Food and Bioprocess Technology

, Volume 5, Issue 2, pp 425–444

NIR Spectroscopy Applications for Internal and External Quality Analysis of Citrus Fruit—A Review

  • Lembe S. Magwaza
  • Umezuruike Linus Opara
  • Hélène Nieuwoudt
  • Paul J. R. Cronje
  • Wouter Saeys
  • Bart Nicolaï
Review Paper


The global citrus industry is continually confronted by new technological challenges to meet the ever-increasing consumer awareness and demand for quality-assured fruit. To face these challenges, recent trend in agribusiness is declining reliance on subjective assessment of quality and increasing adoption of objective, quantitative and non-destructive techniques of quality assessment. Non-destructive instrument-based methods are preferred to destructive techniques because they allow the measurement and analysis of individual fruit, reduce waste and permit repeated measures on the same item over time. A wide range of objective instruments for sensing and measuring the quality attributes of fresh produce have been reported. Among non-destructive quality assessment techniques, near-infrared (NIR) spectroscopy (NIRS) is arguably the most advanced with regard to instrumentation, applications, accessories and chemometric software packages. This paper reviews research progress on NIRS applications in internal and external quality measurement of citrus fruit, including the selection of NIR characteristics for spectra capture, analysis and interpretation. A brief overview on the fundamental theory, history, chemometrics of NIRS including spectral pre-processing methods, model calibration, validation and robustness is included. Finally, future prospects for NIRS-based imaging systems such as multispectral and hyperspectral imaging as well as optical coherence tomography as potential non-destructive techniques for citrus quality assessment are explored.


Non-destructive evaluation Near-infrared spectroscopy NIRS Citrus fruit Internal quality External quality Hyperspectral Multispectral Optical coherence tomography (OCT) X-ray computed tomography (CT) 


  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, 121–137.Google Scholar
  2. Alferéz, F., Agustí, M., & Zacarìas, L. (2003). Postharvest rind staining in ‘Navel’ oranges is aggravated by changes in storage relative humidity: effect on respiration, ethylene production and water potential. Postharvest Biology and Technology, 28, 143–152.Google Scholar
  3. Alferéz, F., & Burns, J. (2004). Postharvest peel pitting at non-chilling temperatures in grapefruit is promoted by changes from low to high relative humidity during storage. Postharvest Biology and Technology, 32, 79–87.Google Scholar
  4. Antonucci, F., Pallottino, F., Paglia, G., Palma, A., D’Aquino, S., & Menesatti, P. (2010). Non-destructive estimation of mandarin maturity status through portable VIS-NIR spectrophotometer. Food and Bioprocess Technology, 3. doi:10.1007/s11947-010-0414-5.
  5. Balasundaram, D., Burks, T. F., Bulanon, D. M., Schubert, T., & Lee, W. S. (2009). Spectral reflectance characteristics of citrus canker and other peel condition of grapefruit. Postharvest Biology and Technology, 51, 220–226.Google Scholar
  6. Blanco, M., & Villarroya, I. (2002). NIR spectroscopy: a rapid-response analytical tool. Trends in Analytical Chemistry, 21, 240–250.Google Scholar
  7. Blasco, J., Aleixos, N., & Moltó, E. (2007). Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering, 81, 535–543.Google Scholar
  8. 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, 384–393.Google Scholar
  9. Blasco, J., Aleixos, N., Gómez-Sanchís, J., & Moltó, E. (2009). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 10, 137–145.Google Scholar
  10. Bobelyn, E., Serban, A., Nicu, M., Lammertyn, J., Nicolaï, B. M., & Saeys, W. (2010). Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biology and Technology, 55, 133–143.Google Scholar
  11. Bulanon, D. M., Burks, T. F., & Alchanatis. (2010). A multispectral imaging analysis for enhancing citrus fruit detection. Environmental Control and Biology, 48(2), 81–91.Google Scholar
