这是indexloc提供的服务，不要输入任何密码
Skip to main content
Springer Nature Link
Log in

Extraction of Spectral Information from Hyperspectral Data and Application of Hyperspectral Imaging for Food and Agricultural Products

  • Review
  • Published:
Food and Bioprocess Technology Aims and scope Submit manuscript

Abstract

Hyperspectral imaging is built with the aggregation of imaging, spectroscopy and radiometric techniques. This technique observes the sample behaviour when it is exposed to light and interprets the properties of the biological samples. As hyperspectral imaging helps in interpreting the sample at the molecular level, it can distinguish very minute changes in the sample composition from its scatter properties. Hyperspectral data collection depends on several parameters such as electromagnetic spectrum wavelength range, imaging mode and imaging system. Spectral data acquired using a hyperspectral imaging system contain variations due to external factors and imaging components. Moreover, food samples are complex matrices with conditions of surface and internal heterogeneities, which may lead to variations in acquired data. Hence, before extracting information, these variations and noises must be reduced from the data using reference-dependent or reference-independent spectral pre-processing techniques. Using of the entire hyperspectral data for information extraction is tedious and time-consuming. In order to overcome this, exploratory data analysis techniques are used to select crucial wavelengths from the excessive hyperspectral data. Using appropriate chemometric techniques (supervised or unsupervised learning techniques) on this pre-processed hyperspectral data, qualitative or quantitative information from sample can be obtained. Qualitative information for analysing of the chemical composition, detecting of the defects and determining the purity of the food product can be extracted using discriminant analysis techniques. Quantitative information including variation in chemical constituents and contamination levels in food and agricultural sample can be extracted using categorical regression techniques. In combination with appropriate spectra pre-processing and chemometric technique, hyperspectral imaging stands out as an advanced quality evaluation system for food and agricultural products.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  • Akbari H., Halig L.V., Zhang H., Wang D., Chen Z.G. & Fei B. (2012) Detection of cancer metastasis using a novel macroscopic hyperspectral method. In: Proceedings of the SPIE Volume 8317 Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 23 March 2012, Paper No. 831711, San Diego, California, USA.

  • Albers, B., DiBenedetto, J., Lutz, S., & Purdy, C. (1995). More efficient environmental monitoring with laser-induced fluorescence imaging. Biophotonics International Magazine, 2(6), 42–54.

    Google Scholar 

  • Amigo, J. M., & Ravn, C. (2009). Direct quantification and distribution assessment of major and minor components in pharmaceutical tablets by NIR-chemical imaging. European Journal of Pharmaceutical Sciences, 37(2), 76–82.

    Article  CAS  Google Scholar 

  • Amigo, J. M. (2010). Practical issues of hyperspectral imaging analysis of solid dosage forms. Analytical Bioanalytical Chemistry, 398(1), 93–109.

    Article  CAS  Google Scholar 

  • Amigo, J. M., Cruz, J., Bautista, M., Maspoch, S., Coello, J., & Blanco, M. (2008). Study of pharmaceutical samples by NIR chemical-image and multivariate analysis. Trends in Analytical Chemistry, 27(8), 696–713.

    Article  CAS  Google Scholar 

  • Anderson, N. M., & Walker, P. N. (2003). Measuring fat content of ground beef stream using on-line visible/NIR spectroscopy. Transactions of the ASAE, 46(1), 117–124.

    Article  Google Scholar 

  • Andersson, C. A. (1999). Direct orthogonalization. Chemometrics and Intelligent Laboratory Systems, 47(1), 51–63.

    Article  CAS  Google Scholar 

  • Anonymous (2013) Hyperspectral imaging. Hyperspectral imaging: components and systems catalog, Middleton Research, Middleton, WI, USA, Available at: www.middletonresearch.com/pdfs/3-HSI.pdf. Accessed 21 November 2013.

  • Ariana, D. P., & Lu, R. (2010). Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. Journal of Food Engineering, 96(4), 583–590.

    Article  Google Scholar 

  • Ariana, D. P., Lu, R., & Guyer, D. E. (2006). Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers. Computers and Electronics in Agriculture, 53(1), 60–70.

    Article  Google Scholar 

  • Baker, J. E., Dowell, F. E., & Throne, J. E. (1999). Detection of parasitized rice weevils in wheat kernels with near-infrared spectroscopy. Biological Control, 16, 88–90.

    Article  Google Scholar 

  • Baker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166–173.

    Article  CAS  Google Scholar 

  • Ballabio D & Todeschini R (2009) Multivariate classification for qualitative analysis. Sun Infrared Spectroscopy for Food Quality Analysis and Control, pp 83–104. Academic press, Burlington, USA.

  • Baranowski, P., Mazurek, W., Wozniak, J., & Majewska, U. (2012). Detection of early bruises in apples using hyperspectral data and thermal imaging. Journal of Food Engineering, 110(3), 345–355.

    Article  Google Scholar 

  • Barbin, D., ElMasry, G., Sun, D.-W., & Allen, P. (2012). Near-infrared hyperspectral imaging for grading and classification of pork. Meat Science, 90(1), 259–268.

    Article  Google Scholar 

  • Barbin, D. F., ElMasry, G., Sun, D.-W., & Allen, P. (2013). Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chemistry, 138(2–3), 1162–1171.

  • Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43(5), 772–777.

    Article  CAS  Google Scholar 

  • Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., & Herppich, W. B. (2011). Early detection of Fusarium infection in wheat using hyperspectral imaging. Computers & Electronics in Agriculture, 75(2), 304–312.

    Article  Google Scholar 

  • Beghi, R., Spinardi, A., Bodria, L., Mignani, I., & Guidetti, R. (2013). Apples nutraceutic properties evaluation through a visible and near-infrared portable system. Food and Bioprocess Technology, 6(9), 2547–2554.

    Article  CAS  Google Scholar 

  • Borengasser, M., Hungate, W. S., & Watkins, R. (2007). Hyperspectral remote sensing: principles and applications. Boca Raton, USA: CRC Press.

    Google Scholar 

  • Botchko V, Berina E, Korotkaya Z, Parkkinen J & Jaaskelainen, T (2003) Independent component analysis in spectral images. 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA 2003), 1–4 April 2003, Nara, Japan.

  • Boysworth, M. K., & Booksh, K. S. (2007). Aspects of multivariate calibration applied to near-infrared spectroscopy. In Ciurczak & Burns (Eds.), Handbook of near-infrared analysis (3rd ed., pp. 207–229). Boca Raton, USA: CRC Press.

    Google Scholar 

  • Brosnan, T., & Sun, D.-W. (2004). Improving quality inspection of food products by computer vision-a review. Journal of Food Engineering, 61(1), 3–16.

    Article  Google Scholar 

  • Brown, C. D., Vega-Montoto, L., & Wentzell, P. D. (2000). Derivatives preprocessing and optimal corrections for baseline drift in multivariate calibration. Applied Spectroscopy, 54(7), 1055–1068.

    Article  CAS  Google Scholar 

  • Buddenbaum, H., & Steffens, M. (2012). The effect of spectral pretreatments on chemometrics analyses of soil profile using laboratory imaging spectroscopy. Applied and Environmental Soil Science, Article ID, 274903, 1–12.

    Article  CAS  Google Scholar 

  • Bulanon, D. M., Burks, T. F., Kim, D. G., & Ritenour, M. A. (2013). Citrus black spot detection using hyperspectral image analysis. Agricultural Engineering International: CIGR Journal, 15(3), 171–180.

    Google Scholar 

  • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167.

    Article  Google Scholar 

  • Centner, V., Massart, D. L., & de Noord, O. E. (1996). Detection of inhomogeneities in sets of NIR spectra. Analytica Chimica Acta, 330(1), 1–17.

    Article  CAS  Google Scholar 

  • Du, C., Ma Z., Zhou J., Goyne K.W. (2013). Application of mid-infrared photoacoustic spectroscopy inmonitoring carbonate content in soils. Sensors and Actuators B, 188, 1167–1175.

  • Chappelle, E. W., McMurtrey III, J. E., & Kim, M. S. (1991). Identification of the pigment responsible for the blue fluorescence band in laser induced fluorescence (LIF) spectra of green plants, and the potential use of this band in remotely estimating rates of photosynthesis. Remote Sensing of Environment, 36(3), 213–218.

    Article  Google Scholar 

  • Chappelle, E. W., Wood, F. M., McMurtrey III, J. E., & Newcomb, W. W. (1984). Laser induced fluorescence of green plants. 1: a technique for the remote detection of plant stress and species differentiation. Applied Optics, 23(1), 134–138.

