Food and Bioprocess Technology

, Volume 5, Issue 4, pp 1121–1142 | Cite as

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

  • D. Lorente
  • N. Aleixos
  • J. Gómez-Sanchis
  • S. Cubero
  • O. L. García-Navarrete
  • J. Blasco
Review Paper

Abstract

Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time-consuming for such high-dimensional datasets, and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, non-destructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.

Keywords

Computer vision Fruits Vegetables Quality Non-destructive inspection Image analysis Hyperspectral imaging Multispectral imaging 

Nomenclature

ANN

Artificial neural networks

ANOVA

Analysis of variance

AOTF

Acousto-optic tunable filters

BMP

Bitmap image format

BSQ

Band sequential

CCD

Charge-coupled device

FLD

Fisher’s linear discriminant

FWHM

Full width at half-maximum

GALDA

Genetic algorithm based on LDA

LCTF

Liquid crystal tunable filters

LD

Lorentzian distribution

LDA

Linear discriminant analysis

MC

Moisture content

MD

Mahalanobis distance

NIR

Near infrared

PCA

Principal component analysis

PLS

Partial least square

PLSDA

PLS discriminant analysis

PLSR

PLS regression

RF

Radiofrequency

RGB

Red, green, blue colour space

RGBI

Red, green, blue, infrared

SAM

Spectral angle mapper

SID

Spectral information divergence

SSC

Soluble solids content

TA

Titratable acid

TIFF

Tagged image file format

UV

Ultraviolet

References

  1. Aleixos, N., Blasco, J., Navarrón, F., & Moltó, E. (2002). Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Computers and Electronics in Agriculture, 33(2), 121–137.CrossRefGoogle Scholar
  2. Al-Mallahi, A., Kataoka, T., & Okamoto, H. (2008). Discrimination between potato tubers and clods by detecting the significant wavebands. Biosystems Engineering, 100(3), 329–337.CrossRefGoogle Scholar
  3. Ariana, D. P., & Lu, R. (2010a). Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles. Computers and Electronics in Agriculture, 74(1), 137–144.CrossRefGoogle Scholar
  4. Ariana, D. P., & Lu, R. (2010b). Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. Journal of Food Engineering, 96(4), 583–590.CrossRefGoogle Scholar
  5. Ariana, D. P., Guyer, D. E., & Shrestha, B. (2006). Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Computers and Electronics in Agriculture, 50, 148–161.CrossRefGoogle Scholar
  6. Bei, L., Dennis, G. I., Miller, H. M., Spaine, T. W., & Carnahan, J. W. (2004). Acousto-optic tunable filters: Fundamentals and applications as applied to chemical analysis techniques. Progress in Quantum Electronics, 28(2), 67–87.CrossRefGoogle Scholar
  7. Bennedsen, B. S., & Peterson, D. L. (2005). Performance of a system for apple surface defect identification in near-infrared images. Biosystems Engineering, 90(4), 419–431.CrossRefGoogle Scholar
  8. Bennedsen, B. S., Peterson, D. L., & Tabb, A. (2007). Identifying apple surface defects using principal components analysis and artificial neural networks. Transactions of the ASABE, 50(6), 2257–2265.Google Scholar
  9. Blasco, J., Aleixos, N., Gómez, J., & Moltó, E. (2007). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering, 83(3), 384–393.CrossRefGoogle Scholar
  10. Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103, 137–145.CrossRefGoogle Scholar
  11. Cayuela, J. A., García-Martos, J. M., & Caliani, N. (2009). NIR prediction of fruit moisture, free acidity and oil content in intact olives. Grasas y Aceites, 60(2), 194–202.CrossRefGoogle Scholar
  12. Chang, C. (1976). Acousto-optic devices and applications. IEEE Transactions on Sonics Ultrasound, 23(1), 2–22.CrossRefGoogle Scholar
  13. Chang, C. (2003). Hyperspectral imaging: Techniques for spectral detection and classification. New York: Springer.Google Scholar
  14. Cheng, X., Chen, Y., Tao, Y., Wang, C., Kim, M. S., & Lefcourt, A. (2004). A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection. Transactions of ASAE, 47(4), 1313–1320.Google Scholar