  12. Butz, P., Hofmann, C., & Tauscher, B. (2005). Recent developments in non-invasive techniques for fresh fruit and vegetable internal quality analysis. Concise Reviews in Food Science, 70, 131–141.Google Scholar
  13. Camps, C., & Christen, D. (2009). Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy. Food Science and Technology, 42, 1125–1131.Google Scholar
  14. Carlini, P., Massantini, R., & Mencarelli, F. (1999). Wavelength selection methods for PLS-based vis–NIR evaluation of SSC in fresh fruits. In: Proceedings of the NIR’99, 9th International Conference on Near-Infrared Spectroscopy, Verona, Italy, 13–18 June 1999.Google Scholar
  15. Carlini, P., Massantini, R., & Mencarelli, F. (2000). Vis–NIR measurement of soluble solids in cherry and apricot by PLS regression and wavelength selection. Journal of Agricultural and Food Chemistry, 48, 5236–5242.Google Scholar
  16. Cayuela, J. A. (2008). Vis–NIR soluble solids prediction in intact oranges (Citrus sinensis L.) cv. Valencia Late by reflectance. Postharvest Biology and Technology, 47, 75–80.Google Scholar
  17. Cayuela, J. S., & Weiland, C. (2010). Intact orange quality prediction with two portable NIR spectrometers. Postharvest Biology and Technology, 58, 113–120.Google Scholar
  18. Cen, H., He, Y., & Huang, M. (2006). Measurements of soluble solids contents and pH in orange juice using chemometrics and Vis–NIRS. Journal of Agricultural Food Chemistry, 54, 7437–7443.Google Scholar
  19. Cen, H., Bao, Y., He, Y., & Sun, D.-W. (2007). Visible and near infrared spectroscopy for rapid detection of citric and tartaric acids in orange juice. Journal of Food Engineering, 82, 253–260.Google Scholar
  20. Centner, V., Massart, D. L., de Noord, O. E., de Jong, S., Vandeginste, M. B., & Sterna, C. (1996). Elimination of uninformative variables for multivariate calibration. Analytical Chemistry, 68, 3851–3858.Google Scholar
  21. Clark, C. J., McGlone, V. A., DeSilva, H. N., Manning, M. A., Burdon, J., & Mowat, A. D. (2004). Prediction of storage disorders of kiwifruit (Actanidia cinensis) based on visible-NIR spectral characteristics at harvest. Postharvest Biology and Technology, 32, 147–158.Google Scholar
  22. Cronje, P.J.R. (2005). Peteca spot of lemons. South African Fruit Journal (Feb/March issue) 26–28Google Scholar
  23. Cronje, P.J.R. (2009). Postharvest rind breakdown of ‘Nules Clementine’ mandarins (Citrus reticulate Blanco) fruit. Ph.D. thesis, Department of Horticultural Science, University of Stellenbosch, Stellenbosch, South Africa.Google Scholar
  24. Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2010). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology. doi:10.1007/s11947-010-0411-8.
  25. De Jong, S. (1993). PLS fits closer than PCR. Journal of Chemometrics, 7, 551–557.Google Scholar
  26. Dull, G. G., & Birth, G. S. (1989). Nondestructive evaluation of fruit quality: use of near infrared spectrophotometry to measure soluble solids in intact honeydew melons. HortScience, 24, 754.Google Scholar
  27. Dull, G., Birth, G., & Leffler, R. (1989). Use of near infrared analysis for the non-destructive measurement of dry matter in potatoes. American Potato Journal, 66, 215–225.Google Scholar
  28. Dull, G. G., Birth, G. S., Smittle, D. A., & Leffler, R. G. (1989). Near infrared analysis of soluble solids of intact cantaloupe. Journal of Food Science, 54, 393–395.Google Scholar
  29. Fercher, A. F., Drexler, W., Hitzenberger, C. K., & Lasser, T. (2003). Optical coherence tomography—principles and applications. Reports on Progress in Physics, 66, 239–303.Google Scholar
  30. Fraser, D. G., Künnemeyer, R., McGlone, V. A., & Jordan, R. B. (2001). Letter to the editor. Postharvest Biology and Technology, 22, 191–195.Google Scholar
  31. Fraser, D. G., Jordan, R. B., Künnemeyer, R., & McGlone, V. A. (2003). Light distribution inside mandarin fruit during internal quality assessment by NIR spectroscopy. Postharvest Biology and Technology, 27, 185–196.Google Scholar
  32. Fu, X., Ying, Y., Lu, H., & Xu, X. (2007). Comparison of diffuse reflectance and transmission mode of visible-near infrared spectroscopy for detecting brown heart of pear. Journal of Food Engineering, 83, 317–323.Google Scholar