    Article  CAS  Google Scholar 

  • Chen, Y.-N., Sun, D.-W., Cheng, J.-H., & Gao, W.-H. (2016). Recent advances for rapid identification of chemical information of muscle foods by hyperspectral imaging analysis. Food Engineering Reviews, 8(3), 336–350.

    Article  CAS  Google Scholar 

  • Chen, Y.-R., Chao, K., & Kim, M. S. (2002). Machine vision technology for agricultural applications. Computers and Electronics in Agriculture, 36(2–3), 173–191.

    Article  Google Scholar 

  • Chen, Z. P., Morris, J., & Martin, E. (2006). Extracting chemical information from spectral data with multiplicative light scattering effects by optical path-length estimation and correction. Analytical Chemistry, 78(22), 7674–7681.

    Article  CAS  Google Scholar 

  • Cheng, W., Sun, D.-W., & Cheng, J.-H. (2016). Pork biogenic amine index (BAI) determination based on chemometric analysis of hyperspectral imaging data. LWT-Food Science and Technology, 73, 13–19.

    Article  CAS  Google Scholar 

  • Cheng, X., Chen, Y. R., Tao, Y., Wang, C. Y., Kim, M. S., & Lefcourt, A. M. (2004). A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection. Transactions of the ASAE, 47(4), 1313–1320.

    Article  Google Scholar 

  • Cho, B.-K., Kim, M. S., Baek, I.-S., Kim, D.-Y., Lee, W.-H., Kim, J., Bae, H., & Kim, Y.-S. (2013). Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery. Postharvest Biology and Technology, 76, 40–49.

    Article  Google Scholar 

  • Choudhary, R., Mahesh, S., Paliwal, J., & Jayas, D. S. (2009). Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosystems Engineering, 102(2), 115–127.

    Article  Google Scholar 

  • Clark, D., & Sasic, S. (2006). Chemical images: technical approaches and issues. Cytometry Part A, 69A(8), 815–824.

    Article  CAS  Google Scholar 

  • Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E., & Nicholson, J. (2005). Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Analytical Chemistry, 77(5), 1282–1289.

    Article  CAS  Google Scholar 

  • Cogdill, R. P., Hurburgh Jr., C. R., Rippke, G. R., Bajic, S. J., Jones, R. W., McClelland, J. F., Jensen, T. C., & Liu, J. (2004). Single-kernel maize analysis by near-infrared hyperspectral imaging. Transactions of the ASAE, 47(1), 311–320.

    Article  Google Scholar 

  • Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314.

    Article  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20(3), 273–297.

    Google Scholar 

  • Dacal-Nieto A, Formella A, Carrión P, Vazquez-Fernandez E & Fernández-Delgado M (2011) Non-destructive detection of hollow heart in potatoes using hyperspectral Imaging. In: Berciano et al. (eds) Proceedings of the 14th International Conference on Computer Analysis of Images and Pattern (CAIP 2011), Part II, 29–31 August 2011, pp 180–187, Seville, Spain.

  • De Temmerman, J., Saeys, W., Nicolai, B., & Ramon, H. (2007). Near infrared reflectance spectroscopy as a tool for the in-line determination of the moisture concentration in extruded semolina pasta. Biosystems Engineering, 97(3), 313–321.

    Article  Google Scholar 

  • Delwiche, S. R., Kim, M. S., & Dong, Y. (2011). Fusarium damage assessment in wheat kernels by Vis/NIR hyperspectral imaging. Sensing and Instrumentation for Food Quality and Safety, 5(2), 63–71.

    Article  Google Scholar 

  • Dhanoa, M. S., Lister, S. J., Sanderson, R., & Barnes, R. J. (1994). The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. Journal of Near Infrared Spectroscopy, 2(1), 43–47.

    Article  CAS  Google Scholar 

  • Ding C & He X (2004) K-means clustering via principal component analysis. In: Brodley (ed) Proceedings of the 21st International Conference on Machine Learning, 4–8 July 2004, Banff, Canada.

  • Donovan, J. J., Snyder, D. A., & Rivers, M. L. (1993). An improved interference correction for trace element analysis. Microbeam Analysis, 2, 23–28.

    CAS  Google Scholar 

  • Dowell, F. E. (2000). Differentiating vitreous and nonvitreous of durum wheat kernels by using near-infrared spectroscopy. Cereal Chemistry, 77(2), 155–158.

    Article  CAS  Google Scholar 

  • Du, C.-J., & Sun, D.-W. (2004). Recent developments in the applications of image processing techniques for food quality evaluations. Trends in Food Science & Technology, 15(5), 230–249.

    Article  CAS  Google Scholar 

  • Du H, Qi H, Wang X, Ramanath R & Snyder, WE (2003) Band selection using independent component analysis for hyperspectral image processing. In: Proceedings of the 32nd Applied Imagery Pattern Recognition Workshop, 15–17 October 2003, Washington, USA.

  • Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd edition). New York, USA: Wiley–Interscience.

    Google Scholar 

  • ElMasry, G. & Sun D.-W. (2010) Principles of hyperspectral imaging technology. In: Sun Hyperspectral Imaging for Food Quality Analysis and Control (1st edition), pp 3–43. Academic Press, London, UK.

  • ElMasry, G., Iqbal, A., Sun, D.-W., Allen, P., & Ward, P. (2011). Quality classification of cooked, sliced Turkey hams using NIR hyperspectral imaging system. Journal of Food Engineering, 103(3), 333–344.

    Article  Google Scholar 

  • ElMasry, G., Kamruzzaman, M., Sun, D.-W., & Allen, P. (2012a). Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Critical Reviews in Food Science and Nutrition, 52(11), 999–1023.

    Article  Google Scholar 

  • ElMasry, G., Sun, D.-W., & Allen, P. (2012b). Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food Engineering, 110(1), 127–140.

    Article  Google Scholar 

  • ElMasry, G., Wang, N., ElSayed, A., & Ngadi, M. (2007). Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering, 81(1), 98–107.

    Article  CAS  Google Scholar 

  • Eluyode, O. S., & Akomolafe, D. T. (2013). Comparative study of biological and artificial neural networks. European Journal of Applied Engineering and Scientific Research, 2(1), 36–46.

    Google Scholar 

  • Feng, Y.-Z., & Sun, D.-W. (2012). Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms. Talanta, 105, 244–249.

    Article  CAS  Google Scholar 

  • Fernández-Ahumada, E., Garrido-Varo, A., & Guerrero-Ginel, J. E. (2008). Feasibility of diode-array instruments to carry near-infrared spectroscopy from laboratory to feed process control. Journal of Agricultural and Food Chemistry, 56(9), 3185–3192.

    Article  CAS  Google Scholar 

  • Firrao, G., Torelli, E., Gobbi, E., Raranciuc, S., Bianchi, G., & Locci, R. (2010). Prediction of milled maize fumonisin contamination by multispectral image analysis. Journal of Cereal Science, 52(2), 327–330.

    Article  CAS  Google Scholar 

  • Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.

    Article  Google Scholar 

  • Fletcher JT & Kong SG (2003) Principal component analysis for poultry tumor inspection using hyperspectral fluorescence imaging. In Proceedings of the IEEE International Joint Conference on Neural Networks, Volume 1, 20–24 July 2003, pp 149–153, Portland, USA.

  • Foca, G., Salvo, D., Cino, A., Ferrari, C., Lo Fiego, D. P., Minelli, G., & Ulrici, A. (2013). Classification of pig fat samples from different subcutaneous layers by means of fast and non-destructive analytical techniques. Food Research International, 52(1), 185–197.

    Article  CAS  Google Scholar 

  • Fong AY & Wachman E (2008) Advanced photonic tools for hyperspectral imaging for life sciences. SPIE Newsroom: Electronic Imaging & Signal Processing, Available at: https://spie.org/documents/Newsroom/Imported/1051/1051-2008-03-20.pdf. Accessed 29 November 2013.

  • Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131–163.

    Article  Google Scholar 

  • Geladi, P., MacDougall, D., & Martens, H. (1985). Linearization and scatter-corrections for near-infrared reflectance spectra of meat. Applied Spectroscopy, 39(3), 491–500.

    Article  Google Scholar 

  • Ghosh, P. K., Jayas, D. S., Gruwel, M. L. H., & White, N. D. G. (2007). A magnetic resonance imaging study of wheat drying kinetics. Biosystems Engineering, 97(2), 189–199.

    Article  Google Scholar 

  • Givens, D. I., De Boever, J. L., & Deaville, E. R. (1997). The principles, practices and some future applications of near infrared spectroscopy for predicting the nutritive value of foods for animals and humans. Nutrition Research Reviews, 10(1), 83–114.

