Use of Infrared Spectroscopy for In-Field Measurement and Phenotyping of Plant Properties: Instrumentation, Data Analysis, and Examples

References

• Sumner, L.W., Mendes, P., and Dixon, R.A. (2003) Plant metabolomics: Large-scale phytochemistry in the functional genomics era. Phytochemistry, 62: 817–836.
   PubMed Web of Science ®Google Scholar
• Woodcock, T., Downey, G., and O’Donnell, C.P. (2008) Better quality food and beverages: The role of near infrared spectroscopy. J. Near Infrared Spectros., 16: 1–29.
   Web of Science ®Google Scholar
• Karoui, R., Downey, G., and Blecker, Ch. (2010) Mid-infrared spectroscopy coupled with chemometrics: A tool for the analysis of intact food systems and the exploration of their molecular structure–quality relationships—A review. Chem. Rev., 110: 6144–6168.
   PubMed Web of Science ®Google Scholar
• Rodriguez-Saona, L.E. and Allendorf, M.E. (2011) Use of FTIR for rapid authentication and detection of adulteration of food.Annu. Rev. Food Sci. Tech., 2: 467–483.
   PubMed Web of Science ®Google Scholar
• McClure, F.W. (2004) 204 Years of near infrared technology: 1800–2003. J. Near Infrared Spectros., 11: 487–488.
   Web of Science ®Google Scholar
• Blanco, M. and Villaroya, I. (2002) NIR spectroscopy: A rapid-response analytical tool. Trends Anal. Chem., 21: 40–50.
   Web of Science ®Google Scholar
• Cozzolino, D. (2009) Near infrared spectroscopy in natural products analysis. Planta Med., 75: 746–757.
   PubMed Web of Science ®Google Scholar
• Cozzolino, D., Cynkar, W.U., Dambergs, R.G., and Gishen, M. (2006) Analysis of grape and wine by near infrared spectroscopy—A review. J. Near Infrared Spectros., 14: 279–289.
   Web of Science ®Google Scholar
• Huang, H., Yu, H., Xu, H., and Ying, Y. (2008) Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: A review. J Food Eng., 87: 303–313.
   Web of Science ®Google Scholar
• Nicolai, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I., and Lammertyn, J. (2007) Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46: 99–118.
   Web of Science ®Google Scholar
• Roggo, Y., Chalus, P., Maurer, L., Lema-Martinez, C., Edmond, A., and Jent, N. (2007) A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J. Pharm. Biomed. Anal., 44: 683–690.
   PubMed Web of Science ®Google Scholar
• McClure, W.F. (2003) 204 years of near infrared technology: 1800–2003. J. Near Infrared Spectros., 11: 487–518.
   Web of Science ®Google Scholar
• Nicolai, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I., and Lammertyn, J. (2007) Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Post Harvest Biol. Tech., 46: 99–118.
   Google Scholar
• Cozzolino, D., Cynkar, W.U., Dambergs, R.G., Shah, N., and Smith, P. (2009) Multivariate methods in grape and wine analysis. Int. J. Wine Research, 1: 123–130.
   Google Scholar
• Murray I., and Cowe, I. (2004) Sample preparation in near infrared spectroscopy in agriculture. In American Society of Agronomy, Roberts, C.A., Workman, J., and Reeves, J.B. Eds. Crop Science Society of America, Soil Science Society of America: Madison, Wisconsin, pp. 75–115.
   Google Scholar
• Smyth, H.E. and Cozzolino, D. (2011) Applications of infrared spectroscopy for quantitative analysis of volatile and secondary metabolites in plant materials. Current Bioactive Compounds 7: 66–74.
   Google Scholar
• Cozzolino, D. (2011) Infrared methods for high throughput screening of metabolites: Food and medical applications. Combinatorial Chemistry & High Throughput Screening, 14: 125–131.
   PubMed Web of Science ®Google Scholar
• Griffiths, P.R. and De Haseth, J.A. (1986) Fourier Transform Infrared Spectrometry. Wiley & Sons: New York.
   Google Scholar
• Hashimoto, A. and Kameoka, T. (2008) Applications of infrared spectroscopy to biochemical, food, and agricultural processes. Appl. Spectros. Rev., 43: 416–451.
   Web of Science ®Google Scholar
• Subramanian, A. and Rodrigez-Saona, L. (2009) Fourier transform infrared (FTIR) spectroscopy. In Infrared Spectroscopy for Food Quality Analysis and Control, Sun, D.W., Ed. Academic Press: Oxford, UK, pp. 146–174.
