A tutorial on near infrared spectroscopy and its calibration

Critical Reviews in Analytical Chemistry

Volumn 40, Issue 4, 2010, Pages 246-260

  Agelet, Lidia Esteve ^a   Hurburgh Jr , Charles R ^a  

^a Iowa State University of Science and Technology   (United States)

Author keywords

Calibration; Chemometrics; NIR; Spectrophotometer

Indexed keywords

CHEMOMETRICS; GRAIN INDUSTRY; MATERIAL SCIENCE; MATHEMATICAL APPROACH; NEAR INFRARED; NIR; OPERATING PROCESS;

CALIBRATION; GRAIN GROWTH; INFRARED RADIATION; METEOROLOGICAL INSTRUMENTS; NEAR INFRARED SPECTROSCOPY; SPECTROPHOTOMETERS; SPECTROPHOTOMETRY;

INFRARED DEVICES;

ARCHAEOLOGY; CALIBRATION; NEAR INFRARED; SPECTROPHOTOMETRY;

EID: 78149390954     PISSN: 10408347     EISSN: 15476510     Source Type: Journal    
DOI: 10.1080/10408347.2010.515468     Document Type: Article

References (92)

1. F.W. Herschel, Investigation of the powers of the prismatic colours to heat and illuminate objects. Philosophical Transactions of the Royal Society of London 90 (1800):255-329.
2. G. Mc. Dryden, "Near Infrared reflectance spectroscopy: Applications in deer nutrition" Publication No W03/007, Project No UQ 109A (2003),, accessed February 2009.
3. J. J. Workman, "An introduction to near infrared spectroscopy" (2005), (http://www.spectroscopynow.com/coi/cda/detail.cda?id =1881&type=EducationFeature&chId=2&page=1) accessed January 2010.
4. L. Bokobza, Near Infrared spectroscopy. Journal of Near Infrared Spectroscopy 6 (1998):3-17.
5. J. R. Hart, and K. H. Norris, Determination of the moisture content of seeds by near-infrared spectophotometry of their methanol extracts. Cereal Chemistry 39 (1962):94-99.
6. K. H. Norris and J. R. Hart, Direct spectrophotospectrometric determination of moisture content of grain and seeds. International Symposium Washington 1963, 4 (1965):19-25.
7. AACC International, Approved Methods of the American Association of Cereal Chemists (AACC, St. Paul, MN, 1999).
8. J. Workman and L. Weyer, History of near infrared (NIR) applications, in Practical Guide to Interpretive Near-Infrared Spectroscopy eds. J. Workman and L. Weyer (CRC Press, Boca Raton, FL, 2007), Ch. 13, 107-111.
9. N. Pou Saboya, Analisis de control de preparados farmaceuticos mediante espectroscopia en el infrarojo proximo. Ph.D dissertation Universitat de Barcelona (UAB), Barcelona, Spain, 2002.
10. D. J. Dham and K. D. Dham, The physics of near-infrared scattering, in Near-Infrared Technology in the Agricultural and Food Industries eds. P. C.Williams and K. Norris (AACC, St Paul,MN, 2001), Ch, 1, 1-17.
11. O. Berntsson, L-G. Danielsson, and S. Folestad, Estimation of effective sample size when analysing powders with diffuse reflectance near-infrared spectrometry. Analytica Chemica Acta 364(1-3) (1998):243-251.
12. K. Norris and P. C. Williams, Optimization of mathematical treatments of raw near-infrared signal in the measurement of protein in hard red spring wheat: I. Influence of particle size. Cereal Chemistry 61 (1984):158-165.
13. P. Corti, G. Ceramelli, E. Dreassi, and S. Mattiim, Near infrared transmittance analysis for the assay of solid pharmaceutical dosage forms. The Analyst 124 (1999):755-758.
14. S. E. Kays, N. Shimizu, F. E. Barton II, and K. Ohtsubo, Nearinfrared transmission and reflectance spectroscopy for the determination of dietary fiber in barley cultivars. Crop Science 45 (2005):2307-2311.
15. S. M. Short, R. P. Cogdill, and C. A. Anderson, Figures of merit comparison of reflectance and transmittance near-infrared methods for the prediction of constituent concentrations in pharmaceutical compacts. Journal of Pharmaceutical Innovation 3(1) (2008): 41-50.