    Article  CAS  Google Scholar 

  • Goetz, A. F. H. (2009). Three decades of hyperspectral remote sensing of the earth: a personal review. Remote sensing of environment, 113. Supplement, 1, S5–S16.

    Google Scholar 

  • Goetz, A. F. H., Vane, G., Solomon, J. E., & Rock, B. N. (1985). Imaging spectrometry for earth remote sensing. Science, 228(4704), 1147–1153.

    Article  CAS  Google Scholar 

  • Golic, M., & Walsh, K. B. (2006). Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content. Analytica Chimica Acta, 555(2), 286–291.

    Article  CAS  Google Scholar 

  • Gorretta, N., Roger, J., Aubert, M., Bellon-Maurel, V., Campan, F., & Roumet, P. (2006). Determining vitreousness of durum wheat kernels using near infrared hyperspectral imaging. Journal of Near Infrared Spectroscopy, 14(1), 231–239.

    Article  CAS  Google Scholar 

  • Gou, P., Santos-Garcés, E., Høy, M., Wold, J. P., Liland, K. H., & Fulladosa, E. (2013). Feasibility of NIR interactance hyperspectral imaging for on-line measurement of crude composition in vacuum packed dry-cured ham slices. Meat Science, 95(2), 250–255.

    Article  CAS  Google Scholar 

  • Gowen, A. A., O’Donnell, C. P., Cullen, P. J., Downey, G., & Frias, J. M. (2007). Hyperspectral imaging—an emerging process analytical tool for food quality and safety control. Trends in Food Science and Technology, 18(12), 590–598.

    Article  CAS  Google Scholar 

  • Gowen, A. A., Taghizadeh, M., & O’Donnell, C. P. (2009). Identification of mushrooms subjected to freeze damage using hyperspectral imaging. Journal of Food Engineering, 93(1), 7–12.

    Article  Google Scholar 

  • Gracia, A., & León, L. (2011). Non-destructive assessment of olive fruit ripening by portable near infrared spectroscopy. Grasas y Aceites, 62(3), 268–274.

    Article  CAS  Google Scholar 

  • Guo, G., Li, S. Z., & Chan, K. L. (2000). Support vector machines for face recognition. Image and Vision Computing, 19(9–10), 631–638.

    Google Scholar 

  • Hand, D. J. (1997). Construction and assessment of classification rules. New York, USA: John Wiley and Sons.

    Google Scholar 

  • Hans, B.-P. (2003). Analysis of water in food by near infrared spectroscopy. Food Chemistry, 82(1), 107–115.

    Article  CAS  Google Scholar 

  • Harris, P. J., & Hartley, R. D. (1976). Detection of bound ferulic acid in cell walls of the Gramineae by ultraviolet fluorescence microscopy. Nature, 259, 508–510.

    Article  CAS  Google Scholar 

  • Heia, K., Sivertsen, A. H., Stormo, S. K., Elvevoll, E., Wold, J. P., & Nilsen, H. (2007). Detection of nematodes in cod (Gadus morhua) fillets by imaging spectroscopy. Journal of Food Science, 72(1), E011–E015.

    Article  CAS  Google Scholar 

  • Heraud, P., Wood, B. R., Beardall, J., & McNaughton, D. (2006). Effects of pre-processing of Raman spectra on in vivo classification of nutrient status of microalgal cells. Journal of Chemometrics, 20(5), 193–197.

    Article  CAS  Google Scholar 

  • Herault J & Jutten C (1986) Space or time adaptive signal processing by neural network models. In: Proceedings of the American Institute of Physics Conference, Neural Networks for Computing, 13–16 April 1986, Snowbird, USA.

  • Hottelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Education Psychology, 24(6), 417–441.

    Article  Google Scholar 

  • Hu, K., & Wang, D. (2011). Unvoiced speech segregation from nonspeech interference via CASA and spectral subtraction. IEEE Transactions on Audio, Speech, and Language Processing, 19(6), 1600–1609.

    Article  Google Scholar 

  • Huang, M., & Lu, R. (2010). Optimal wavelength selection for hyperspectral scattering prediction of apple firmness and soluble solids content. Transactions of the ASABE, 53(4), 1175–1182.

    Article  Google Scholar 

  • Huang, M., Zhu, Q., Wang, B., & Lu, R. (2012a). Analysis of hyperspectral scattering images using locally linear embedding algorithm for apple mealiness classification. Computers and Electronics in Agriculture, 89, 175–181.

    Article  Google Scholar 

  • Huang, Y. B., Thomson, S. J., Molin, W. T., Reddy, K. N., & Yao, H. B. (2012b). Early detection of soybean plant injury from glyphosate by measuring chlorophyll reflectance and fluorescence. Journal of Agricultural Science, 4(5), 117–124.

    Article  Google Scholar 

  • Iqbal, A., Sun, D.-W., & Allen, P. (2013). Prediction of moisture, color and pH in cooked, pre-sliced Turkey hams by NIR hyperspectral imaging system. Journal of Food Engineering, 117(1), 42–51.

    Article  CAS  Google Scholar 

  • Isaksson, T., & Kowalski, B. (1993). Piece-wise multiplicative scatter correction applied to near-infrared diffuse transmittance data from meat products. Applied Spectroscopy, 47(6), 702–709.

    Article  CAS  Google Scholar 

  • Jennrich RJ (1977) Stepwise discriminant analysis. In: Enslein et al (eds) Statistical Methods for Digital Computers, Wiley and Sons, New York, USA.

  • Jensen JR (2007) Introductory to digital image processing: a remote sensing perspective. Prentice Hall Series in Geographic Information Science.

  • Jiang, L., Zhu, B. & Tao, Y. (2010) Hyperspectral image classification methods. In: Sun (ed) Hyperspectral Imaging for Food Quality Analysis and Control (1st edition), pp 79–98, Academic Press is an imprint of Elsevier, London, UK.

  • Jun, W., Kim, M. S., Cho, B.-K., Millner, P. D., Chao, K., & Chan, D. E. (2010). Microbial biofilm detection on food contact surfaces by macro-scale fluorescence imaging. Journal of Food Engineering, 99(3), 314–332.

    Article  Google Scholar 

  • Kaliramesh, S., Chelladurai, V., Jayas, D. S., Alagusundaram, K., White, N., & Fields, P. (2013). Detection of infestation by Callosobruchus maculatus in mung bean using near-infrared hyperspectral imaging. Journal of Stored Products Research, 52, 107–111.

    Article  Google Scholar 

  • Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2011). Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of Food Engineering, 104(3), 332–340.

    Article  Google Scholar 

  • Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2012). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovative Food Science and Emerging Technologies, 16, 218–226.

    Article  CAS  Google Scholar 

  • Kamruzzaman, M., Sun, D.-W., ElMasry, G., & Allen, P. (2013). Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis. Talanta, 103, 130–136.

    Article  CAS  Google Scholar 

  • Karstang, T. V., & Manne, R. (1992). Optimized scaling: a novel approach to linear calibration with closed data sets. Chemometrics and Intelligent Laboratory Systems, 14(1–3), 165–173.

    Article  CAS  Google Scholar 

  • Karunakaran, C., Jayas, D. S., & White, N. D. G. (2003). Soft X-ray inspection of wheat kernels infested by Sitophilus oryzae. Transactions of the ASAE, 46(3), 739–745.

    Article  Google Scholar 

  • Kavdir, I., Buyukcan, M. B., Lu, R., Kocabiyik, H., & Seker, M. (2009). Prediction of olive quality using FT-NIR spectroscopy in reflectance and transmittance modes. Biosystems Engineering, 103(3), 304–312.

    Article  Google Scholar 

  • Kellicut, D., Weiswasser, J., Arora, S., Freeman, J., Lew, R., Shuman, C., Mansfield, J. R., & Sidewy, A. N. (2004). Emerging technology: hyperspectral imaging. Perspectives in Vascular Surgery and Endovascular Therapy, 16(1), 53–57.

    Article  Google Scholar 

  • Kemps, B., Leon, L., Best, S., De Baerdemaeker, J., & Ketelaere, D. (2010). Assessment of the quality parameters in grapes using VIS/NIR spectroscopy. Biosystems Engineering, 105(4), 507–513.

    Article  Google Scholar 

  • Keulemans, W., Delalieux, S., Aardt, J., & Coppin, P. (2007). Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral analysis: nonparametric statistical approaches and physiological implications. European Journal of Agronomy, 27(1), 130–143.

