Near-infrared spectral analysis

Martelo-Vidal, M. J., Dommguez-Agis, R, and Vazquez, M. 2013. Ultraviolet/visible/ near-infrared spectral analysis and chemometric tools for the discrimination of wines between subzones inside a controlled designation of origin A case study of Rias Baixas. Aust. J. Grape Wine R. 19 62-67. 

W. Windig and D.A. Stephenson, Self-modeling mixture analysis of second-derivative near-infrared spectral data using the Simplisma approach. Anal. Chem., 64 (1992) 2735-2742. 

R.C. Lyon, D.S. Lester, E.N. Lewis, E. Lee, L.X. Yu, E.H. Jefferson and A. S., Hussain, Near-infrared spectral imaging for quality assurance of pharmaceutical products analysis of tablets to assess powder blend homogeneity, AAPS Pharm. Sci. Tech., 3(3), 1-15 (2002). 

Near Infrared Reflectance Analysis (NIRA) is in use at over 5000 sites for the analysis of multiple constituents in food and other products. The technology is based upon correlation transform spectroscopy, which combines NIR spectrophotometry and computerized analysis of a "learning set" of samples to obtain calibrations without the need for detailed spectroscopic knowledge of factors being analyzed. The computer can obtain spectral characteristics of the analyte (based upon a correlation with data from an accepted reference analysis) without separation of the sample s constituents. 

Lyon, R.C. Lester, D.S. Lewis, E.N. Lee, E. Yu, L.X. Jefferson, E.H. 8t Hussain, A.S., Near-Infrared Spectral Imaging for Quality Assurance of Pharmaceutical Products Analysis of Tablets to Assess Powder Blend Homogeneity AAPS PharmSciTech. 2002, 3(3), article 17, 1-15. 

Lewis, E.N., Kidder, L.H., Lee, E. and Haber, K.S. (2005) Near-infrared spectral imaging with focal plane array detectors, in Spectrochemical Analysis Using Infrared Multichannel Detector (eds R. Bhargava and l.W. Levin), Blacl ell Publ. Ltd., Oxford, pp. 25-55. 

Chen, J. and Wang, X.Z. (2001) A new approach to near-infrared spectral data analysis using... 

Figure 7. Near-infrared spectral variation in surface sediments of Stor-Skdrtrdsket. The figure shows the spectral variance in the first principal component from a PCA-analysis based on 165 surface sediment samples. The bottom topography is presented as depth curves with a 2-m interval. Modified from Korsman et al. (1999).

Near-infrared reflectance analysis is a useful technique for characterizing textile raw materials, fiber, yarns, and fabrics. It is a nondestructive quantitative analysis that is simple to use and allows rapid testing of the sample. Its ability to measure multiple components of the sample simultaneously and eliminate extensive sample preparation are major advantages of NIRA in the characterization of textile materials. Many innovative mathematical treatments, for example, discriminant analysis and spectral reconstruction, have been developed by instrument manufactures and software companies. These instruments not only aid in the quantitative analysis of the data but also allow morphological investigations of fibers and yarns and rapid, qualitative identification of specific sample sets. 

The ATR accessory has revolutionized the ease of infrared spectral analysis of solids. For example, it is often unnecessary to make use of KBr pellets and Nujol mulls. The spectrum obtained with an ATR FT-IR is nearly identical to that obtained with an FT-IR operating in the transmittance mode. One may observe some differences in the relative intensities of the peaks, but the peak position in wavenumbers is identical in both modes. ATR FT-IR does not require a clear sample that allows light to pass through the sample, such as is common with transmittance instruments. There are some limitations with a diamond ATR instrument. Some materials such as coatings on metal and very dark samples do not analyze satisfactorily, but there are few other limitations. 

Fast Fourier Transformation is widely used in many fields of science, among them chemoractrics. The Fast Fourier Transformation (FFT) algorithm transforms the data from the "wavelength" domain into the "frequency" domain. The method is almost compulsorily used in spectral analysis, e, g., when near-infrared spectroscopy data arc employed as independent variables. Next, the spectral model is built between the responses and the Fourier coefficients of the transformation, which substitute the original Y-matrix. 

Samola and Urleb [15] reported qualitative and quantitative analysis of OTC using near-infrared (NIR) spectroscopy. Multivariate calibration was performed on NIR spectral data using principle component analysis (PCA), PLS-1, and PCR. 

Various spectral features can be used to derive the nitrogen abundance in dwarfs. Unfortunately weak high excitation (x=10.34 eV) near-infrared NI lines at 7468.31, 8216.34, 8683.4, 8703.25 and 8718.83 A disappear at metallicities [Fe/H] < -1 and for the analysis of N in metal-poor stars we are left with the CN and NH molecular bands at 3883 and 3360 A, respectively. It must be mentioned... 

Infrared spectra, of fats and oils, 10 823 Infrared spectral region, 19 564 Infrared spectroscopy, 14 224-243 23 136-143. See also Chromatography-infrared spectroscopy Far- infrared spectroscopy ir-selective surfaces Ir (infrared) spectroscopy Near- infrared spectroscopy Thermal analysis-infrared spectroscopy applications of, 14 239-240 23 140-141 in composition measurements, 20 682 in fiber optic fabrication, 11 138 industrial applications of, 14 240 instrumentation in, 14 225-228 23 137-138... 

---