   Google Scholar
• Stratis, D.N., Eland, K.L., Carter, J.C., Tomlinson, S.J., and Angel, S.M. (2001) Comparison of acoustic-optic and liquid crystal tunable filters for laser induced breakdown spectroscopy. Appl. Spectros., 55: 999–1004.
   Web of Science ®Google Scholar
• Montes, J.M., Melchinger, A.E., and Reif, J.C. (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci., 12: 433–436.
   PubMed Web of Science ®Google Scholar
• Montes, J.M., Technow, F., Dhillon, B.S., Mauch, F., and Melchinger, A.E. (2011) High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crop. Res., 121: 268–273.
   Web of Science ®Google Scholar
• Lu, R. (2007) Quality evaluation of fruit by hyperspectral imaging. In Computer Vision Technology for Food Quality, Sun, D.W., Ed. Elsevier. , pp. 319–348.
   Google Scholar
• White, J.W., Andrade-Sanchez, P., Gore, M.A., Bronson, K.F., Coffelt, T.A., Conley, M.M., Feldmann, K.A., French, A.N., Heun, J., Hunsaker, D.J., Jenks, M.A., Kimball, B.A., Roth, R.L., Strand, R.J., Thorp, K.R., Wall, G.A., and Wang, G. (2012) Field-based phenomics for plant genetics research. Field Crop. Res., 133: 101–112.
   Web of Science ®Google Scholar
• McGoverin, C.M., Weeranantanaphan, J., Downey, G., and Manley, M. (2010) The application of near infrared spectroscopy to the measurement of bioactive compounds in food commodities. J. Near Infrared Spectros., 18: 87–111.
   Web of Science ®Google Scholar
• Lavine, B.K. (2006) Clustering and classification of analytical data. In Encyclopedia of Analytical Chemistry, Meyers, R.A., Ed. John Wiley & Sons. : Chichester, pp. 1–21.
   Google Scholar
• Gottlieb, D.M., Schultz, J., Bruun, S.W., Jacobsen, S., and Søndergaard, I. (2004) Multivariate approaches in plant science. Phytochemistry, 65: 1531–1548.
   PubMed Web of Science ®Google Scholar
• Cozzolino, D., Shah, N., Cynkar, W., and Smith, P. (2011) Technical solutions for analysis of grape juice, must and wine: The role of infrared spectroscopy and chemometrics. Anal. Bioanal. Chem., 401: 1479–1488.
   Web of Science ®Google Scholar
• Dardenne, P. (2010) Some considerations about NIR spectroscopy: Closing speech at NIR-2009. NIR News, 21 (8–9): . 8–14.
   Google Scholar
• Naes, T., Isaksson, T., Fearn, T., and Davies, T. (2002) A User-Friendly Guide to Multivariate Calibration and Classification. NIR Publications: Chichester, UK.
   Google Scholar
• Brereton, R.G. (2003) Chemometrics: Data Analysis for the Laboratory and Chemical Plant. John Wiley & Sons: Chichester, UK.
   Google Scholar
• Brereton, R.G. (2007) Applied Chemometrics for Scientist. John Wiley & Sons: Chichester, UK.
   Google Scholar
• Smyth, H. and Cozzolino, D. (2013) Instrumental methods (spectroscopy, electronic nose and tongue) as tools to predict taste and aroma in beverages: Advantages and limitations. Chem. Rev., 113: 1429–1440,
   PubMed Web of Science ®Google Scholar
• Cozzolino, D. (2012) Recent trends on the use of infrared spectroscopy to trace and authenticate natural and agricultural food products. Appl. Spectros. Rev., 47: 518–530.
   Web of Science ®Google Scholar
• Cozzolino, D. (2012) Benefits and limitations of infrared technologies in omic research and development of natural drugs and pharmaceutical products. Drug Dev. Res., 73: 504–512.
   Web of Science ®Google Scholar
• Agelet, L.E. and Hurburgh, Ch.R. (2010) A tutorial on near infrared spectroscopy and its calibration. Crit. Rev. Anal. Chem., 40: 246–260.
   Web of Science ®Google Scholar
• De Bei, R., Sullivan, W., Cynkar, W.U., Cozzolino, D., Dambergs, R.G., and Tyerman, S.D. (2011) Non destructive measurement of grapevine water potential using near infrared spectroscopy. Australian Journal of Grape and Wine Research, 17: 62–71.