  33. Gaffney, J. J. (1973). Reflectance properties of citrus fruit. Transactions of the American Society of Agricultural Engineers, 16(2), 310–314.Google Scholar
  34. Geeola, F., Geeola, F., & Peiper, U. M. (1994). A spectrophotometric method for detecting surface bruises on ‘Golden Delicious’ apples. Journal of Agricultural Engineering Research, 58, 47–51.Google Scholar
  35. Golic, M., Walsh, K. B., & Lawson, P. (2003). Short-wavelength near-infrared spectra of sucrose, glucose, and fructose with respect to sugar concentration and temperature. Applied spectroscopy, 57, 139–145.Google Scholar
  36. Golic, M., & Walsh, K. B. (2006). Robustness of calibration medels based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids contents. Analytica Chimica Acta, 555, 286–291.Google Scholar
  37. Gómez, A. H., He, Y., & Pereira, A. G. (2006). Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using vis–NIR spectroscopy techniques. Journal of Food Engineering, 77, 313–319.Google Scholar
  38. Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., et al. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86.Google Scholar
  39. 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, 590–598.Google Scholar
  40. Greensill, C. V., & Walsh, K. B. (2002). Calibration transfer between miniature photodiode array-based spectrometers in the near infrared assessment of mandarin soluble solids content. Journal of Near Infrared Spectroscopy, 10, 27–35.Google Scholar
  41. Guthrie, J., & Walsh, K. (1997). Non-invasive assessment of pineapple and mango fruit quality using near infrared spectroscopy. Australian Journal of Experimental Agriculture, 37, 253–263.Google Scholar
  42. Guthrie, J. A., Wedding, B., & Walsh, K. B. (1998). Robustness of NIR calibrations for soluble solids in intact melon and pineapple. Journal of Near Infrared Spectroscopy, 6, 259–265.Google Scholar
  43. Guthrie, J. A., Walsh, K. B., Reid, D. J., & Liebenberg, C. J. (2005). Assessment of internal quality attributes of mandarin fruit. 1. NIR calibration model development. Australian Journal of Agricultural Research, 56, 405–416.Google Scholar
  44. Guthrie, J. A., Reid, D. J., & Walsh, K. B. (2005). Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness. Australian Journal of Agricultural Research, 56, 417–426.Google Scholar
  45. Guthrie, J. A., Liebenberg, C. J., & Walsh, K. B. (2006). NIR model development and robustness in prediction of melon fruit total soluble solids. Australian Journal of Agricultural Research, 57, 1–8.Google Scholar
  46. Hebden, J. C., Gibson, A., Yusof, R. M., Everdell, N., Hillman, E. M. C., Delpy, D. T., et al. (2002). Three-dimensional optical tomography of the premature infant brain. Physics in Medicine and Biology, 47, 4155–4166.Google Scholar
  47. Huang, D., Swanson, E. A., Lin, C. P., Schuman, J. S., Stinson, W. G., Chang, W., et al. (1991). Optical coherence tomography. Science, 254, 1178–1181.Google Scholar
  48. Huang, H., Yu, H., Xu, H., & Ying, Y. (2008). Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. Journal of Food Engineering, 87, 303–313.Google Scholar
  49. Kader, A. A. (2002). Opportunities in using biotechnology to maintain postharvest quality and safety of fresh produce. HortScience, 37, 24–25.Google Scholar
  50. Kawano, S., Fujiwara, T., & Iwamoto, M. (1993). Non-destructive determination of sugar content in ‘Satsuma’ mandarins using NIRS transmittance. Journal of the Japanese Society for Horticultural Science, 62, 465–470.Google Scholar
  51. Kemsely, E. K., Tapp, H. S., Binns, R., Mackin, R. O., & Peyton, A. J. (2008). Feasibility study of NIR diffuse optical tomography on agricultural produce. Postharvest Biology and Technology, 48, 223–230.Google Scholar
  52. Kim, J., Mowat, A., Poole, P., & Kasabov, N. (2000). Linear and non-linear pattern recognition models for classification of fruit from visible–near infrared spectra. Chemometrics and Intelligent Laboratory Systems, 51, 201–216.Google Scholar