  15. Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sun, D.-W., & Menesatti, P. (2011). Shape analysis of agricultural products: A review of recent research advances and potential application to computer vision. Food and Bioprocess Technology, 4, 673–692.CrossRefGoogle Scholar
  16. Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504.CrossRefGoogle Scholar
  17. Du, C.-J., & Sun, D.-W. (2006). Learning techniques used in computer vision for food quality evaluation: a review. Journal of Food Engineering, 72, 39–55.Google Scholar
  18. Du, C.-J., & Sun, D.-W. (2009). Retrospective shading correlation of confocal laser scanning microscopy beef images for three-dimensional visualization. Food and Bioprocess Technology, 2, 167–176.CrossRefGoogle Scholar
  19. Egmont-Petersen, M., de Ridder, D., & Handels, H. (2002). Image processing with neural networks—A review. Pattern Recognition, 35(10), 2279–2301.CrossRefGoogle Scholar
  20. 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, 98–107.CrossRefGoogle Scholar
  21. ElMasry, G., Nassar, A., Wang, N., & Vigneault, C. (2008a). Spectral methods for measuring quality changes of fresh fruits and vegetables. Stewart Postharvest Review, 4, 1–13.CrossRefGoogle Scholar
  22. ElMasry, G., Wang, N., Vigneault, C., Qiao, J., & ElSayed, A. (2008b). Early detection of apple bruises on different background colors using hyperspectral imaging. LWT, 41, 337–345.CrossRefGoogle Scholar
  23. ElMasry, G., Wang, N., & Vigneault, C. (2009). Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks. Postharvest Biology and Technology, 52, 1–8.CrossRefGoogle Scholar
  24. Erives, H., & Fitzgerald, G. J. (2005). Automated registration of hyperspectral images for precision agriculture. Computers and Electronics in Agriculture, 47(2), 103–119.CrossRefGoogle Scholar
  25. Farrera-Rebollo, R. R., Salgado-Cruz, M. P., Chanona-Pérez, J., Gutiérrez-López, G. F., Alamilla-Beltrán, L., & Calderón-Domínguez, G. (2011). Evaluation of image analysis tools for characterization of sweet bread crumb structure. Food and Bioprocess Technology. doi:10.1007/s11947-011-0513-y.
  26. Fernandes, A. M., Oliveira, P., Moura, J. P., Oliveira, A. A., Falco, V., Correia, M. J., et al. (2011). Determination of anthocyanin concentration in whole grape skins using hyperspectral imaging and adaptive boosting neural networks. Journal of Food Engineering, 105(2), 216–226.CrossRefGoogle Scholar
  27. Fisher, R. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188.CrossRefGoogle Scholar
  28. Geladi, P. L. M. (2007). Calibration standards and image calibration. In H. F. Grahn & P. Geladi (Eds.), Techniques and applications of hyperspectral image analysis (pp. 203–220). Chichester: Wiley.CrossRefGoogle Scholar
  29. Gómez-Sanchis, J., Camps-Valls, G., Moltó, E., Gómez-Chova, L., Aleixos, N., & Blasco, J. (2008a). Segmentation of hyperspectral images for the detection of rotten mandarins. Lecture Notes in Computer Science, 5112, 1071–1080.CrossRefGoogle Scholar
  30. Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., et al. (2008b). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86.CrossRefGoogle Scholar
  31. Gómez-Sanchis, J., Moltó, E., Camps-Valls, G., Gómez-Chova, L., Aleixos, N., & Blasco, J. (2008c). Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. Journal of Food Engineering, 85(2), 191–200.CrossRefGoogle Scholar
  32. Gómez-Sanchis, J., Martín-Guerrero, J. D., Soria-Olivas, E., Martínez-Sober, M., Magdalena-Benedito, R., & Blasco, J. (2012). Detecting rottenness caused by Penicillium in citrus fruits using machine learning techniques. Expert Systems with Applications, 39(1), 780–785.Google Scholar
  33. Gonzalez, R. C., & Woods, R. E. (2008). Digital image processing (3rd ed.). Upper Saddle River: Prentice Hall.Google Scholar
  34. Gowen, A. A., O’Donnell, C. P., Cullen, P. J., Downey, G., & Frias, J. M. (2007). Hyperspectral imaging—An emerging process analytical tool. Trends in Food Science & Technology, 18(12), 590–598.CrossRefGoogle Scholar