   Web of Science ®Google Scholar
• Munns, R., James, R.A., Sirault, X.R.R., Furbank, R.T., and Jones, H.G. (2010) New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J. Exp. Bot., 61: 3499–3507.
   PubMed Web of Science ®Google Scholar
• García, J. and Cozzolino, D. (2006) Use of near infrared reflectance spectroscopy to predict chemical composition of forages from breeding programs. Agricultura Tecnica (Chile), 66: 41–48.
   Google Scholar
• Morón, A., Cozzolino, D., García, A., and Sawchik, J. (2007) Preliminary study on the use of near infrared reflectance spectroscopy to assess nitrogen content on undried wheat plants. J. Sci. Food Agr., 87: 142–157.
   Google Scholar
• Cabrera-Bosquet, L., Crossa, J., von Zitzewitz J., and Serret, M.D. (2012) High-throughput phenotyping and genomic selection: The frontiers of crop breeding converge. J. Integr. Plant Biol., 54: 312–320.
   PubMed Web of Science ®Google Scholar
• Walter, A., Studer, B., and Kolliker, R. (2012) Advanced phenotyping offers opportunities for improved breeding of forages and turfs species. Ann. Bot., 110: 1271–1279.
   PubMed Web of Science ®Google Scholar
• Cozzolino, D. and Labandera, M. (2002) Determination of dry matter and crude protein contents of undried forages by near infrared reflectance spectroscopy. J. Sci. Food Agr. 82: 380–384.
   Web of Science ®Google Scholar
• Fox, G.P., Bloustein, G., and Sheppard, J. (2010) On the go NIT technology to assess protein and moisture during harvest of wheat breeding trials. J Cereal Sci., 51: 171–173.
   Web of Science ®Google Scholar
• Long, D.S., Engel, R.E., and Siemens, M.C. (2008) Measuring grain protein concentration with in line near infrared reflectance spectroscopy. Agron. J., 100: 247–252.
   Web of Science ®Google Scholar
• Casler, M. and van Santen, E. (2010) Breeding objectives in forages. In Handbook of plant Breeding: Fodder Crops and Amenity Grasses, Boller, B., Posselt, U., and Veronesi, F., Eds. Springer Science and Business Media: New York, pp. 115–160.
   Google Scholar
• . Association of Official Analytical Chemists (AOAC). (1990) Official Methods of Analysis, 15th ed. AOAC: Arlington, Virginia.
   Google Scholar
• . Carbonero C.H., Mueller-Harvey I., Brown T.A., and Smith L. (2011) Sainfoin (Onobrychis viciifolia): A beneficial forage legume. Plant Genetic Resources–Characterization and Utilization 9: 70–85.
   Google Scholar
• Roberts, C.A., Joost, R.E., and Rottinghaus, G.E. (1997) Quantification of ergovalina in tall fescue by near infrared reflectance spectroscopy. Crop Sci., 37: 281–284.
   Web of Science ®Google Scholar
• Andres, S., Giraldez, F.J., Lopez, S., Mantecon, A.R., and Calleja, A. (2005) Nutritive evaluation of herbage from permanent meadows by near-infrared reflectance spectroscopy: 1. Prediction of chemical composition and in vitro digestibility. J. Sci. Food Agr. 85: 1564–1571.
   Web of Science ®Google Scholar
• Carbonero, C.H., Mueller-Harvey, I., Brown, T.A., and Smith, L. (2001) Sainfoin a beneficial forage legume. Plant Genetic Resources: Characterisation and Utilization, 9: 70–85.
   Google Scholar
• Ritsema, T. and Smeekens, S. (2003) Fructans: Beneficial for plants and humans. Curr. Opin. Plant Biol., 6: 223–230.
   PubMed Web of Science ®Google Scholar
• Livingston, D., Hincha, D., and Heyer, A. (2009) Fructan and its relationships to abiotic stress tolerance in plants. Cell. Mol. Life Sci., 66: 2007–2023.
   PubMed Web of Science ®Google Scholar
• Shetty, N. and Gislum, R. (2011) Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR. Field Crop. Res., 120: 31–37.
   Web of Science ®Google Scholar
• Sanada, Y., Takai, T., and Yamada, T. (2007) Inheritance of the concentration of water soluble carbohydrates and its relationship with the concentration of fibre and crude protein in herbage of cocksfoot (Dactylis glomerata). Grass and Forage Science, 62: 322–331.
