. acknowledge the EPSRC. M.J.B. acknowledges the Rosemere Cancer Foundation, the EPSRC, Brain Tumour North West, the Sydney Driscoll Neuroscience Foundation and the Defence Science and Technology (Dstl).

Author information

F.L.M. is the principal investigator who conceived the idea for the manuscript; M.J.B. and K.M.D. provided information regarding FTIR FPA imaging; and P.R.F. provided information regarding microfluidic devices; J.T. wrote sections regarding Data processing, ANTICIPATED RESULTS and Figures, as well as maintaining a working manuscript; H.J.B. wrote the Instrumentation and Spectral acquisition sections; K.A.H. and B.O. wrote the Sample preparation and MATERIALS sections; R.J.S. wrote the INTRODUCTION and PROCEDURE, and contributed to the ANTICIPATED RESULTS section; C.H. provided material for Sample preparation, PCA–k-means clustering and cross-validation; P.L. provided figure suggestions and information regarding water vapor; B.R.W. provided information regarding live-cell imaging; R.B. provided significant revisions to the final manuscript; M.J.B., P.B., K.M.D., N.J.F., C.H., P.L., P.L.M.-H., G.D.S., J.S.-S., M.J.W., S.W.F., B.R.W. and P.G. all provided feedback on the manuscript; and F.L.M. brought together the text and finalized the manuscript.

Correspondence to Francis L Martin.

Ethics declarations

Competing interests

P.L. is the author and owner of CytoSpec, a software package for vibrational hyperspectral imaging.

Supplementary information

Supplementary Method 1

Direct drop ATR-FTIR spectroscopy biofluid analysis. (PDF 159 kb)

Supplementary Method 2

FTIR FPA imaging using Agilent 670-IR spectrometer coupled with Agilent 620-IR microscope and FPA detector. (PDF 803 kb)

Supplementary Method 3

SVM classification in MATLAB using the IRootLab toolbox. (PDF 332 kb)

Supplementary Method 4

Protocol for FTIR spectroscopy of single living cells using a synchrotron source. (PDF 1161 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Baker, M., Trevisan, J., Bassan, P. et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat Protoc 9, 1771–1791 (2014) doi:10.1038/nprot.2014.110

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.