  35. Gowen, A. A., O’Donnell, C. P., Taghizadeh, M., Cullen, P. J., Frias, J. M., & Downey, G. (2008). Hyperspectral imaging combined with principal component analysis for bruise damage detection on white mushrooms (Agaricus bisporus). Journal of Chemometrics, 22(3–4), 259–267.CrossRefGoogle Scholar
  36. Gowen, A. A., Taghizadeh, M., & O’Donnell, C. P. (2009a). Identification of mushrooms subjected to freeze damage using hyperspectral imaging. Journal of Food Engineering, 93, 7–12.CrossRefGoogle Scholar
  37. Gowen, A. A., Tsenkova, R., Esquerre, C., Downey, G., & O’Donnell, P. D. (2009b). Use of near infrared hyperspectral imaging to identify water matrix co-ordinates in mushrooms (Agaricus bisporus) subjected to mechanical vibration. Journal of Near Infrared Spectroscopy, 17(6), 363–371.CrossRefGoogle Scholar
  38. Grahn, H. F., & Geladi, P. (2007). Techniques and applications of hyperspectral image analysis. Chichester: Wiley.CrossRefGoogle Scholar
  39. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.Google Scholar
  40. Hetch, E. (2001). Optics (4th ed.). Reading: Addison Wesley.Google Scholar
  41. Huang, Y., Kangas, L. J., & Rasco, B. A. (2007). Applications of artificial neural networks (ANNs) in food science. Critical Reviews in Food Science and Nutrition, 47(2), 113–126.CrossRefGoogle Scholar
  42. Jiménez, A., Beltrán, G., Aguilera, M. P., & Uceda, M. (2008). A sensor-software based on artificial neural network for the optimization of olive oil elaboration process. Sensors and Actuators B, 129, 985–990.CrossRefGoogle Scholar
  43. Jobson, J. D. (1992). Applied multivariate data analysis: Categorical and multivariate methods, vol. Berlin: Springer.Google Scholar
  44. Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York: Springer.Google Scholar
  45. Kalkan, H., Beriat, P., Yardimci, Y., & Pearson, T. C. (2011). Detection of contaminated hazelnuts and ground red chili pepper flakes by multispectral imaging. Computers and Electronics in Agriculture, 77(1), 28–34.CrossRefGoogle Scholar
  46. Karimi, Y., Maftoonazad, N., Ramaswamy, H. S., Prasher, S. O., & Marcotte, M. (2009). Application of hyperspectral technique for color classification avocados subjected to different treatments. Food and Bioprocess Technology. doi:10.1007/s11947-009-0292-x.
  47. Kays, S. J. (1999). Preharvest factors affecting appearance. Postharvest Biology and Technology, 15, 233–247.CrossRefGoogle Scholar
  48. Kim, M. S., Chen, Y. R., & Mehl, P. M. (2001). Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Transactions of ASAE, 44(3), 721–729.Google Scholar
  49. Kleynen, O., Leemans, V., & Destain, M. F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69, 41–49.CrossRefGoogle Scholar
  50. Lee, J. A., & Verleysen, M. (2007). Nonlinear dimensionality reduction. New York: Springer.CrossRefGoogle Scholar
  51. Lefcout, A. M., Kim, M. S., Chen, Y.-R., & Kang, B. (2006). Systematic approach for using hyperspectral imaging data to develop multispectral imagining systems: Detection of feces on apples. Computers and Electronics in Agriculture, 54, 22–35.CrossRefGoogle Scholar
  52. Li, J., Rao, X., & Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78(1), 38–48.CrossRefGoogle Scholar
  53. Liu, Y., Chen, Y. R., Wang, C. Y., Chan, D. E., & Kim, M. S. (2005). Development of a simple algorithm for the detection of chilling injury in cucumbers from visible/near-infrared hyperspectral imaging. Applied Spectroscopy, 59(1), 78–85.Google Scholar
  54. Liu, Y., Chen, Y. R., Wang, C. Y., Chan, D. E., & Kim, M. S. (2006). Development of hyperspectral imaging technique for the detection of chilling injury in cucumbers: Spectral and image analysis. Applied Engineering in Agriculture, 22(1), 101–111.Google Scholar
  55. Lleó, L., Barreiro, P., Ruiz-Altisent, M., & Herrero, A. (2009). Multispectral images of peach related to firmness and maturity at harvest. Journal of Food Engineering, 93(2), 229–235.CrossRefGoogle Scholar