  53. Krivoshiev, G. P., Chalucova, R. P., & Moukarev, M. I. (2000). A possibility for elimination of the interference from the peel in nondestructive determination of the internal quality of fruit and vegetables by vis–NIR spectroscopy. Lebensm-Wiss University of technology, 33, 344–353.Google Scholar
  54. Kutis, I. S., Sapozhnikova, V. V., Kuranov, R. V., & Kamenskii, V. A. (2005). Study of the morphological and functional state of higher plant tissues by optical coherence microscopy and optical coherence tomography. Russian Journal of Plant Physiology, 52, 559–564.Google Scholar
  55. Lammertyn, J., Peirs, J., De Baerdemaeker, J., & Nicolaï, B. M. (2000). Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment. Postharvest Biology and Technology, 18, 121–132.Google Scholar
  56. Lammertyn, J., Dressalaers, T., Van Hecke, P., Jancsók, P., Wevers, M., & Nicolaï, B. M. (2003). MRI and X-ray CT study of spatial distribution of core breakdown in ‘Conference’ pears. Magnetic Resonance Imaging, 21(7), 805–815.Google Scholar
  57. Lammertyn, J., Dresselaers, T., Van Hecke, P., Jancsók, P., Wevers, M., & Nicolaï, B. M. (2003). Analysis of the time course of core breakdown in ‘Conference’ pears by means of MRI and X-ray CT. Postharvest Biology and Technology, 29, 19–28.Google Scholar
  58. Leonardi, L., & Burns, D. H. (1999). Quantitative multiwavelength consistuent measurements using single-wavelength photo time-of-flight correction. Applied Spectroscopy, 53, 637–646.Google Scholar
  59. Liu, Y., Sun, X., & Ouyang, A. (2010). Non-destructive measurements of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLS and PCA-BPNN. LWT-Food Science and Technology, 43, 602–607.Google Scholar
  60. Liu, Y., Sun, X., Zhang, H., & Aiguo, O. (2010). Nondestructive measurement of internal quality of Nanfeng mandarin fruit by charge coupled device near infrared spectroscopy. Computers and Electronics in Agriculture, 71(S1), S10–S14.Google Scholar
  61. López-García, F., Andreu-García, A., Blasco, J., Aleixos, N., & Valiente, J. M. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 71, 189–197.Google Scholar
  62. Louw, E. D., & Theron, K. I. (2010). Robust prediction models for quality parameters in Japanese plums (Prunus salicina L.) using NIR spectroscopy. Postharvest Biology and Technology, 58, 176–184.Google Scholar
  63. Lovász, T., Merész, P., & Salgó, A. (1994). Application of near infrared transmission spectroscopy for the determination of some quality parameters of apples. Journal of Near Infrared Spectroscopy, 2, 213–221.Google Scholar
  64. Lu, Q., Gan, X., Gu, M., & Luo, Q. (2004). Monte Carlo modelling of optical coherence tomography imaging through turbid media. Applied Optics, 43, 1628–1637.Google Scholar
  65. Lu, H., Xu, H., Ying, Y., Fu, X., Yu, H., & Tian, H. (2006). Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits. Journal of Zhejiang University Scence, 7, 794–799.Google Scholar
  66. Magwaza, L.S., Opara, U.L., Nieuwoudt, H., & Cronje, P. (2011). Non-destructive quality assessment of ‘Valencia’ orange using FT-NIR spectroscopy. In: Proceedings of the NIR 2011, 15th International Conference on Near-Infrared Spectroscopy, Cape Town, South Africa, 13–20 May 2011.Google Scholar
  67. McClure, W. F. (2003). Review: 204 years of near infrared technology: 1800–2003. Journal of Near Infrared spectroscopy, 11, 487–518.Google Scholar
  68. McGlone, V. A., & Kawano, S. (1998). Firmness, dry-matter and soluble solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biology and Technology, 13, 131–141.Google Scholar
  69. McGlone, V. A., Jordan, R. B., & Martinsen, P. J. (2002). Vis-NIR estimation at harvest of pre- and post-storage quality indices for ‘Royal Gala’ apple. Postharvest Biology and Technology, 25, 135–144.Google Scholar
  70. McGlone, V. A., Fraser, D. G., Jordan, R. B., & Kunnemeyer, R. (2003). Internal quality assessment of mandarin fruit by vis–NIR spectroscopy. Journal of Near Infrared Spectroscopy, 11, 323–332.Google Scholar
  71. Meglinski, I. V., Buranachai, C., & Terry, L. A. (2010). Plant photonics: application of optical tomography to monitor defects and rots in onion. Laser Physics Letters. doi:10.1002/lapl.200910141.