16. B. A. Orman and R. A. Jr. Schuman, Comparison of near-infrared spectroscopy calibration methods for the prediction of protein, oil, and starch in maize grain. Journal of Agricultural and Food Chemistry 39(5) (1991):883-886.
17. P. C.Williams andD. C. Sovering, Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. Journal of Near Infrared Spectroscopy 1 (1993):25-32.
18. T. Börjesson, B. Stenberg, and J. Schnürer, Near-infrared spectroscopy for estimation of ergosterol content in barley: A comparison between reflectance and transmittance techniques. Cereal Chemistry 84(3) (2007):231-236.
19. J. Xing and D. Guyer, Comparison of transmittance and reflectance to detect insect infestation inMontmorency tart cherry. Computers and Electronics in Agriculture 64(2) (2008):194-201.
20. S. R. Delwiche, Single wheat kernel analysis by near-infrared transmittance: protein content. Cereal Chemistry 72(1) (1995): 11-16.
21. R. P. Cogdill, C. A. Anderson, M. Delgado-Lopez, D. Molseed, R. Bolton, T. Herkert, A. M. Afnan, and J. K. Drennen III, Process analytical technology case study part I: Feasibility studies for quantitative near-infrared method development. AAPS Pharm-SciTech 6(2) (2007):E262-E272.
22. D. Fischer and E. Pigorsch, New developments in process control by spectroscopic methods in the polymer and plastics industry-Near infrared miniature spectrometer and high temperature and pressure near infrared and raman probes. Paper presented at the third annual UNESCO school IUPAC conference on Macromolecules and materials science, Matieland, South Africa (2000), (academic.sun.ac.za/unesco/ PolymerED2000/Conf2000/Fischer.pdf). accessed January 2010.
23. E. D. Lipp, Near-infrared spectroscopy of silicon-containing materials. Applied Spectroscopy Reviews 27(4) (1992):385-408.
24. R. R. Buchanan, D. E. Honigs, C. J. Lee, and W. Roth, Detection of ethanol in wines using optical-fiber measurements and nearinfrared analysis. Applied Spectroscopy 42 (1988):1106-1111.
25. E. Tamburini, G. Vaccari, S. Tosi, and A. Trilli, Near-infrared spectroscopy: A tool for monitoring submerged fermentation processes using an immersion optical-fiber probe. Applied Spectroscopy 57(2) (2003):132-138.
26. M. C. Sarraguca, A. Paulo, M. M. Alves, A. M. A. Dias, J.A. Lopes, and E. C. Ferreira, Quantitative monitoring of an activated sludge reactor using on-line UV-visible and near-infrared spectroscopy. Analytical and Bioanalytical Chemistry 395(4) (2009):1159-1166.
27. M. Bacci, C. Bellucci, C. Cucci, C. Frosinini, M. Picollo, S. Porcinai, and B. Radicati, Fiber optics reflectance spectroscopy in the entire VIS-IR Range: A powerful tool for the non-invasive characterization of paintings, in Materials Issues in Art and Archaeology VII eds. P. B. Vandiver, J. L. Mass, and A. Murray, (Materials Research Society, Warrendale, PA, USA, 2005), 852:297-302.
28. B. Yu, E. S. Burnside, G. A. Sisney, J. M. Harter, Z. Changfang, A. Dhalla, and N. Ramunjam, Feasibility of near-infrared diffuse optical spectroscopy on patients undergoing image guided coreneedle biopsy. Optics Express 15(12) (2007):7335-7350.
29. J. P. Smith, Product review: Spectrometers get small. Miniature spectrometers rival benchtop instruments. Analytical Chemistry 72(19) (2000):653A-658A.
30. E. Stark and K. Luchter, NIR instrumentation technology. NIR News 16(7) (2005):13-16.
31. W. F. McClure, D. Moody, D. L. Standfield, and O. Kinoshita, Hand-held NIR spectrometry. Part II: An economical no-moving parts spectrometer for measuring chlorophyll and moisture. Applied Spectroscopy 56(6) (2002):720-724.