  56. Lleó, L., Roger, J. M., Herrero-Langreo, A., Diezma-Iglesias, B., & Barreiro, P. (2011). Comparison of multispectral indexes extracted from hyperspectral images for the assessment of fruit ripening. Journal of Food Engineering, 104(4), 612–620.CrossRefGoogle Scholar
  57. Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., & Blasco, J. (2011). Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food and Bioprocess Technology. doi:10.1007/s11947-011-0737-x.
  58. Lu, R. (2003). Detection of bruises on apples using near-infrared hyperspectral imaging. Transactions of the ASAE, 46, 523–530.Google Scholar
  59. Lu, R., & Peng, Y. (2006). Hyperspectral scattering for assessing peach fruit firmness. Biosystems Engineering, 93(2), 161–171.CrossRefGoogle Scholar
  60. Lunadei, L., Diezma, B., Lleó, L., Ruiz-Garcia, L., Cantalapiedra, S., & Ruiz-Altisent, M. (2012). Monitoring of fresh-cut spinach leaves through a multispectral vision system. Postharvest Biology and Technology, 63, 74–84.CrossRefGoogle Scholar
  61. Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. (2011). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—A review. Food and Bioprocess Technology. doi:10.1007/s11947-011-0697-1.
  62. Manickavasagan, A., Jayas, D. S., White, N. D. G., & Paliwal, J. (2010). Wheat class identification using thermal imaging. Food and Bioprocess Technology, 3(3), 450–460.CrossRefGoogle Scholar
  63. Martinez, A. M., & Kak, A. C. (2004). PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 228–233.CrossRefGoogle Scholar
  64. Martínez-Usó, A., Pla, F., & García-Sevilla, P. (2005). Multispectral iSegmentation by energy minimization for fruit quality estimation. In: Pattern Recognition and Image Analysis: Second Iberian Conference (IbPRIA 2005), Estoril, Portugal, June 7–9, 2005. LNCS, 3523, 689–696.Google Scholar
  65. Mather, P. M. (1998). Computer processing of remotely sensed images. Chichester: Wiley.Google Scholar
  66. McLachlan, G. J. (2004). Discriminant analysis and statistical pattern recognition. New Jersey: Wiley-Interscience.Google Scholar
  67. Mehl, P. M., Chen, Y. R., Kim, M. S., & Chan, D. E. (2004). Development of hyperspectral imaging technique for detection of apple surface defects and contaminations. Journal of Food Engineering, 61, 67–81.CrossRefGoogle Scholar
  68. 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
  69. Menesatti, P., Zanella, A., D’Andrea, S., Costa, C., Paglia, G., & Pallottino, F. (2009). Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples. Food and Bioprocess Technology, 2, 308–314.CrossRefGoogle Scholar
  70. Nguyen Do Trong, N., Tsuta, M., Nicolaï, B. M., De Baerdemaeker, J., & Saeys, W. (2011). Prediction of optimal cooking time for boiled potatoes by hyperspectral imaging. Journal of Food Engineering, 105(4), 617–624.CrossRefGoogle Scholar
  71. Nicolaï, B. M., Lötze, 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, 1–6.CrossRefGoogle Scholar
  72. Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., et al. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46(2), 99–118.CrossRefGoogle Scholar
  73. Noh, H. K., & Lu, R. (2007). Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality. Postharvest Biology and Technology, 43, 193–201.CrossRefGoogle Scholar
  74. Noh, H., Peng, Y., & Lu, R. (2007). Integration of hyperspectral reflectance and fluorescence imaging for assessing apple maturity. Transactions of the ASABE, 50(3), 963–971.Google Scholar
  75. Ozaki, Y., McClure, W. F., & Christy, A. A. (Eds.). (2006). Near-infrared spectroscopy in food science and technology. New Jersey: Wiley-Interscience.Google Scholar
  76. Paulus, I., De Busscher, R., & Schrevens, E. (1997). Use of image analysis to investigate human quality classification of apples. Journal of Agricultural Engineering Research, 68, 341–353.CrossRefGoogle Scholar