  72. 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, 67–81.Google Scholar
  73. Menesatti, P., Antonucci, F., Pallottino, F., Rocuzzo, G., Allegra, M., Stagno, F., et al. (2010). Estimation of plant nutritional status by vis–NIR spectroscopic analysis on orange leaves [Citrus sinensis (L.) Osbeck cv Tarocco]. Biosystems Engineering, 105, 448–454.Google Scholar
  74. Miller, B. K., & Delwiche, M. J. (1991). Spectral analysis of peach surface defects. Transactions of the American Society for Agricultural Engineering, 34, 2509–2515.Google Scholar
  75. Miller, W. M., & Zude, M. (2002). Non-destructive brix sensing of Florida grapefruit and honey tangerines. Proceedings of the Florida state Horticultural society, 115, 56–60.Google Scholar
  76. Miyamoto, K., & Kitano, Y. (1995). Non-destructive determination of sugar content in Satsuma mandarin fruit by near infrared transmittance spectroscopy. Journal of Near Infrared Spectroscopy, 3, 227–237.Google Scholar
  77. Miyamoto, K., Kawauchi, M., & Fukuda, T. (1998). Classification of high acid fruits by PLS using the near infrared transmittance spectra of intact Satsuma mandarins. Journal of Near Infrared Spectroscopy, 6(1–4), 267–271.Google Scholar
  78. Moon, D. G., & Mizutani, F. (2002). Relationship between fruit shape acid content in different parts of citrus fruits. Journal of the Japanese Society for Horticultural Science, 71, 56–58.Google Scholar
  79. 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.Google Scholar
  80. Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, I. K., et al. (2007). Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46, 99–118.Google Scholar
  81. Nicolaï, B. M., Theron, K. I., & Lammertyn, J. (2007). Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemometrics and Intelligent Laboratory Systems, 85, 243–252.Google Scholar
  82. Nicolaï, B. M., Verlinden, B. E., Desmet, M., Saevels, S., Saeys, W., Theron, K., et al. (2008). Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biology and Technology, 47, 68–74.Google Scholar
  83. Nicolaï, B. M., Bulens, I., De Baerdemaker, J., De Ketelaere, B., Hertog, M. L. A. T. M., Verboven, P., et al. (2009). Non-destructive evaluation: detection of external and internal attributes frequently associated with quality and damage. In D. Florkowiski (Ed.), Postharvest Handling: A Systems Approach (pp. 421–442). Amsterdam: Academic Press, Elsevier.Google Scholar
  84. Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L., & Engelsen, S. B. (2000). Interval partial least-square (iPLS): a comparative chemometric study with an example from near infrared spectroscopy. Applied Spectroscopy, 54, 413–419.Google Scholar
  85. Norris, K. H., & Hart, J. R. (1965). Direct spectrophotometric determination of moisture content of grain and seeds. In Humidity and moisture, vol 4. Principles and methods of measuring moisture in liquids and solids. New York: Reinhold.Google Scholar
  86. Osborne, S. D., Jordan, R. B., & Kunnemeyer, R. (1997). Method of wavelength selection for partial least squares. Analyst, 122, 1531–1537.Google Scholar
  87. Osborne, S. D., Kunnemeyer, R., & Jordan, R. B. (1999). A low-cost system for the grading of kiwifruit. Journal of Near Infrared Spectroscopy, 7, 9–15.Google Scholar
  88. Osborne, B. G. (2000). Near infrared spectroscopy in food analysis (pp. 1–14). Australia: BRI Australia Ltd.Google Scholar
  89. Ou, A. S., Lin, S., Lin, T., Wu, S., & Tiarn, M. (1997). Studies on the determination of quality-related constituents in ‘Ponkan’ mandarin by near infrared spectroscopy. Journal of the Chinese Agricultural Chemical Society, 35, 462–474.Google Scholar
