Near-infrared spectroscopy data analysis

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. 

K. I. Hildrum, T. Isaksson, T. Naes and A. Tandburg (Eds.), Near-Infrared Spectroscopy Bridging the Gap between Data Analysis and NIR Applications, Ellis Horwood, New York, 1992. 

There are also more recent developments of other dual physio-chemical experimental methods. For example Durand et al (2006) presented a laboratory-made system that allows the coupling of dielectric analysis and Fourier-transform near-infrared spectroscopy (FT-NIR) to follow the cure of polyepoxy reactive systems. Complementary data are provided by the simultaneous dielectric analysis (the vitrification phenomenon) and near-infrared spectroscopic analysis (the extent of the reaction). 

In literature there are numerous applications of MR spectroscopy for the analysis of meat quality. One of the most important aim is to monitor the freshness of meat products. Sinelli et al. in 2010 investigated the ability of Near Infrared spectroscopy to follow meat freshness decay. PCA was applied by authors to the data and was able to discriminate samples on the basis of storage time and temperature. The modelling of PC scores versus time allowed the setting of the time of initial freshness decay for the samples (6-7 days at 4.3 °C, 2-3 days at 8.1 °C and less than 1 day at 15.5 °C). Authors reported that results showed the feasibility of MR for estimating quality decay of fresh minced beef during marketing. 

Downey, G., McIntyre, R, and Davies, A.N. Geographic classification of extra virgin olive oils from the eastern Mediterranean by chemometric analysis of visible and near infrared spectroscopic data. Applied Spectroscopy, 57, 158-163. 2003. 

Eschenauer, U., O. Henck, M. HUhne, P. Wu, I. Zebger, and H. W. Siesler. 1992. Near-infrared spectroscopy in chemical research, quality assurance and process control. In Near Infra- Red Spectroscopy Bridging the Gap between data Analysis and NIR Applications, eds. K. L. HUdrum, T. Isaksson, T. Naes, and A. Tandberg, pp. 11-18. New York EUis Horwood. 

Near-infrared spectroscopy proved valuable for fhe analysis of pharmaceutical powders in a 1981 paper by Becconsall et al. [119]. Near-infrared and UV phofoacoustic spectroscopy were used for determination of propranolol-magnesium carbonate mixtures. Spectra were collected from 1300 to 2600 nm with carbon black as the reference. An aromatic C-H combination band at 2200 nm and an overtone band at 1720 nm were used to quantify propranolol. In this case, the UV data were nonlinear, while the NIR method provided a linear calibration. 

Monfre, S.L. and F.A. DeThomas, Non-destructive pharmaceutical tablet assay by near-infrared spectroscopy. Near infrared spectroscopy Bridging the gap between data analysis and NIR applications, ed. K.I. Hildrum. Ellis-Horwood Chichester, UK (1992). 

Hansen MG, Khettry A. In-line monitoring of molten polymers near infrared spectroscopy, robnst probes, and rapid data analysis. Polym Eng Sci 1994 34 1758-1766. 

Near-infrared spectroscopy (NIRS) is also a rapid nondestructive technique, able to measure a large number of seed components in one rapid (about 2 min) analysis. It is a secondary technique. NIRS results are predicted as they are calculated from the calibration data sets. As for NMR or any secondary method, the chosen reference methods used to calibrate the instrument strongly affect the results obtained by the NIR method. 

W.R. Hruschka, Data analysis wavelength selection methods, pp. 35-55 in P.C. Williams and K. Norris, eds. Near-infrared Reflectance Spectroscopy. Am. Cereal Assoc., St. Paul MI, 1987. P. Geladi, D. McDougall and H. Martens, Linearization and scatter-correction for near-infrared reflectance spectra of meat. Appl. Spectrosc., 39 (1985) 491-500. 

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. 

Barnes, R. J., Dhanoa, M. S., Lister, S. J. Appl. Spectrosc. 43,1989, 772-777. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Barnes, R. J., Dhanoa, M. S., Lister, S. J. J. Near Infrared Spectrosc. 1, 1993, 185-186. Correction of the description of standard normal variate (SNV) and De-Trend transformations in practical spectroscopy with applications in food and beverage analysis. Brereton, R. G. Chemometrics—Data Analysis for the Laboratory and Chemical Plant. Wiley, Chichester, United Kingdom, 2006. 

The raw data plots and experience with near-infrared reflectance spectroscopy indicates the need for preprocessing to miniinize the effects of scatter. However, to illustrate the effect of preprocessing on the analysis, no preprocessing will be performed for the first pass through the data. ... 

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