  77. Peirs, A., Scheerlinck, N., De Baerdemaeker, J., & Nicolaï, B. M. (2003). Starch index determination of apple fruit by means of a hyperspectral near infrared reflectance imaging system. Journal of near infrared spectroscopy, 11(5), 379–389.CrossRefGoogle Scholar
  78. Peng, Y., & Lu, R. (2005). Modeling multispectral scattering profiles for prediction of apple fruit firmness. Transactions of ASAE, 48(1), 235–242.Google Scholar
  79. Peng, Y., & Lu, R. (2006). An LCTF-based multispectral imaging system for estimation of apple fruit firmness: Part I. Acquisition and characterization of scattering images. Transactions of ASAE, 49(1), 259–267.Google Scholar
  80. Peng, Y., & Lu, R. (2008). Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 48, 52–56.CrossRefGoogle Scholar
  81. Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps-Valls, G., et al. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113(1), S110–S122.CrossRefGoogle Scholar
  82. Polder, G., van der Heijden, G. W. A. M., & Young, I. T. (2002). Spectral image analysis for measuring ripeness of tomatoes. Transactions of ASAE, 45, 1155–1161.Google Scholar
  83. 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.CrossRefGoogle Scholar
  84. Polder, G., van der Heijden, G. W. A. M., van der Voet, H., & Young, I. T. (2004). Measuring surface distribution of carotenes and chlorophyll in ripening tomatoes using imaging spectrometry. Postharvest Biology and Technology, 34, 117–129.CrossRefGoogle Scholar
  85. Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107, 1–23.CrossRefGoogle Scholar
  86. Qin, J., & Lu, R. (2005). Detection of pits in tart cherries by hyperspectral transmission imaging. Transactions of ASAE, 48(5), 1963–1970.Google Scholar
  87. 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, 183–191.CrossRefGoogle Scholar
  88. Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2012). Development of a two-band spectral imaging system for real-time citrus canker detection. Journal of Food Engineering, 108(1), 87–93.CrossRefGoogle Scholar
  89. Quevedo, R., & Aguilera. (2010). Color computer vision and stereoscopy for estimating firmness in the salmon (Salmon salar) fillets. Food and Bioprocess Technology, 3(4), 561–567.CrossRefGoogle Scholar
  90. Quevedo, R., Aguilera, J. M., & Pedreschi, F. (2010). Color of salmon fillets by computer vision and sensory panel. Food and Bioprocess Technology, 3(5), 637–643.CrossRefGoogle Scholar
  91. 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.CrossRefGoogle Scholar
  92. Russ, J. C. (2011). The image processing handbook (6th ed.). Boca Raton: CRC.Google Scholar
  93. Shaw, P. J. A. (2003). Multivariate statistics for the environmental sciences. New York: Hodder-Arnold.Google Scholar
  94. Shih, F. Y. (2010). Image processing and pattern recognition: Fundamentals and techniques. New York: Wiley-IEEE.CrossRefGoogle Scholar
  95. Sjöström, M., Wold, S., & Söderström, B. (1986). PLS discriminant plots. In E. S. Gelsema & L. N. Kanal (Eds.), Pattern recognition in practice I (pp. 461–470). Amsterdam: Elsevier.Google Scholar
  96. Sugiyama, T., Sugiyama, J., Tsuta, M., Fujita, K., Shibata, M., Kokawa, M., et al. (2010). NIR spectral imaging with discriminant analysis for detecting foreign materials among blueberries. Journal of Food Engineering, 101(3), 244–252.CrossRefGoogle Scholar
  97. Sun, D.-W. (Ed.). (2007). Computer vision technology for food quality evaluation. London: Academic.Google Scholar
  98. Sun, D.-W. (Ed.). (2009). Infrared spectroscopy for food quality analysis and control. London: Academic.Google Scholar
  99. Sun, D.-W. (Ed.). (2010). Hyperspectral imaging for food quality analysis and control. London: Academic.Google Scholar
  100. Taghizadeh, M., Gowen, A. A., & O’Donnell, C. P. (2011a). Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms. Biosystems Engineering, 108(2), 191–194.CrossRefGoogle Scholar
  101. Taghizadeh, M., Gowen, A. A., & O’Donnell, C. P. (2011b). 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.CrossRefGoogle Scholar