  90. Pallav, P., Diamond, G. G., Hutchins, D. A., Green, R. J., & Gan, T. J. (2009). A near infrared (NIR) technique for imaging food materials. Journal of Food Science, 74, 23–33.Google Scholar
  91. Palmer, K. F., & Williams, D. (1974). Optical properties of water in the near infrared. Journal of the Optical Society of America, 64, 1107–1110.Google Scholar
  92. Peirera, A. F. C., Pontes, M. J. C., Neto, F. F. G., Santos, S. R. B., Galvaõ, R. K. H., & Araújo, M. C. U. (2008). NIR spectrometric determination of quality parameters in vegetable oils using iPLS and variable selection. Food Research International, 41, 341–348.Google Scholar
  93. Peiris, K. H. S., Dull, G. G., Leffler, R. G., & Kays, S. J. (1998a). Near-infrared spectrometric method for nondestructive determination of soluble solids content of peaches. American society for Horticultural Science, 123, 898–905.Google Scholar
  94. Peiris, K. H. S., Dull, G. G., Leffler, R. G., & Kays, S. J. (1998b). Near-infrared (NIR) spectrometric technique for non-destructive determination of soluble solids content in processing tomatoes. American society for Horticultural Science, 123, 1089–1093.Google Scholar
  95. Peiris, K. H. S., Dull, G. G., & Leffler, R. G. (1998c). Nondestructive detection of selection drying, an internal disorder in tangerine. HortScience, 33, 310–312.Google Scholar
  96. Peiris, K. H. S., Dull, G. G., Leffler, R. G., & Kays, S. J. (1999). Spatial variability of soluble solids or dry-matter content within individual fruits, bulbs, or tubers: Implications for the development and use of NIR spectrometric techniques. HortScience, 34, 114–118.Google Scholar
  97. Peirs, A., Tirry, J., Verlinden, B., Darius, P., & Nicolaï, B. M. (2002). Effect of biological variability on the robustness of NIR-models for soluble solids content of apples. Postharvest Biology and Technology, 28, 269–280.Google Scholar
  98. Peirs, A., Scheerlinck, N., & Nicolaï, B. M. (2003). Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents. Postharvest Biology and Technology, 30, 233–248.Google Scholar
  99. 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. Sensory and Instrumental Food Quality, 2, 168–177.Google Scholar
  100. 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.Google Scholar
  101. Saiz-Abajo, M. J., Mevick, B.-H., Segtnan, V. H., & Naes, T. (2005). Ensemble methods and data augmentation by noise addition applied to the analysis of spectroscopic data. Analytica Chimica Acta, 533, 147–159.Google Scholar
  102. Sapozhnikova, V. V., Kamenskii, V. A., & Kuranov, R. V. (2003). Visualization of plant tissues by optical coherence tomography. Russian Journal of Plant Physiology, 50, 282–286.Google Scholar
  103. Sapozhnikova, V. V., Kamensky, V. A., Kuranov, R. V., Kutis, I., Snopova, L. B., & Myakov, A. V. (2004). In vivo visualization of Tradescantia leaf tissue and monitoring the physiological and morphological states under different water supply conditions using optical coherence tomography. Planta, 219, 601–609.Google Scholar
  104. Schaare, P. N., & Fraser, D. G. (2000). Comparison of reflectance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis). Postharvest Biology and Technology, 20, 175–184.Google Scholar
  105. Smith, L. G. (1984). Pineapple sepcifiuc gravity as an index of eating quality. Tropical Agriculture (Trinidad), 61, 196–199.Google Scholar
  106. Spreen, T.H. (2009). Projections of world production and consumption of citrus to 2010. China/FAO citrus symposium. Food and Agricultural Organisation of the United nations.