32. Axun Technologies, Designing a miniature spectrometer, Technical note (2005), , accessed January 2010.
33. J. Malinen, M. Känsäkoski, R. Rikola, and C. G. Eddison, LEDbased NIR spectrometer module for hand-held and process analyzer applications. Sensors and Actuators B: Chemical 51(1-3) (1998):220-226.
34. M. Lang, Diodes storm the tunable laser ranks, Photonics Tech Briefs (1999), (http://www.ptbmagazine.com/articles/ diodes0199/), accessed January 2010.
35. C. Balas, Review of biomedical optical imaging-A powerful, non-invasive, non-ionizing technology for improving in-vivo diagnosis. Measurement Science Technology 20 (2009):1-12.
36. Y. Garini, I. T. Young, and G. McNamara, Spectral imaging: Principles and applications. Cytometry 69A(8) (2006): 735-747.
37. A. W. Schumann and J. H. Meyer, Progress with the implementation of diode array near-infrared spectrometer for direct at-line analysis of sugarcane samples. Proceedings of South Africa Sugar Technology Association 74 (2000):122-123.
38. W. Wang and J. Paliwal, Design and evaluation of a visible-tonear-infrared electronic slitless spectrograph. Science Technology 17 (2006):2698-2704.
39. S. Holler, Y. Pan, R. K. Chang, J. R. Bottiger, S. C. Hill, and D. B. Hillis, Two-dimensional angular optical scattering for the characterization of airborne microparticles. Optics Letters 23 (1998):1489-1491.
40. J. Domanchin and J. R. Gilchrist, Size and spectrum. Photonics July 2001, (2001):12-118.
41. Thermo Fisher Scientific, Advantages of fourier-transform nearinfrared spectroscopy. Application note 50771 (2006), , accessed January 2010.
42. C. V. Greensill and K. B. Walsh, Optimization of instrument precision and wavelength resolution for the performance of NIR calibrations of sucrose in water-cellulose matrix. Applied Spectroscopy 54(3) (2000):426-430.
43. Y. Geller, A new approach to NIR spectroscopy allowing remote analysis. LabPlus International 20 (2006):13-16.
44. P. R. Amstrong, F. X. Maghirang, and F. E. Dowell, Comparison of dispersive and Fourier-transform NIR instruments for measuring grain and flour attributes. Applied Engineering in Agriculture 22(3) (2006):453-457.
45. L. Mcarthur and C. Greensill, Impact of resolution on NIR PLS calibration of kaolinite content with weipa bauxite. Measurement Science Technology 18 (2007):1343-1347.
46. H. Chung, S. Choi, J. Choo, and Y. Lee, Investigation of partial least squares (PLS) calibration performance based on different resolutions of near infrared spectra. Bulletin of Korean Chemistry Society 25(5):647-651.
47. T. Tarumi, A. K. Amerov, M. A. Arnold, and G. W. Small, Design considerations for near-infrared filter photometry: Effects of noise sources and selectivity. Applied Spectroscopy 63(6) (2009): 700-708.
48. K. B. Wash, J. A. Guthire, and J. W. Burney, Application of commercially available, low-cost, miniaturized NIR spectrometers to the assessment of the sugar content of intact fruit. Australian Journal of Plant Physiology 27(12) (2000):1175-1186.
49. C. Jr R. Hurburgh, and G. R. Rippke, Calibration, standardization and validation economics. Oral presentation at the International Diffuse Reflectance Conference (IDRC) (2008), Chambersburg, PA.
50. P. J. Brimmer, F. A. De Thomas, and J. W. Hall, Method development and implementation of near-infrared spectroscopy in industrial manufacturing processes, in Near-Infrared Technology in the Agricultural and Food Industries eds. P. C.Williams and K. Norris (AACC, St. Paul, MN), Ch. 11, (2001):199-214.
51. P. C. Williams, Implementation of Near Infrared technology, in Near-Infrared Technology in the Agricultural and Food Industries, eds. P. C.Williams and K. Norris (AACC, St Paul,MN, 2001), Ch. 8, 145-170.