    Article  Google Scholar 

  • Kim, I., Kim, M. S., Chen, Y. R., & Kong, S. G. (2004). Detection of skin tumors on chicken carcasses using hyperspectral fluorescence imaging. Transactions of the ASAE, 47(5), 1785–1792.

    Article  Google Scholar 

  • Kim, M. S., Chen, Y. R., & Mehl, P. M. (2001). Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Transactions of the ASAE, 44(3), 721–729.

    Google Scholar 

  • Kim, M. S., Lefcourt, A. M., Chao, K., Chen, Y. R., Kim, I., & Chan, D. E. (2002). Multispectral detection of fecal contamination on apples based on hyperspectral imagery: part I. Application of visible and near-infrared reflectance imaging. Transactions of the ASAE, 45(6), 2027–2037.

    Google Scholar 

  • Kim, T., Cho, B.-K., & Kim, M. S. (2010). Emission filter design to detect poultry skin tumors using fluorescence hyperspectral imaging. Revista Colombiana de Ciencias Pecuarias, 23(1), 9–16.

    Google Scholar 

  • Kiyotoki, S., Nishikawa, J., Okamoto, T., Hamabe, K., Saito, M., Goto, A., Fujita, Y., Hamamoto, Y., Takeuchi, Y., Satori, S., & Sakaida, I. (2013). New method for detection of gastric cancer by hyperspectral imaging: a pilot study. Journal of Biomedical Optics, 18(2), 26010.

    Article  CAS  Google Scholar 

  • Kong SG (2003) Inspection of poultry skin tumor using hyperspectral fluorescence imaging. In Proceedings of the SPIE 6th International Conference on Quality Control by Artificial Vision, Paper No 5132, pp 455–463, 30 April 2003, Gatlinburg, USA.

  • Kong, S. G., Chen, Y.-R., Kim, I., & Kim, M. S. (2004). Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. Applied Optics, 43(4), 824–833.

    Article  Google Scholar 

  • Kotsiantis, S. B. (2007). Supervised machine learning: a review of classification techniques. Informatica, 31, 249–268.

    Google Scholar 

  • Koutchma, T. (2008). UV light for processing foods. IUVA news, 10(4), 24–29.

    Google Scholar 

  • Kumar, A., Lee, W. S., Ehsani, R., Albrigo, L. G., Yang, C., & Mangan, R. L. (2012). Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques. Journal of Applied Remote Sensing, 6(1), 063542.

    Article  Google Scholar 

  • Kuska, M., Wahabzada, M., Leucker, M., Dehne, H.-W., Kersting, K., Oerke, K.-C., Steiner, U., & Mahlein, A.-K. (2015). Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions. Plant Methods, 11, 28.

    Article  Google Scholar 

  • Lang, M., Stiffel, P., Braunova, Z., & Lichtenthaler, H. K. (1992). Investigation of the blue–green fluorescence emission of plant leaves. Botanica Acta, 105, 395–468.

    Article  Google Scholar 

  • Langley, P., Iba, W., & Thompson, K. (1992). An analysis of Bayesian classifiers. In Proceedings of the 10th National Conference on artificial intelligence (pp. 223–228). San Jose, USA: AAAI Press.

    Google Scholar 

  • Lara, M. A., Lleó, L., Diezma-Iglesias, B., Roger, J. M., & Ruiz-Altisent, M. (2013). Monitoring spinach shelf-life with hyperspectral image through packaging films. Journal of Food Engineering, 119(2), 353–361.

    Article  CAS  Google Scholar 

  • Lasch, P. (2012). Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging. Chemometrics and Intelligent Laboratory Systems, 117, 100–114.

    Article  CAS  Google Scholar 

  • Lee, K.-J., Kang, S., Kim, M.S., Noh S.H. (2005) Hyperspectal imaging for detecting defect on apples. ASABE conference paper, Paper No. 053075. St. Joseph, MI, USA.

  • Lefcourt, A. M., Kim, M. S., Chen, Y. R., & Kang, S. (2006). Systematic approach for using hyperspectral imaging data to develop multispectral imaging systems: detection of feces on apples. Computers and Electronics in Agriculture, 54(1), 22–35.

    Article  Google Scholar 

  • Leiva-Valenzuela, G. A., Lu, R., & Aguilera, J. M. (2012). Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Journal of Food Engineering, 115(1), 91–98.

    Article  Google Scholar 

  • Lelong, C. C. D., Pinet, P. C., & Poilve, H. (1998). Hyperspectral imaging and stress mapping in agriculture: a case study on wheat in Beauce (France). Remote Sensing of Environment, 66(2), 179–191.

    Article  Google Scholar 

  • Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., & Dotsinsky, I. (2005). Removal of power-line interference from the ECG: a review of the subtraction procedure. Biomedical Engineering Online, 4(50), 1–18.

    Google Scholar 

  • Lieber, C. A., & Mahadevan-Jansen, A. (2003). Automated method for subtraction of fluorescence from biological Raman spectra. Applied Spectroscopy, 57(11), 1363–1367.

    Article  CAS  Google Scholar 

  • Liu, C., Liu, W., Lu, X., Ma, F., Chen, W., Yang, J., & Zheng, L. (2014). Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit. PloS One, 9(2), e87818.

    Article  CAS  Google Scholar 

  • Liu, L., Ngadi, M. O., Prasher, S. O., & Gariépy, C. (2010). Categorization of pork quality using Gabor filter-based hyperspectral imaging technology. Journal of Food Engineering, 99(3), 284–293.

    Article  Google Scholar 

  • Liu, Z., Wang, H., & Li, Q. (2012). Tongue tumor detection in medical hyperspectral images. Sensors, 12(1), 162–174.

    CAS  Google Scholar 

  • Lopes, M. B., & Wolff, J. C. (2009). Investigation into classification/sourcing of suspect counterfeit Heptodintrade mark tablets by near infrared chemical imaging. Analytica Chimica Acta, 633(1), 149–155.

    Article  CAS  Google Scholar 

  • Lopes, M. B., Wolff, J. C., Bioucas-Dias, J. M., & Figueiredo, M. A. (2010). Near-infrared hyperspectral unmixing based on a minimum volume criterion for fast and accurate chemometric characterization of counterfeit tablets. Analytical Chemistry, 82(4), 1462–1469.

    Article  CAS  Google Scholar 

  • Lü, Q., & Tang, M. (2012). Detection of hidden bruise on kiwi fruit using hyperspectral imaging and parallelepiped classification. Procedia Environmental Sciences, 12(B), 1172–1179.

    Article  Google Scholar 

  • Lu, R., & Ariana, D. P. (2013). Detection of fruit fly infestation in pickling cucumbers using a hyperspectral reflectance/transmittance imaging system. Postharvest Biology and Technology, 81, 44–50.

    Article  Google Scholar 

  • Lu R & Chen YR (1999). Hyperspectral imaging for safety inspection of food and agricultural products. In: Proceedings of the SPIE Conference on Pathogen Detection and Remediation for Safe Eating, Volume 3544, 12 January 1999, pp 121–133, Boston, USA.

  • Lu, R. (2003). Detection of bruises on apples using near-infrared hyperspectral imaging. Transactions of the ASAE, 46(2), 523–530.

    Google Scholar 

  • Ma, J., Sun, D.-W., & Pu, H. (2016). Spectral absorption index in hyperspectral image analysis for predicting moisture contents in pork longissimus dorsi muscles. Food Chemistry, 197, 848–854.

    Article  CAS  Google Scholar 

  • Mahalanobis, P. C. (1936). On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India, 2(1), 49–55.

    Google Scholar 

  • Mahesh S, Jayas DS, Paliwal J & White NDG (2010) Near-infrared (NIR) hyperspectral imaging—an emerging analytical tool for classification of western Canadian wheat classes from different locations and crop years. ASABE conference paper, Paper No. MB-SK 10–302. St. Joseph, MI, USA.

  • Mahesh, S., Jayas, D. S., Paliwal, J., & White, N. D. G. (2011). Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples. Sensing and Instrumentation for Food Quality and Safety, 5(1), 1–9.

    Article  Google Scholar 

  • Mahesh, S., Manickavasagan, A., Jayas, D. S., Paliwal, J., & White, N. D. G. (2008). Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Engineering, 101(1), 50–57.

    Article  Google Scholar 

  • Manley, M., Williams, P., Nilsson, D., & Geladi, P. (2009). Near infrared hyperspectral imaging for the evaluation of endosperm texture in whole yellow maize (Zea maize L.) kernels. Journal of Agricultural and Food Chemistry, 57(19), 8761–8769.