   Web of Science ®Google Scholar
• Gergely, S. and Salgo, A. (2005) Changes in carbohydrate content during wheat-maturation—What is measured by near infrared spectroscopy? J. Near Infrared Spectros., 13: 9–17.
   Web of Science ®Google Scholar
• Jespersen, B.M. and Munck, L. (2009) Cereals and cereal products. In Infrared Spectroscopy for Quality Analysis and Control, Sun, D., Ed. Elsevier. : Oxford, pp. 275–320.
   Google Scholar
• Williams, P. (2010) The analysis of wheat by near infrared spectroscopy. Applications of vibrational spectroscopy to oilseeds analysis. In Applications of Vibrational Spectroscopy in Food Science, Li-Chan., E., Griffiths, P.R., and Chalmers, J.M., Eds. Wiley & Sons: Chichester, pp. 349–367.
   Google Scholar
• Himmelsbasch, D.S. (2010) The analysis of rice by vibrational spectroscopy. In Applications of Vibrational Spectroscopy in Food Science, Li-Chan., E., Griffiths, P.R., and Chalmers, J.M., Eds. Wiley & Sons: Chichester, pp. 387–397.
   Google Scholar
• Baranska, M., Schulz, H., Strehle, M., and Popp, J. (2010) Applications of vibrational spectroscopy to oilseeds analysis. In Applications of Vibrational Spectroscopy in Food Science. Li-Chan., E., Griffiths, P.R., and Chalmers, J.M., Eds. Wiley & Sons. : Chichester.
   Google Scholar
• Campbell, M.R., Sykes, J., and Glover, D.V. (2000) Classification of single and double-mutant corn endosperm genotypes by near-infrared transmittance spectroscopy. Cereal Chem., 77: 774–778.
   Web of Science ®Google Scholar
• Chambers, J. and Ridgeway, C. (1995) Rapid detection of contaminants in cereals. In Near Infrared Spectroscopy: The Future Waves, Davies, A.M.C. and Williams, P., Eds. NIR Publications: Chichester, UK, pp. 484–489.
   Google Scholar
• Ridgway, C. and Chambers, J. (1996) Detection of external and internal insect infestation in wheat by near-infrared reflectance spectroscopy. J. Sci. Food Agr., 71 (2): 251–264.
   Web of Science ®Google Scholar
• Ridgway, C. and Chambers, J. (1998) Detection of insects inside wheat kernels by NIR imaging. J. Near Infrared Spectros., 6 (1): 115–119.
   Google Scholar
• Singh, C.B., Jayas, D.S., Paliwal, J., and White, N.D.G. (2010) Identification of insect-damaged wheat kernels using shortwave near-infrared hyperspectral and digital colour imaging. Comput. Electron. Agr., 73 (2): 118–125.
   Web of Science ®Google Scholar
• Velasco, L., Pérez-Vich, B., and Fernandez-Martinez, J.M. (1999) Non-destructive screening for oleic and linoleic acid in single sunflower achenes by near infrared reflectance spectroscopy. Crop Sci., 39: 219–222.
   Web of Science ®Google Scholar
• Fassio, A. and Cozzolino, D. (2004) Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy. Ind. Crop. Prod., 20: 321–329.
   Web of Science ®Google Scholar
• Agelet, L.E., Armstrong, P.R., Romagosa Clariana, I., and Hurburgh, Ch.R. (2012) Measurement of single soybean seed attributes by near infrared technologies. A comparative study. J. Agr. Food Chem., 60: 8314–8322.
   PubMed Web of Science ®Google Scholar
• Yu, P., Doiron, K., and Liu, D. (2008) Shining light on the differences in molecular structural chemical make-up and the cause of distinct degradation behaviour between malting- and feed-type barley using synchrotron FTIR microspectroscopy: A novel approach. J. Agr. Food Chem., 56: 3417–3426.
   PubMed Web of Science ®Google Scholar
• Yu, P., Block, H.C., and Doiron, K. (2009) Understanding the differences in molecular conformation of carbohydrate and protein in endosperm tissues of grains with different biodegradation kinetics using advanced synchrotron technology. Spectrochim. Acta Mol. Biomol. Spectros., 71: 1837–1844.
   PubMed Web of Science ®Google Scholar
• Yu, P., Damiran, D., Azarfar, A., and Niu, Z. (2011) Detecting molecular features of spectra mainly associated with structural and non-structural carbohydrates in co-products from bioethanol production using DRIFT with uni- and multivariate molecular spectral analyses. Int. J. Mol. Sci., 12: 1921–1935.