52. T. Fearn, Chemometrics: An enabling tool for NIR. NIR News 16(7) (2005):17-19.
53. T. Naes, The design of calibration in near reflectance analysis by clustering. Journal of Chemometrics 1 (1987):121-134.
54. T. Naes, T. Isaksson, T. Fearn, and T. Davies, Selection of samples for calibration, in A User-Friendly Guide to Multivariate Calibration and Classification (NIR Publications, Chichester, UK, 2002), Ch. 15, 191-195.
55. D. B. Coats, Is near infrared spectroscopy only as good as the laboratory reference values? An empirical approach. Spectroscopy Europe 14(4) (2002):24-26.
56. I. V. Kovalenko, G. R. Rippke, and C. R. Hurburgh, Determination of amino acid composition of soybeans (Glycine max) by nearinfrared spectroscopy. Journal of Agricultural and Food Chemistry 54 (2006):3485-3491.
57. M. Hubert, P. J. Rousseeuw, and S. Van Aelst, High-breakdown robust multivariate methods. Statistical Science 23(1) (2008): 92-119.
58. W. J. Dixon, Analysis of extreme values. Annals of Mathematic Statistics 21 (1950):488-506.
59. F. E. Grubbs, Sample criteria for testing outlying observations. Annals of Mathematic Statistics 21 (1950):27-58.
60. B.Walczak and D. L.Massart,Multiple outlier detection revisited. Chemometrics and Intelligent Laboratory Systems 41(1) (1998):1-15.
61. T. Naes, T. Isaksson, T. Fearn, and T. Davies, Outlier detection, in A User-Friendly Guide to Multivariate Calibration and Classification (NIR Publications, Chichester, UK 2002), Ch. 14, 177-189.
62. D. M. Haaland and E. V. Thomas, Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information. Analytical Chemistry 60 (1988):1193-1202.
63. P. Geladi, D. MacDougall, and H. Martens, Linearization and scatter-correction for near-infrared reflectance spectra of meat. Applied Spectroscopy 39 (1985):491-500.
64. R. J. Barnes, M. S. Dhanoa, and S. J. Lister, Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy 43 (1989):72-777.
65. M. S. Danoa, S. J. Lister, R. Sanderson, and R. J. Barnes, The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. Journal of Near Infrared Spectroscopy 2 (1994):43-47.
66. T. Fearn, C. Riccicoli, A. Garrido-Baro, and E. Guerrero-Ginel, On the geometry of SNV and MSC. Chemometrics and Intelligent Laboratory Systems 96 (2009):22-26.
67. D. W. Hopkins, Using data pretreatments effectively. Seminar at International Diffuse Reflectance Conference (2008), Chambersburg, PA.
68. A. Savitzky and M. J. E. Golay, Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36(8) (1964):1627-1639.
69. S. Wold, H. Antti, F. Lindgren, and J. Öhman, Orthogonal signal correction of near-infrared spectra. Chemometric Intelligent Laboratory Systems 44 (1998):175-185.
70. J. C. Sternberg, H. S. Stillo, and R. H. Schwendeman, Spectrophotometric analysis of multicomponent systems using the least squares method in matrix form. Analytical Chemistry 32 (1960):84-90.
71. S. Wold, Soft modeling by latent variables; the non-linear iterative partial least squares approach. in Perspectives in Probability and Statistics, Papers in Honour of M.S. Barlett, ed. J. Gani (Academic Press London 1975).
72. T. Naes, C. Irgens, and H. Martens, Comparison of linear statistical methods for calibration of NIR instruments. Journal of the Royal Statistical Society Series C (Applied Statistics) 35(2) (1986):195-206.
73. B. Hammateenejad, M. Akhind, and F. Samar, A comparative study between PCR and PLS in simultaneous spectrophotometric determination of diphenylamine, aniline, and phenol: Effect of wavelength selection. Spectrochimica Acta, part A:Molecular and Biomolecular Spectroscopy 67(3-4) (2007):958-965.