    Article  CAS  Google Scholar 

  • Mark, H., & Workman, J. (2003). Statistics in spectroscopy (2nd edition). San Diego: Academic Press.

    Google Scholar 

  • Marquez, A. J., AM, D., & MIP, R. (2005). Using optical NIR sensor for on-line virgin olive oils characterization. Sensors and Actuators B, 107(1), 64–68.

    Article  CAS  Google Scholar 

  • Martens, H., & Naes, T. (1992). Multivariate calibration. New York: John Wiley and Sons.

    Google Scholar 

  • Martens, H., & Stark, E. (1991). Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy. Journal of Pharmaceutical and Biomedical Analysis, 9(8), 625–635.

    Article  CAS  Google Scholar 

  • Martens H, Jensen SA & Geladi P (1983) Multivariate linearity transformations for near infrared reflectance spectroscopy. In: Christie (ed) Proceedings of the Nordic Symposium on Applied Statistics, pp 205–234, 12–14 June 1983, Stokkand Forlag Publishers, Stavanger, Norway.

  • Martens, H., Nielsen, J. P., & Engelsen, S. B. (2003). Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures. Analytical Chemistry, 75(3), 394–404.

    Article  CAS  Google Scholar 

  • Martinsen, P., Schaare, P., & Andrews, M. (1999). A versatile near infrared imaging spectrometer. Journal of Near Infrared Spectroscopy, 7(1), 17–25.

    Article  CAS  Google Scholar 

  • Massart DL, Vandeginste BGM, Buydens LMC, de Jong S, Lewi PJ & Smeyers-Verbeke J (1997) Handbook of chemometrics and qualimetrics: Part A. Elsevier Science B. V., Amsterdam, The Netherlands.

  • Mazet, V., Carteret, C., Brie, D., Idier, J., & Humbert, B. (2005). Background removal from spectra by designing and minimising a non-quadratic cost function. Chemometrics and Intelligent Laboratory Systems, 76(2), 121–133.

    Article  CAS  Google Scholar 

  • McGoverin, C. M., Engelbrecht, P., Geladi, P., & Manley, M. (2011). Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics. Analytical and Bioanalytical Chemistry, 401(7), 2283–2289.

    Article  CAS  Google Scholar 

  • McLachlan, G. (1992). Discriminant analysis and statistical pattern recognition. Hoboken: John Wiley & Sons, Inc..

    Book  Google Scholar 

  • Mehl, P. M., Chen, Y.-R., Kim, M. S., & Chan, D. E. (2004). Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. Journal of Food Engineering, 61(1), 67–81.

    Article  Google Scholar 

  • Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8), 1778–1790.

    Article  Google Scholar 

  • Mendoza, F., Lu, R., Ariana, D., Cen, H., & Bailey, B. (2011). Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 62(2), 149–160.

    Google Scholar 

  • Mohan, L. A., Karunakaran, C., Jayas, D. S., & White, N. D. G. (2005). Classification of bulk cereals using visible and NIR reflectance characteristics. Canadian Biosystems Engineering, 47, 7.7–7.14.

    Google Scholar 

  • Nagata M, Tallada JG, Kobayashi T & Toyada H (2005) NIR hyperspectral imaging for measurement of internal quality in strawberries. ASABE conference paper, Paper No. 053131, St. Joseph, MI, USA.

  • Nagata M, Tallada JG, Kobayashi T, Cui Y & Gejima Y (2004) Predicting maturity quality parameters of strawberries using hyperspectral imaging. ASABE conference paper, Paper No. 043033, St. Joseph, MI, USA.

  • Neville, R. A., Levesque, J., Staenz, K., Nadeau, C., Hauff, P., & Borstad, G. A. (2003). Spectral unmixing of hyperspectral imagery for mineral exploration: comparison of results from SFSI and AVIRIS. Canadian Journal of Remote Sensing, 29(1), 99–110.

    Article  Google Scholar 

  • Nguyen NH, Chen J, Richard C, Honeine P & Theys C (2013) Supervised nonlinear unmixing of hyperspectral images using a pre-image methods. In: Mary et al (eds) New Concepts in Imaging: Optical and Statistical Models, EAS Publications Series, 59, 417–437.

  • Nicolai, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biology and Technology, 46(2), 99–118.

    Article  Google Scholar 

  • Nicolai, B. M., Lotze, E., Peirs, A., Scheerlinck, N., & Theron, K. I. (2006). Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging. Postharvest Biology and Technology, 40(1), 1–6.

    Article  Google Scholar 

  • Noh HK & Lu R (2005) Hyperspectral reflectance and fluorescence for assessing apple quality. ASABE conference paper, Paper No 053069, St. Joseph, MI, USA.

  • Noh, H. K., & Lu, R. (2007). Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality. Postharvest Biology and Technology, 43(2), 193–201.

    Article  Google Scholar 

  • Noh, H. K., Peng, Y., & Lu, R. (2007). Integration of hyperspectral reflectance and fluorescence imaging for assessing apple maturity. Transactions of the ASABE, 50(3), 963–971.

    Article  Google Scholar 

  • Norris, K. H., & Williams, P. C. (1984). Optimization of mathematical treatments of raw near-infrared signals in the measurement of protein in hard red spring wheat: 1. Influence of particle size. Cereal Chemistry, 61(2), 158–165.

    CAS  Google Scholar 

  • Norris KH (1983) Extraction information from spectrophotometric curves. Predicting chemical composition from visible and near-infrared spectra. In: Martens & Russwurm Jr (eds) Food Research and Data Analysis, pp 95–113, Applied Science, London, UK.

  • Novales, B., Bertrand, D., Devaux, M. F., Robert, P., & Sire, A. (1999). Multispectral fluorescence imaging for the identification of food products. Journal of the Science of Food and Agriculture, 71(3), 376–382.

    Article  Google Scholar 

  • O’Farrell, M., Wold, J. P., Hoy, M., Tschudi, J., & Schulerud, H. (2010). On-line fat content classification of in homogeneous pork trimmings using multispectral near infrared interactance imaging. Journal of Near Infrared Spectroscopy, 18(2), 135–146.

    Article  CAS  Google Scholar 

  • Okamoto, H., & Lee, W. S. (2009). Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture, 66(2), 201–208.

    Article  Google Scholar 

  • Osuna E, Freund R & Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 17–19 June 1997, pp 130–136, Puerto Rico, USA.

  • Ottavian, M., Fasolato, L., Serva, L., Facco, P., & Barolo, M. (2014). Data fusion for food authentication: fresh/frozen-thawed discrimination in west African goatfish (Pseudupeneus prayensis) fillets. Food and Bioprocess Technology, 7(4), 1025–1036.

    Article  CAS  Google Scholar 

  • Otto, O. (1998). Chemometrics: statistics and computer application in analytical chemistry. Wiley-VCH Verlag GmbH and Co. Weinheim: KGaA.

    Google Scholar 

  • Paliwal, J., Jayas, D. S., Visan, N. S., & White, N. D. G. (2005). Quantification of variations in machine-vision-computed features of cereal grains. Canadian Biosystems Engineering, 47, 7.1–7.6.

    Google Scholar 

  • Park B & Lu R (2015) Hyperspectral Imaging Technology in Food and Agriculture. Gustavo VB (eds.). Springer, New York, USA.

  • Park, B., Lawrence, K. C., Windham, W. R., & Buhr, R. J. (2002). Hyperspectral imaging for detecting fecal and ingesta contamination on poultry carcasses. Transactions of the ASAE, 45(6), 2017–2026.

    Article  Google Scholar 

  • Park, B., Lawrence, K. C., Windham, W. R., & Smith, D. P. (2006). Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. Journal of Food Engineering, 75(3), 340–348.

    Article  Google Scholar 

  • Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2, 559–572.

    Article  Google Scholar 

  • Pearson, T. C., Wicklow, D. T., Maghirang, E. B., Xie, F., & Dowell, F. E. (2001). Detecting aflatoxin in single corn kernels by using transmittance and reflectance spectroscopy. Transactions of the ASAE, 44(5), 1247–1254.

    Article  CAS  Google Scholar 

  • Pérez-Marín D, Sánchez M-T, Paz P & González-Dugo V (2011) Postharvest shelf-life discrimination of nectarines produced under different irrigation strategies using NIR-spectroscopy. Postharvest shelf-life discrimination of nectarines produced under different irrigation strategies using NIR-spectroscopy, 44(6), 1405–1414.

  • Pérez-Marín, D., Sánchez, M.-T., Paz, P., Soriano, M.-A., Guerrero, J.-E., & Garrido-Varo, A. (2009). Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage. Postharvest Biology and Technology, 52(2), 180–188.