   PubMed Web of Science ®Google Scholar
• Liu, N. and Yu, P. (2010) Characterization of the micro chemical structure of seed endosperm within a cellular dimension among six barley varieties with distinct degradation kinetics, using ultra spatially resolved synchrotron-based infrared micro spectroscopy. J. Agr. Food Chem., 58: 7801–7810.
   PubMed Web of Science ®Google Scholar
• Zhang, X. and Yu, P. (2012) Using ATR-FT/IR molecular spectroscopy to detect effects of blend DDGS inclusion level on the molecular structure spectral and metabolic characteristics of the proteins in hulless barley. Spectrochim. Acta Mol. Biomol. Spectros., 95: 53–63.
   PubMed Web of Science ®Google Scholar
• Huang, Z., Shab, S., Rong, Z., Chen, J., He, Q., Khan, D.M., and Zhua, S. (2013) Feasibility study of near infrared spectroscopy with variable selection for non-destructive determination of quality parameters in shell-intact cottonseed. Ind. Crop. Prod., 43: 654–660.
   Web of Science ®Google Scholar
• Liu, Z., Liu, B., Pan, T., and Yang, J. (2013) Determination of amino acid nitrogen in tuber mustard using near-infrared spectroscopy with waveband selection stability. Spectrochim. Acta Mol. Biomol. Spectros., 102: 269–274.
   PubMed Web of Science ®Google Scholar
• Ribeiro, L.F., Peralta-Zamora, P.G., Lameiro, B.H., Maia, N., Pereira Ramos, L., and Pereira-Netto, A.B. (2013) Prediction of linolenic and linoleic fatty acids content in flax seeds and flax seeds flours through the use of infrared reflectance spectroscopy and multivariate calibration. Food Res. Int., 51: 848–854.
   Web of Science ®Google Scholar
• Yu, P. (2005) Applications of hierarchical cluster analysis (CLA) and principal component analysis (PCA) in feed structure and feed molecular chemistry research, using synchrotron-based Fourier transform infrared (FTIR) micro spectroscopy. J. Agr. Food Chem., 53: 7115–7127.
   PubMed Web of Science ®Google Scholar
• Yu, P. (2007) Molecular chemical structure of barley proteins revealed by ultra-spatially resolved synchrotron light source FTIR micro spectroscopy: Comparison of barley varieties. Biopolymers, 85: 308–317.
   PubMed Web of Science ®Google Scholar
• Rossel, S.A., Hardy, C.L., Hurburgh, C.R., and Rippke, G.R. (2001) Application of near-infrared diffuse reflectance spectroscopy to the detection and identification of transgenic corn. Appl. Spectros., 55: 1425–1432.
   Google Scholar
• Rui, Y., Luo, Y., Haung, K., Wang, W., and Zhang, L. (2005) Discrimination of transgenic corns using NIR diffuse reflectance spectroscopy and back propagation (BP). Spectroscopy and Spectral Analysis, 25: 1581–1592.
   Web of Science ®Google Scholar
• Alishahi, H., Farahmand, H., Prieto, N., and Cozzolino, D. (2010) Identification of transgenic foods using NIR spectroscopy: A review. Spectrochim. Acta Mol. Biomol. Spectros., 75: 1–7.
   PubMed Web of Science ®Google Scholar
• Munck, L., Møller, B., Jacobsen, S., and Søndergaard, S. (2004) Near infrared spectra indicate specific mutant endosperm genes and reveal a new mechanism for substituting starch with (1/3, 1/4)-b-glucan in barley. J. Cereal Sci., 40: 213–222.
   Web of Science ®Google Scholar
• Munck, L., Pram Nielsen, J., Møller, B., Jacobsen, S., Søndergaard, I., Engelsen, S.B., Nørgaard, L., and Bro, R. (2001) Exploring the phenotypic expression of a regulatory proteome-altering gene by spectroscopy and chemometrics. Anal. Chim. Acta, 446: 171–186.
   Web of Science ®Google Scholar
• Hurburgh, C.R., Rippke, G.R., Heithoff, C., Roussel, S.A., and Hardy, C.L. (2000) Detection of genetically modified grains by near-infrared spectroscopy. Proceedings of PITTCON 2000-Science for the 21st Century, New Orleans, USA, March 12–17.