  107. Steuer, B., Schulz, H., & Läger, E. (2001). Classification and analysis of citrus oils by NIR spectroscopy. Food Chemistry, 72, 113–117.Google Scholar
  108. Sun, X., Zhang, H., & Liu, Y. (2009). Nondestructive assessment of quality of ‘Nanfeng’ mandarin fruit by a portable near infrared spectroscopy. International Journal of Agricultural and Biological Engineering, 2, 65–71.Google Scholar
  109. Tewari, J. C., Dixit, V., Chi, B.-K., & Malik, K. A. (2008). Determination of origin and sugars of citrus fruit using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 71, 1119–1127.Google Scholar
  110. Tomlins, P. H., & Wang, R. K. W. (2005). Theory, developments and applications of optical coherence tomography. Journal of Physics. D: Applied Physics, 38, 2519–2535.Google Scholar
  111. Tsuchikawa, S., Sakai, E., Inoue, K., & Miyamoto, K. (2003). Application of time-of-flight near-infrared spectroscopy to detect sugar and acid content in Satsuma mandarin. Journal of the American Society for Horticultural Science, 128, 391–396.Google Scholar
  112. Walsh, K. B., Guthrie, J. A., & Burney, J. W. (2000). Application of commercially available, low cost, miniaturised NIR spectrometers to the assessment of the sugar content of intact fruit. Australian Journal of Plant Physiology, 27, 1175–1186.Google Scholar
  113. Walsh, K.B. (2005). Commercial adoption of technologies for fruit grading, with emphasis on NIRS. Information and technology for sustainable fruit and vegetable production, FRUTIC 05, Montpellier, France, 12–16 September 2005Google Scholar
  114. Wang, W., & Paliwal, J. (2007). Near-infrared spectroscopy and imaging in food quality and safety. Sensory and Instrumental Food Chemistry, 1, 193–207.Google Scholar
  115. Wetzel, D. L. (1983). Near infrared reflectance analysis: sleeper among spectroscopic techniques. Analytical Chemistry, 55, 1165–1176.Google Scholar
  116. Williams, P. C., & Norris, K. H. (1987). Qualitative applications of near-infrared reflectance spectroscopy. In P. C. Williams & K. H. Norris (Eds.), Near-infrared technology in the agricultural and food industries (pp. 241–246). St. Paul: American Association of Cereal Chemistry.Google Scholar
  117. Williams, P., & Norris, K. H. (2001). Variable affecting near infrared spectroscopic analysis. In P. Williams & K. H. Norris (Eds.), Near infrared technology in the agriculture and food industries (2nd ed., pp. 171–185). St Paul: The American Association of Cereal Chemists.Google Scholar
  118. Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130.Google Scholar
  119. Xia, J., Li, X., Li, P., Ma, Q., & Ding, X. (2007). Application of wavelet transform in the prediction of ‘Navel’ orange vitamin C content by near-infrared spectroscopy. Agricultural Sciences in China, 6(9), 1067–1073.Google Scholar
  120. Xing, J., Landahl, S., Lammertyn, J., Vrindts, E., & De Baerdemaeker, J. (2003). Effects of bruise type on discrimination of bruised and nonbruised ‘Golden Delicious’ apples by vis–NIR spectroscopy. Postharvest Biology and Technology, 30, 249–258.Google Scholar
  121. Xing, J., Bravo, C., Jancsó, P. T., Ramon, H., & De Baerdemaeker, J. (2005). Detecting bruises on ‘Golden Delicious’ apples using hyperspectral imaging with multiple wavebands. Biosystems Engineering, 90, 27–36.Google Scholar
  122. Zhao, X., Burks, T. F., Qin, J., & Ritenour, M. A. (2010). Effect of fruit harvest time on citrus canker detection using hyperspectral reflectance imaging. Sensory and Instrumental Food Quality, 4, 126–135.Google Scholar
  123. Zheng, Y., He, S., Yi, S., Zhou, Z., Mao, S., Zhao, X., et al. (2010). Predicting oleocellosis sensitivity in citrus using vis–NIR reflectance spectroscopy. Scientia Hoticulturae, 125, 401–405.Google Scholar
  124. Zude, M., Pflanz, M., Kaprielian, C., & Aivazian, B. (2008). NIRS as a tool for precision horticulture in citrus industry. Biosystem Engineering, 99, 455–459.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Lembe S. Magwaza
    • 1
  • Umezuruike Linus Opara
    • 1
    • 2
  • Hélène Nieuwoudt
    • 3
  • Paul J. R. Cronje
    • 4
  • Wouter Saeys
    • 5
  • Bart Nicolaï
    • 5
  1. 1.Postharvest Technology Research Laboratory, Department of Horticultural ScienceStellenbosch UniversityStellenboschSouth Africa
  2. 2.Postharvest Technology Research Laboratory, Department of Food ScienceStellenbosch UniversityStellenboschSouth Africa
  3. 3.Department of Viticulture and Oenology, Institute for Wine BiotechnologyStellenbosch UniversityStellenboschSouth Africa
  4. 4.Citrus Research International, Department of Horticultural ScienceStellenbosch UniversityStellenboschSouth Africa
  5. 5.VCBT-MeBioS, Biosystems DepartmentKatholieke Universiteit LeuvenHeverleeBelgium

Personalised recommendations