    Article  Google Scholar 

  • Pierna, J. A. F., Baeten, V., & Dardenne, P. (2006). Screening of compound feeds using NIR hyperspectral data. Chemometrics and Intelligent Laboratory Systems, 84(1–2), 114–118.

    Article  CAS  Google Scholar 

  • Pierna, J. A. F., Vermeulen, P., Amand, O., Tossens, A., Dardenne, P., & Baeten, V. (2012). NIR hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed. Chemometrics and Intelligent Laboratory Systems, 117, 233–239.

    Article  CAS  Google Scholar 

  • Pierna, J. A. F., Volery, P., Besson, R., Baeten, V., & Dardenne, P. (2005). Classification of modified starches by Fourier transform infrared spectroscopy using support vector machines. Journal of Agricultural and Food Chemistry, 53(17), 6581–6585.

    Article  CAS  Google Scholar 

  • Polder, G., Van Der Heijden, G. W. A. M., & Young, I. T. (2003). Tomato sorting using independent component analysis on spectral images. Real-Time Imaging, 9, 253–259.

    Article  Google Scholar 

  • Polder, G., Van Der Heijden, G. W. A. M., Waalwijk, C., & Young, I. T. (2005). Detection of Fusarium in single wheat kernels using spectral imaging. Seed Science and Technology, 33, 655–668.

    Article  Google Scholar 

  • Pu, H., Liu, D., Wang, L., & Sun, D.-W. (2016). Soluble solids content and pH prediction and maturity discrimination of lychee fruits using visible and near infrared hyperspectral imaging. Food Analytical Methods, 9(1), 235–244.

    Article  Google Scholar 

  • Pu, Y.-Y., & Sun, D.-W. (2016). Prediction of moisture content uniformity of microwave-vacuum dried mangoes as affected by different shapes using NIR hyperspectral imaging. Innovative Food Science and Emerging Technologies, 33, 348–356.

    Article  Google Scholar 

  • Qiao, J., Ngadi, M. O., Wang, N., Gariépy, C., & Prasher, S. O. (2007). Pork quality and marbling level assessment using a hyperspectral imaging system. Journal of Food Engineering, 83(1), 10–16.

    Article  Google Scholar 

  • Qin, J., & Lu, R. (2005). Detection of pits in tart cherries by hyperspectral transmission imaging. Transactions of the ASABE, 48(5), 1963–1970.

    Article  Google Scholar 

  • Qin, J., Burks, T. F., Kim, M. S., Chao, K., & Ritenour, M. A. (2008). Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sensing and Instrumentation for Food Quality and Safety, 2(3), 168–177.

    Article  Google Scholar 

  • Qin, J., Burks, T. F., Ritenour, M. A., & Bonn, W. G. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 93(2), 183–191.

    Article  Google Scholar 

  • Qu J-H, Sun D-W, Cheng J-H & Pu H (2016) Mapping moisture contents in grass carp (Ctenopharyngodon idella) slices under different freeze drying periods by Vis-NIR hyperspectral imaging. LWT-Food Science and Technology, In Press, Accepted Manuscript. Available online 19 September 2016.

  • Rajkumar, P., Wang, N., Eimasry, G., Raghavan, G. S. V., & Gariepy, Y. (2012). Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of Food Engineering, 108(1), 194–200.

    Article  Google Scholar 

  • Ramalingam, G., Neethirajan, S., Jayas, D. S., & White, N. D. G. (2011). Characterization of the influence of moisture content on single wheat kernels using machine vision. Applied Engineering in Agriculture, 27(3), 403–409.

    Article  Google Scholar 

  • Rinnan A, Norgaard L, Van Der Berg FWJ, Thygesen J, Bro R & Engelsen SB (2009) Data pre-processing. In: Sun (ed) Infrared Spectroscopy for Food Quality Analysis and Control, pp 29–50, Academic press, Burlington, USA.

  • Rodríguez-Pulido, F. J., Barbin, D. F., Sun, D.-W., Gordillo, B., González-Miret, M. L., & Heredia, F. J. (2013). Grape seed characterization by NIR hyperspectral imaging. Postharvest Biology and Technology, 76, 74–82.

    Article  CAS  Google Scholar 

  • Romia MB & MA Bernardez (2009) Multivariate calibration for quantitative analysis. In: Sun (ed) Infrared spectroscopy for food quality analysis and control, pp 51–82, Academic Press, Burlington, USA.

  • Sapirstein, H. D., Neuman, M., Wright, E. H., Shwedyk, E., & Bushuk, W. (1987). An instrumental system for cereal grain classification using digital image analysis. Journal of Cereal Science, 6(1), 3–14.

    Article  Google Scholar 

  • Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639.

    Article  CAS  Google Scholar 

  • Schaare, P. N., & Fraser, D. G. (2000). Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis). Postharvest Biology and Technology, 20(2), 175–184.

    Article  Google Scholar 

  • Schatzki, T. F., Haff, R. P., Young, R., Can, I., Le, L.-C., & Toyofuku, N. (1997). Defect detection in apples by means of X-ray imaging. Transactions of the ASAE, 40(5), 1407–1415.

    Article  Google Scholar 

  • Schulze, G., Jirasek, A., Yu, M. M., Lim, A., Turner, R. F., & Blades, M. W. (2005). Investigation of selected baseline removal techniques as candidates for automated implementation. Applied Spectroscopy, 59(5), 545–574.

    Article  CAS  Google Scholar 

  • Schweizer, S. M., & Moura, J. M. F. (2001). Efficient detection in hyperspectral imagery. IEEE Transactions on Image Processing, 10(4), 584–597.

    Article  CAS  Google Scholar 

  • Serranti, S., Cesare, D., Marini, F., & Bonifazi, G. (2013). Classification of oat and groat kernels using NIR hyperspectral imaging. Talanta, 103, 276–284.

    Article  CAS  Google Scholar 

  • Shenderey, C., Shmulevich, I., Alchanatis, V., Egozi, H., Hoffman, A., Ostrovsky, V., Lurie, S., Arie, B., & Schmilovitch, Z. (2010). NIRS detection of moldy core in apples. Food and Bioprocess Technology, 3(1), 79–86.

    Article  Google Scholar 

  • Singh, C. B., Jayas, D. S., Paliwal, J., & White, N. D. G. (2007). Fungal detection in wheat using near-infrared hyperspectral imaging. Transactions of the ASABE, 50(6), 2171–2176.

    Article  Google Scholar 

  • Singh, C. B., Jayas, D. S., Paliwal, J., & White, N. D. G. (2009). Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. Journal of Stored Products Research, 45(3), 151–158.

    Article  Google Scholar 

  • Singh, C. B., Jayas, D. S., Paliwal, J., & White, N. D. G. (2010). Detection of midge-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Biosystems Engineering, 105(3), 380–387.

    Article  Google Scholar 

  • Singh, C. B., Jayas, D. S., Paliwal, J., & White, N. D. G. (2012). Fungal damage detection in wheat using short-wave near-infrared hyperspectral and digital colour imaging. International Journal of Food Properties, 15(1), 11–24.

    Article  Google Scholar 

  • Siripatrawan, U., Makino, Y., Kawagoe, Y., & Oshita, S. (2011). Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging. Talanta, 85(1), 276–281.

    Article  CAS  Google Scholar 

  • Sivertsen, A. H., Chu, C.-K., Wang, L.-C., Godtliebsen, F., Heia, K., & Nilsen, H. (2009). Ridge detection with application to automatic fish fillet inspection. Journal of Food Engineering, 90(3), 317–324.

    Article  CAS  Google Scholar 

  • Sivertsen, A. H., Heia, K., Hindberg, K., & Godtliebsen, F. (2012). Automatic nematode detection in cod fillets (Gadus morhua L.) by hyperspectral imaging. Journal of Food Engineering, 111(4), 675–681.

    Article  Google Scholar 

  • Sivertsen, A. H., Heia, K., Stormo, S. K., Elvevoll, E., & Nilsen, H. (2011). Automatic nematode detection in cod fillets (Gadus morhua) by transillumination hyperspectral imaging. Journal of Food Science, 76(1), s77–s83.

    Article  CAS  Google Scholar 

  • Smith, K. L., Steven, M. D., & Colls, J. J. (2004). Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sensing of Environment, 92(2), 207–217.

    Article  Google Scholar 

  • Soulez F, Bongard S, Thiebaut E & Bacon R (2011) Restoration of hyperspectral astronomical data from integral field spectrograph. In: Proceedings of the 3rd IEEE-GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing WHISPERS, 6–9 June 2011, pp 1–4, Lisbonne, Portugal.