   Google Scholar
• Ahmed, F.E. (2002) Detection of genetically modified organisms in foods. Trends Biotechnol., 20: 215–223.
   PubMed Web of Science ®Google Scholar
• Lun, A.D., da Silva, A.P., Pinhoa, J.S.A., Ferré, J., and Boqué, R. (2013) Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy. Spectrochim. Acta A, 100: 115–119.
   Web of Science ®Google Scholar
• Sánchez, M.T., De la Haba, M.J., and Pérez-Marín, D. (2013) Internal and external quality assessment of mandarins on-tree and at harvest using a portable NIR spectrophotometer. Comput. Electron. Agr., 92: 66–74.
   Web of Science ®Google Scholar
• Mireei, S.A. and Sadeghi, M. (2013) Detecting bunch withering disorder in date fruit by near infrared spectroscopy. J. Food Eng., 114: 397–403.
   Web of Science ®Google Scholar
• Rotbart, N., Schmilovitch, Z., Cohen, Y., Alchanatis, V., Erel, R., Ignat, T., Shenderey, C., Dag, A., Yermiyahu, U. (2013) Estimating olive leaf nitrogen concentration using visible and near-infrared spectral reflectance. Biosystems Eng., 114: 426–434.
   Web of Science ®Google Scholar
• Salguero-Chaparro, L., Baeten, V., Abbas, O., and Peña-Rodríguez, F. (2012) On-line analysis of intact olive fruits by Vis-NIR spectroscopy: Optimisation of the acquisition parameters. J. Food Eng., 112: 152–157.
   Web of Science ®Google Scholar
• Salguero-Chaparro, L., Baeten, V., Abbas, O., and Peña-Rodríguez, F. (2013) Near infrared spectroscopy (NIRS) for on-line determination of quality parameters in intact olives. Food Chem., 139: 1121–1126.
   PubMed Web of Science ®Google Scholar
• Inácio, M.R., de Lima, K.M., Lopes, V.G., Pessoa, J.D., and de Almeida Teixeira, G.H. (2013) Total anthocyanin content determination in intact açaí (Euterpe oleracea Mart.) and palmitero-juçara (Euterpe edulis Mart.) fruit using near infrared spectroscopy (NIR) and multivariate calibration. Food Chem., 136: 1160–1164.
   PubMed Web of Science ®Google Scholar
• Sankaran, S., Mishra, A., Maja, J-M., and Ehsani, R. (2011) Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards. Comput. Electron. Agr., 77: 127–134.
   Web of Science ®Google Scholar
• Dowell, F.E., Maghirang, E.B., and Jayaraman, V. (2010) Measuring grain and insect characteristics using NIR laser array technology. Appl. Eng. Agr., 26: 165–169.
   Web of Science ®Google Scholar
• Pearson, T.C., Wicklow, D.T., Maghirang, E.B., Xie, F., and Dowell, F.E. (2001) Detecting aflatoxin in single corn kernels by transmittance and reflectance spectroscopy. Trans, ASAE, 44: 1247–1254.
   Google Scholar
• Peiris, K., Pumphrey, M.O., Dong, Y., Maghirang, E.B., Berzonsky, W., and Dowell, F.E. (2010) Near-infrared spectroscopic method for the identification of Fusarium head blight damage and prediction of deoxynivalenol in single wheat kernels. Cereal Chem., 87: 511–517.
   Web of Science ®Google Scholar
• Peiris, K.H.S., Pumphrey, M.O., and Dowell, F.E. (2009) NIR absorbance characteristics of deoxynivalenol and of sound and Fusarium-damaged wheat kernels. J. Near Infrared Spectros., 17: 213–221.
   Web of Science ®Google Scholar
• Ohnmacht, B., Hahn, V., and Pfitzner, C. (2012) Comparison of different near infrared spectrometers and sample presentations to estimate the protein and starch content in cereal grains during trial harvest. Journal fur Kulturpflanzen, 64: 237–249.
   Google Scholar
• Kolukisaoglu, U. and Thurow, K. (2010) Future and frontiers of automated screening in plant sciences. Plant Sci., 178: 476–484.
   Web of Science ®Google Scholar
• Zhu, J., Ingram, P.A., Benfey, P.N., and Elich, T. (2011) From lab to field, new approaches to phenotyping root system architecture. Curr. Opin. Plant Biol., 14: 310–317.
   PubMed Web of Science ®Google Scholar