  102. Unay, D., & Gosselin, B. (2006). Automatic defect segmentation of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharvest Biology and Technology, 42, 271–279.CrossRefGoogle Scholar
  103. Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M. F., & Debeir, O. (2011). Automatic grading of bi-colored apples by multispectral machine vision. Computers and Electronics in Agriculture, 75(1), 204–212.CrossRefGoogle Scholar
  104. Vila, J., Calpe, J., Pla, F., Gómez, L., Connell, J., Marchant, J. A., et al. (2005). SmartSpectra: Applying multispectral imaging to industrial environments. Real-Time Imaging, 11, 85–98.CrossRefGoogle Scholar
  105. Vila-Francés, J., Calpe-Maravilla, J., Gómez-Chova, L., & Amorós-López, J. (2010). Analysis of acousto-optic tunable filter performance for imaging applications. Optical Engineering, 49(11), 113203–113203-9.CrossRefGoogle Scholar
  106. Vila-Francés, J., Calpe-Maravilla, J., Gómez-Chova, L., & Amorós-López, J. (2011). Design of a configurable multispectral imaging system based on an AOTF. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 58(1), 259–262.CrossRefGoogle Scholar
  107. Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (Eds.). (2010). Handbook of partial least squares. Berlin: Springer.Google Scholar
  108. Wang, J., Nakano, K., Ohashi, S., Kubota, Y., Takizawa, K., & Sasaki, Y. (2011a). Detection of external insect infestations in jujube fruit using hyperspectral reflectance imaging. Biosystems Engineering, 108(4), 345–351.CrossRefGoogle Scholar
  109. Wang, W., Li, C., Tollner, E. W., Rains, G. C., & Gitaitis, R. D. (2011b). A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization. Computers and Electronics in Agriculture. doi:10.1016/j.compag.2011.09.003.
  110. Wang, W., Ca, L., Tollner, E. W., Rains, G. C., & Gitaitis, R. D. (2011c). A liquid crystal tunable filter based shortwave infrared spectral imaging system: Design and integration. Computers and Electronics in Agriculture. doi:10.1016/j.compag.2011.07.012.
  111. Wang, W., Li, C., Tollner, E. W., Gitaitis, R. D., & Rains, G. C. (2012). Shortwave infrared hyperspectral imaging for detecting sour skin (burkholderia cepacia)-infected onions. Journal of Food Engineering, 109(1), 36–48.Google Scholar
  112. Xing, J., & De Baerdemaeker, J. (2005). Bruise detection on ‘Jonagold’ apples using hyperspectral imaging. Postharvest Biology and Technology, 37(2), 152–162.CrossRefGoogle Scholar
  113. Xing, J., Bravo, C., Jancsók, 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.CrossRefGoogle Scholar
  114. Xing, J., Jancsók, P. T., & De Baerdemaeker, J. (2007). Stem-end/calyx identification on apples using contour analysis in multispectral images. Biosystems Engineering, 96(2), 231–237.CrossRefGoogle Scholar
  115. 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.CrossRefGoogle Scholar
  116. Zhao, J., Vittayapadung, S., Quansheng, C., Chaitep, S., & Chuaviroj, R. (2009). Nondestructive measurement of sugar content of apple using hyperspectral imaging technique. Maejo International Journal of Science and Technology, 3(1), 130–142.Google Scholar
  117. Zhao, J., Ouyang, Q., Chen, Q., & Wang, J. (2010). Detection of bruise on pear by hyperspectral imaging sensor with different classification algorithms. Sensor Letters, 8, 570–576.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • D. Lorente
    • 1
  • N. Aleixos
    • 2
  • J. Gómez-Sanchis
    • 3
  • S. Cubero
    • 1
  • O. L. García-Navarrete
    • 1
    • 4
  • J. Blasco
    • 1
  1. 1.Centro de AgroingenieríaInstituto Valenciano de Investigaciones AgrariasMoncadaSpain
  2. 2.Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser HumanoUniversitat Politècnica de ValènciaValenciaSpain
  3. 3.Intelligent Data Analysis Laboratory, IDAL, Electronic Engineering DepartmentUniversitat de ValènciaBurjassot (Valencia)Spain
  4. 4.Departamento de Ingeniería Civil y AgrícolaUniversidad Nacional de Colombia-Sede BogotáBogotáColombia

Personalised recommendations