  • Steinier, J., Termonia, Y., & Deltour, J. (1972). Comments on smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 44(11), 1906–1909.

    Article  CAS  Google Scholar 

  • Steinmetz, V., Roger, J. M., Molto, E., & Blasco, J. (1999). On-line fusion of colour camera and spectrophotometer for sugar content prediction of apples. Journal of Agricultural Research, 73(2), 207–216.

    Article  Google Scholar 

  • Stenlund, H., Johansson, E., Gottfries, J., & Trygg, J. (2009). Unlocking interpretation in near infrared multivariate calibrations by orthogonal partial least squares. Analytical Chemistry, 81(1), 203–209.

    Article  CAS  Google Scholar 

  • Stordrange, L., Libnau, F. O., Malthe-Sørenssen, D., & Kvalheim, O. M. (2002). Feasibility study of NIR for surveillance of a pharmaceutical process, including a study of different preprocessing techniques. Journal of Chemometrics, 16(8–10), 529–541.

    Article  CAS  Google Scholar 

  • Su, W.-H., & Sun, D.-W. (2016). Potential of hyperspectral imaging for visual authentication of sliced organic potatoes from potato and sweet potato tubers and rapid grading of the tubers according to moisture proportion. Computers and Electronics in Agriculture, 125, 113–124.

    Article  Google Scholar 

  • Suzuki, Y., Okamoto, H., & Kataoka, T. (2008c). Image segmentation between crop and weed using hyperspectral imaging for weed detection in soybean field. Environment Control in Biology, 46(3), 163–173.

    Article  Google Scholar 

  • Suzuki, Y., Okamoto, H., Takahashi, M., Kataoka, T., & Shibata, Y. (2012). Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging. Japanese Society of Grassland Science, 58(1), 1–7.

    Article  Google Scholar 

  • Suzuki, Y., Okamoto, H., Tanaka, K., Kato, W., & Kataoka, T. (2008a). Estimation of chemical composition of grass in meadows using hyperspectral imaging. Environment Control in Biology, 46(2), 129–137.

    Article  Google Scholar 

  • Suzuki, Y., Tanaka, K., Kato, W., Okamoto, H., Kataoka, T., Shimada, H., Sugiura, T., & Shima, E. (2008b). Field mapping of chemical composition of forage using hyperspectral imaging in a grass meadow. Japanese Society of Grassland Science, 54(4), 179–188.

    Article  CAS  Google Scholar 

  • Symons, S. J., & Fulcher, R. G. (1988). Determination of wheat kernel morphological variation by digital image analysis: I. Variation in eastern Canadian milling quality wheats. Journal of Cereal Science, 8(3), 211–218.

    Article  Google Scholar 

  • Taghizadeh, M., Gowen, A. A., & O’Donnell, C. P. (2011). The potential of visible-near infrared hyperspectral imaging to discriminate between casing soil, enzymatic browning and undamaged tissue on mushroom (Agaricus bisporus) surfaces. Computers and Electronics in Agriculture, 77(1), 74–80.

    Article  Google Scholar 

  • Tatzer, P., Wolf, M., & Panner, T. (2005). Industrial application for inline material sorting using hyperspectral imaging in the NIR range. Real-Time Imaging, 11(2), 99–107.

    Article  Google Scholar 

  • Tøgersen, G., Arnesen, J. F., Nilsena, B. N., & Hildruma, K. I. (2003). On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy. Meat Science, 63(4), 515–523.

    Article  Google Scholar 

  • Tøgersen, G., Isaksson, T., Nilsen, B. N., Bakkerb, E. A., & Hildruma, K. I. (1999). On-line NIR analysis of fat, water and protein in industrial scale ground meat batches. Meat Science, 51(1), 97–102.

    Article  Google Scholar 

  • Tran, C. D. (2005). Principles, instrumentation and applications of infrared multispectral imaging, an overview. Analytical Letters, 38(5), 735–752.

    Article  CAS  Google Scholar 

  • Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16(3), 119–128.

    Article  CAS  Google Scholar 

  • Trygg, J., Holmes, E., & Lundstedt, T. (2007). Chemometrics in metabonomics. Journal of Proteome Research, 6(2), 469–479.

    Article  CAS  Google Scholar 

  • Tukey, J. W. (1980). We need both exploratory and confirmatory. The American Statistician, 34(1), 23–25.

    Google Scholar 

  • Vadivambal, R., & Jayas, D. S. (2016). Bio-imaging: principles, techniques and applications. Oxford: CRC Press, Taylor and Francis Group Ltd.

    Google Scholar 

  • Vadivambal, R., Jayas, D. S., & White, N. D. G. (2007). Wheat disinfestation using microwave energy. Journal of Stored Products Research, 43(4), 508–514.

    Article  Google Scholar 

  • Vajna, B., Patyi, G., Nagy, Z., Farkas, A., & Marosi, G. (2011). Comparison of chemometric methods in the analysis of pharmaceuticals with hyperspectral Raman imaging. Journal of Raman Spectroscopy, 42(11), 1977–1986.

    Article  CAS  Google Scholar 

  • Van Der Maaten L, Postma E & Van Der Herik J (2009) Dimensionality reduction: a comparative review. Tilburg University Technical Report, TiCC-TR 2009–005.

  • Vargas AM, Kim MS, Tao Y, Lefcourt A & Chen Y-R (2004) Safety inspection of cantaloupes and strawberries using multispectral fluorescence imaging techniques. ASABE conference paper, Paper No. 043056. St. Joseph, MI, USA.

  • Vargas, A. M., Kim, M. S., Tao, Y., Lefcourt, A. M., Chen, Y.-R., Luo, Y., Song, Y., & Buchanan, R. (2005). Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery. Journal of Food Science, 70(8), e471–e476.

    Article  CAS  Google Scholar 

  • Varshney, P. K., & Arora, M. K. (2004). Advanced image processing techniques for remotely sensed hyperspectral data. Berlin Heidelberg: Springer-Verlag.

    Book  Google Scholar 

  • Vermeulen, P., Pierna, J. A., Van Egmond, H. P., Dardenne, P., & Baeten, V. (2011). Online detection and quantification of ergot bodies in cereals using infrared hyperspectral imaging. Food additives and contaminants. Part A, Chemistry, Analysis, Control, Exposure & Risk Assessment, 29(2), 232–240.

    Article  CAS  Google Scholar 

  • Vigneau, N., Ecarnot, M., Rabatel, G., & Roumet, P. (2011). Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat. Field Crops Research, 122(1), 25–31.

    Article  Google Scholar 

  • Wang, D., Dowell, F. E., Ram, M. S., & Schapaugh, W. T. (2003). Classification of fungal-damaged soybean seeds using near-infrared spectroscopy. International Journal of Food Properties, 7(1), 75–82.

    Article  Google Scholar 

  • Wang, H., Li, C., & Wang, M. (2013). Qualitative determination of onion internal quality using hyperspectral imaging with reflectance, interactance, and transmittance modes. Transactions of ASABE, 56(4), 1–14.

    Google Scholar 

  • Wang, K., Chi, G., Lau, R., & Chen, T. (2011). Multivariate calibration of near infrared spectroscopy in the presence of light scattering effect: a comparative study. Analytical Letters, 44(5), 824–836.

    Article  CAS  Google Scholar 

  • Wang, L., Pu, H., & Sun, D.-W. (2016). Estimation of chlorophyll-a concentration of different seasons in outdoor ponds using hyperspectral imaging. Talanta, 147, 422–429.

    Article  CAS  Google Scholar 

  • Wang, W., & Paliwal, J. (2006). Spectral data compression and analyses techniques to discriminate wheat classes. Transactions of the ASABE, 49(5), 1607–1612.

    Article  Google Scholar 

  • Wang, W., & Paliwal, J. (2007). Near-infrared spectroscopy and imaging in food quality and safety. Sensing and Instrumentation for Food Quality and Safety, 1(4), 193–207.

    Article  Google Scholar 

  • Wang, W., Li, C., Tollner, E. W., Gitaitis, R. D., & Rains, G. C. (2012). Shortwave infrared hyperspectral imaging for detecting sour skin (Burkholderiacepacia)-infected onions. Journal of Food Engineering, 109(1), 38–48.

    Article  Google Scholar 

  • Wang W, Paliwal J & Jayas DS (2004). Determination of moisture content of ground wheat using near-infrared spectroscopy. ASABE conference paper, Paper No. MB04–200, St. Joseph, MI, USA.