74. R. Muñiz, M. A. Perez, C. De la Torre, C. E. Carleos, N. Corral, and J. A. Baro, Comparison of principal component regression (PCR) and partial least square (PLS) methods in prediction of raw milk composition by VIS-NIR spectrometry. Application to development of on-line sensors for fat, protein and lactose contents. Oral presentation proceedings for XIX IMEXO world congress of appliedMetrology, Lisbon, Portugal (2009).Viewed January 2010. http://www.imeko2009.it.pt/Papers/FP 229.pdf.
75. J. H. Kalivas and P. J. Gemperline, Calibration, in Practical Guide to Chemometrics, ed. P. J. Gemperline (CRC, FL, Taylor and Francis Group, Boca Raton 2006) Ch. 6, 105-166.
76. T. Naes, T. Isaksson, T. Fearn, and T. Davies, Non-linearity problems in calibration, in A User-Friendly Guide to Multivariate Calibration and Classification, NIR Publications, Chichester, UK, 2002, Ch. 9, 93-97.
77. M. Howard and J. Workman, Jr., Chemometrics in spectroscopy-Linearity in calibration: TheDurbin-Watson statistic. Spectroscopy Magazine, 20(3) (2005):34-40.
78. T. Naes, Multivariate calibration when data are split into subsets. Journal of Chemometrics 5 (1991):487-501.
79. F. Despagne and L. Massart, Neural networks in multivariate calibration. Analyst 123 (1998):157R-178R.
80. D. A. Burns and E.W. Ciurczak, Handbook of Near-Infrared Analysis (CRC Press, Taylor and Francis Group, Boca Raton, FL, 2007).
81. B. G. M. Vandegiste, D. L. Massart, L.M. C. Buydens, S. De Jong, P.J. Lewi, and J. Smeyers-Verbeke, Artificial neural Networks, in Handbook of Chemometrics and Qualimetrics: Part B, eds.B.G.M. Vandeginste and S.C. Rutan (Elsevier, Amsterdam, 1998), Ch. 44, 649-695.
82. V. Vapnik and A. Lerner, Pattern recognition using generalized portrait method. Automation and Remote Control 24 (1963):774-780.
83. H. Drucker, C. J.C. Burges, L. Kaufman, A. Smola, and V. Vapnik, Support vector regression machines. Advances in Neural Information Processing Systems 9 (1997):155-161.
84. M. Aizerman, E. Braverman, and L. Rozonoer, Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25 (1964):821-837.
85. O. Ivanciuc, Applications of support vector machines in chemistry. Reviews in Computational Chemistry 23 (2007):291-400.
86. S. Zomer, Classification with support vector machines. Homepage of Chemometrics (2004), , accessed October 2009.
87. K. P. Bennett and M. J. Embrechts, An optimization perspective on kernel partial least squares regression, in Advances in Learning Theory: Methods, Models and Applications NATO Science Series III:Computer&Systems Sciences, ed. J.A.K. Suykens, G. Horvath, S. Basu, C. Micchelli, and J. Vandewalle (IOS Press, Amsterdam, 2003), Vol. 190, 227-250.
88. T. Naes, T. Isaksson, T. Fearn, and T. Davies, Validation, in A User-Friendly Guide to Multivariate Calibration and Classification (NIR Publications, Chichester, UK, 2002), Ch. 13, 155-160.
89. T. Fearn, Assessing calibrations: SEP, RPD, RER, and R2. NIR News 13(6) (2002):12-14.
90. A. M. C. Davies and T. Fearn, Back to basics: Calibration statistics. Spectroscopy Europe 18(2) (2006):31-32.
91. C. N. G. Scotter, NIR techniques in cereal analysis, in Cereals and Cereal Products: Chemistry and Technology eds. D. A. V. Dendy and B. J. Dobrasky (Aspen Publishers, Gaithersburg, MD, 2001), Ch. 5, 90-98.
92. E. Bouveresse and B. Campbell, Transfer of multivariate calibration models based on near-infrared spectroscopy, in Handbook of Near Infrared Analysis eds. D. A. Burns and E.W. Ciurczak (CRC Press, Boca Raton, FL, 2007), Ch. 11, 231-244.