Near-infrared hyperspectral imaging

The operation of an LCTF may be understood by considering a simplified Lyot filter stack, in which (N +1) polarizers are separated by N layers of liquid crystals sandwiched between birefringent crystals. The optical retardation, Rnm, introduced by birefringent crystals is dependent on the thickness of the crystal, tfnm, and the difference between the refractive index of the ordinary ray, and the extraordinary ray, tie, at the wavelength of interest  

The velocities of the extraordinary and ordinary ray differ, and these emerge from the stack with a phase delay, T radians, that is dependent upon the wavelength, X, of the radiation  

In a typical Lyot filter, crystals are often selected so that transmission has its maximum value at the wavelength determined by the thickest crystal retarder, with 

Other stages in the filter serving to block the transmission of unwanted wavelengths. The spectral region passed by the LCTF is dependent upon the choice of polarizer, the optical coating and the liquid crystal characteristics (nematic, cholesteric, smectic, etc.) In practice, a Lyot LCTF may have as many as 11 polarizers and 10 liquid crystalline layers, and is sometimes equipped with an internal microprocessor to tune all of the stages. 

The bandpass of a typical AOTF ranges from several nanometers to tens of nanometers for the visible and NIR spectral regions. This resolution is suitable for fluorescence spectroscopy where bands are very broad, and is just adequate for Raman hyperspectral imaging, albeit with lower resolution than may be achieved with a monochromator (see Section 1.5). A NIR spectrometer based on an AOTF has also been sold commercially. However, the transmission of these devices for 

Wavelength scanning in NIR hyperspectral imaging spectrometers has also been accomplished with both a scanning monochromator and an acousto-optic tunable filter (AOTF). AOTFs are electro-optical devices that function as electronically tunable filters. They rely on a birefringent crystal, the optical properties of which vary upon interaction of the crystal with an acoustic wave. The resulting 

The NIR hyperspectral imaging spectrometers marketed by Malvern Instruments are equipped with a Stirling-engine-cooled indium antimonide (InSb) FPA detector with 320 pixels in one dimension and 256 pixels in the other (total 81920 

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Lu, R. (2003) Detection of bruises on apples using near-infrared hyperspectral imaging. Trans. ASAE 46(2), 523-30. 

Codgill, R. P, Hurburgh, C. R. Jr, Jensen, T. C. and Jones, R. W. (2002) Single-kernel maize analysis by near-infrared hyperspectral imaging, Proceedings of the 10th International Conference on Near-Infrared Spectroscopy, 2001 (A. M. C. Davies and R. K. Cho, eds), Korea, pp. 243-7. 

Near-Infrared Hyperspectral Imaging in Food and Agricultural Science... 

While Mahesh et al. (2011) used near-infrared hyperspectral images (wavelength range 960-1700 nm), applied to a bulk samples, to classify the moisture levels (12,14,16,18, and 20%) on the wheat. Principal components analysis (PCA) was used to identify the region (1260-1360 nm) with more information. The linear and quadratic discriminant analyses (LDA) and quadratic discriminant analysis (QDA) could classify the sample based on moisture contents than also identifying specific moisture levels with a god levels of accuracy (61- 100% in several case). Spectral features at key wavelengths of 1060, 1090, 1340, and 1450 nm were ranked at top in classifying wheat classes with different moisture contents. 

Manley et al. (2011) used near infrared hyperspectral imaging combined with chemometrics techniques for tracking diffusion of conditioning water in single wheat kernels of different hardnesses. NIR analysers is a commonly, non-destructive, non-contact and fast solution for quality control, and a used tool to detect the moisture-content of carrot samples during storage but Firtha (2009) used hyperspectral system that is able to detect the spatied... 

Barbin, D. Elmasry, G. Sun, D.W. Allen, P. (2011). Near-infrared hyperspectral imaging for grading and classification of pork. Meat Science. Article in press Basilevsk, A. (1994) Statistical factor analysis and related methods theory and applications. Wiley-Interscience Publication. ISBN 0-471-57082-6... 

Subbiah, J., C.R. Calkins, A. Samal, and G.E. Meyer. 2008. Visible/near-infrared hyperspectral imaging for beef tenderness prediction. Journal of Computers and Electronics in Agriculture. 64 225-233. 

Vermeulen, E, Fernandez Pierna, J.A., van Egmond, H.P., Dardenne, P., and Baeten, V. (2011) Online detection and quantification of ergot bodies in cereals using near infrared hyperspectral imaging. Food Addit Contam. Part A Chem. Anal Control Exposure Risk Assess., 29, 232-240,... 

Delwiche, S.R., Souza, E.J., and Kim, M.S. (2013) Limitations of single kernel near-infrared hyperspectral imaging of soft wheat for milling quality. Biosyst. Eng, 115 (3), 260-273. 

Barbin, D.F. et al. (2013) Nondestructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Ghent, 138 (2-3), 1162-1171. 

Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. ). Food Eng.,... 

Kamruzzaman, M. et al (2012) Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Ami Chim. Acta, 714, 57 -67. 

Mora, C.R. etoL (2011) Determination of basic density and moisture content of loblolly pine wood disks using a near infrared hyperspectral imaging system. J. Near Infrared Spectrosc., 19 (5), 401-409. 

Thumm, A. et al. (2010) Near infrared hyperspectral imaging applied to mapping chemical composition in wood... 

Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innovative Food Sci. Emerg. TechnoL, 18, 237—245. 

Wu, D. and Sun, D.W. (2013) Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. Talanta, 116, 266—276. 

Manley, M. et al (2009) Near infrared hyperspectral imaging for the evaluation of endosperm texture in whole yellow maize (Zea maize L.) Kernels. J. Agric. Food Chem., 57 (19), 8761-8769. 

P. (2011) Tracking diffusion of conditioning water in single wheat kernels of different hardnesses by near infrared hyperspectral imaging. Anal. Chim. Acta, 686 (1 -2), 64-75. 

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