  • Williams, P., Geladi, P., Fox, G., & Manley, M. (2009). Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Analytica Chimica Acta, 653(2), 121–130.

    Article  CAS  Google Scholar 

  • Wold, H. (1975). Soft modeling by latent variables. In Gani (Ed.), Nonlinear iterative partial least squares approach. Perspectives in probability and statistics (pp. 520–540). London: Academic Press.

    Google Scholar 

  • Wold, J. P. (2016). On-line and non-destructive measurement of core temperature in heat treated fish cakes by NIR hyperspectral imaging. Innovative Food Science and Emerging Technologies, 33, 431–437.

    Article  Google Scholar 

  • Wold, J. P., O’Farrell, M., Hoy, M., & Tschudi, J. (2011). On-line determination and control of fat content in batches of beef trimmings by NIR imaging spectroscopy. Meat Science, 89(3), 317–324.

    Article  CAS  Google Scholar 

  • Wold, S., Antti, H., Lindgren, F., & Ohman, J. (1998). Orthogonal signal correction of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems, 44(1–2), 175–185.

    Article  CAS  Google Scholar 

  • Wold, S., Martens, H., & Wold, H. (1983). The multivariate calibration problem in chemistry solved by the PLS method. In Ruhe & Kagstrom (eds) proceedings of the conference on matrix pencils. Lecture notes in mathematics, 973 (pp. 286–293). Heidelberg: Springer.

    Google Scholar 

  • Wu, D., & Sun, D.-W. (2013a). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review-part I: fundamentals. Innovative Food Science and Emerging Technologies, 19, 1–14.

    Article  CAS  Google Scholar 

  • Wu, D., & Sun, D.-W. (2013b). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review-part II: applications. Innovative Food Science and Emerging Technologies, 19, 15–28.

    Article  CAS  Google Scholar 

  • Wu, D., Shi, H., Wang, S., He, Y., Bao, Y., & Liu, K. (2012). Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Analytica Chimica Acta, 726, 57–66.

    Article  CAS  Google Scholar 

  • Wu, W., Mallet, Y., Walczak, B., Penninckx, W., Massart, D. L., Heuerding, S., & Erni, F. (1996). Comparison of regularized discriminant analysis, linear discriminant analysis and quadratic discriminant analysis, applied to NIR data. Analytica Chimica Acta, 329(3), 257–265.

    Article  CAS  Google Scholar 

  • Xie, A., Sun, D.-W., Zhu, Z., & Pu, H. (2016). Nondestructive measurements of freezing parameters of frozen porcine meat by NIR hyperspectral imaging. Food and Bioprocess Technology, 9(9), 1444–1454.

    Article  CAS  Google Scholar 

  • Xie, C., Shao, Y., Li, X., & Hea, Y. (2015). Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Scientific Reports, 5, 16564.

    Article  CAS  Google Scholar 

  • Xing, J., Bravo, C., Jancsok, P. T., Ramon, H., & De Baerdemaeker, J. (2005). Detecting bruises on ‘golden delicious’ apples using hyperspectral imaging with multiple wavebands. Biosystems Engineering, 90(1), 27–36.

    Article  Google Scholar 

  • Xing, J., Hung, P. V., Symons, S., Shahin, M., & Hatcher, D. (2009). Using a short wavelength infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels. Sensing and Instrumentation for Food Quality and Safety, 3(4), 211–218.

    Article  Google Scholar 

  • Xing, J., Saeys, W., & De Baerdemaeker, J. (2007). Combination of chemometric tools and image processing for bruise detection on apples. Computers and Electronics in Agriculture, 56(1), 1–13.

    Article  Google Scholar 

  • Xing, J., Symons, S., Hatcher, D., & Shahin, M. (2011). Comparison of short-wavelength infrared (SWIR) hyperspectral imaging system with an FT-NIR spectrophotometer for predicting alpha-amylase activities in individual Canadian western red spring (CWRS) wheat kernels. Biosystems Engineering, 108(4), 303–310.

    Article  Google Scholar 

  • Xu, J.-L., Riccioli, C., & Sun, D.-W. (2016a). Development of an alternative technique for rapid and accurate determination of fish caloric density based on hyperspectral imaging. Journal of Food Engineering, 190, 185–194.

    Article  Google Scholar 

  • Xu, J.-L., Riccioli, C., & Sun, D.-W. (2016b). Efficient integration of particle analysis in hyperspectral imaging for rapid assessment of oxidative degradation in salmon fillet. Journal of Food Engineering, 169, 259–271.

    Article  CAS  Google Scholar 

  • Yang, C. (2012). A high-resolution airborne four-camera imaging system for agricultural remote sensing. Computers and Electronics in Agriculture, 88, 13–24.

    Article  CAS  Google Scholar 

  • Zayas, I., Pomeranz, Y., & Lai, F. S. (1985). Discrimination between Arthur and Arkan wheats by image analysis. Cereal Chemistry, 62(6), 478–480.

    Google Scholar 

  • Zhang, H., Paliwal, J., Jayas, D. S., & White, N. D. G. (2007). Classification of fungal infected wheat kernels using near-infrared reflectance hyperspectral imaging and support vector machine. Transactions of the ASABE, 50(5), 1779–1785.

    Article  Google Scholar 

  • Zhang, Q., Wang, H., Plemmons, R. J., & Pauca, V. P. (2008). Tensor methods for hyperspectral data analysis: a space object material identification study. Journal of the Optical Society of America. A, Optics, Image Science, and Vision, 25(12), 3001–3012.

    Article  Google Scholar 

  • Zhang, X., Liu, F., He, Y., & Li, X. (2012). Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds. Sensors, 12(12), 17234–17246.

    Article  CAS  Google Scholar 

  • Zheng, G., Chen, Y., Intes, X., Chance, B., & Glickson, J. D. (2004). Contrast-enhanced near-infrared (NIR) optical imaging for subsurface cancer detection. Journal of Porphyrins and Phthalocyanines, 8(9), 1106–1117.

    Article  CAS  Google Scholar 

  • Zhu, Q. B., Huang, M., Zhao, X., & Wang, S. (2013). Wavelength selection of hyperspectral scattering image using new semi-supervised affinity propagation for prediction of firmness and soluble solid content in apples. Food Analytical Methods, 6(1), 334–342.

    Article  Google Scholar 

  • Zude, M., Herold, B., Roger, J.-M., Bellon-Maurel, V., & Landahl, S. (2006). Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life. Journal of Food Engineering, 77(2), 254–260.

    Article  Google Scholar 

  • Zupan J & Gesteinger J (1993) Neural networks for chemists: an introduction. Wiley, VCH, Weinheim, Germany.

  • Zupan, J. (1994). Introduction of artificial neural network (ANN) methods: what they are and how to use them. Acta Chimica Slovenica, 41(3), 327–352.

    CAS  Google Scholar 

Download references

Acknowledgments

The authors thank the University of Manitoba Graduate Fellowship, the Graduate Enhancement of Tri-Council Stipends (GETS) program and the Natural Sciences and Engineering Research Council of Canada for funding this study. This research was also supported by the International S&T Cooperation Program of China (2015DFA71150) and the International S&T Cooperation Program of Guangdong Province, China (2013B051000010).

Author information

Authors and Affiliations

  1. Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada

    Lankapalli Ravikanth & Digvir S. Jayas

  2. Cereal Research Center, Agriculture and Agri-Food Canada, c/o Department of Biosystems Engineering, University of Manitoba Winnipeg, Winnipeg, MB, Canada

    Noel D. G. White & Paul G. Fields

  3. School of Food Science and Engineering, South China University of Technology, Guangzhou, 510641, People’s Republic of China

    Da-Wen Sun

  4. Food Refrigeration and Computerised Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland

    Da-Wen Sun

Authors
  1. Lankapalli Ravikanth
    View author publications

    Search author on:PubMed Google Scholar

  2. Digvir S. Jayas
    View author publications

    Search author on:PubMed Google Scholar

  3. Noel D. G. White
    View author publications

    Search author on:PubMed Google Scholar

  4. Paul G. Fields
    View author publications

    Search author on:PubMed Google Scholar

  5. Da-Wen Sun
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Digvir S. Jayas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ravikanth, L., Jayas, D.S., White, N.D.G. et al. Extraction of Spectral Information from Hyperspectral Data and Application of Hyperspectral Imaging for Food and Agricultural Products. Food Bioprocess Technol 10, 1–33 (2017). https://doi.org/10.1007/s11947-016-1817-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1007/s11947-016-1817-8

Keywords