Advertisement

Food Analytical Methods

, Volume 9, Issue 1, pp 178–191 | Cite as

Recent Advances in the Application of Hyperspectral Imaging for Evaluating Fruit Quality

  • Nan-Nan Wang
  • Da-Wen SunEmail author
  • Yi-Chao Yang
  • Hongbin Pu
  • Zhiwei Zhu
Article

Abstract

The quality of fruit is a very important measure in determining the market value of the fruit. Good fruit quality provides great potential benefits for antioxidant properties of abundant bioactive compounds such as flavonoids, anthocyanins, polyphenols, and ascorbic acid. Therefore, giving enough attention to fruit quality and its changes is important for assuring high quality of fruits. Hyperspectral imaging (HSI) technique has showed great potential for evaluating quality attributes of fruit due to its nature of simple, continuous noninvasiveness and economy. However, for inherent peculiarity of fruit, some problems arose in the practice of estimating fruit quality by HSI, including focusing, determination of scanning parameters, image processing, data mining, and data analysis. The purpose of this paper is to review the use of HSI in the estimation of physical-chemical attributes, detection of common defects and contaminants, and evaluation of maturity stage of fruit. Remaining problems are also identified, along with suggested solutions and possible future trends in the field.

Keywords

Hyperspectral imaging (HSI) Fruit quality Physical-chemical attributes Defects and contaminants Maturity stage 

Notes

Acknowledgments

The authors gratefully acknowledge the Guangdong Province Government (China) for its support through the program “Leading Talent of Guangdong Province (Da-Wen Sun).” This research was also supported by the National Key Technologies R&D Program (2015BAD19B03), the International S&T Cooperation Programme of China (2015DFA71150), the International S&T Cooperation Projects of Guangdong Province (2013B051000010), and the Natural Science Foundation of Guangdong Province (2014A030313244).

Conflict of Interest

Nan-Nan Wang declares that she has no conflict of interest. Da-Wen Sun declares that he has no conflict of interest. Yi-Chao Yang declares that he has no conflict of interest. Hongbin Pu declares that he has no conflict of interest. Zhiwei Zhu declares that he has no conflict of interest. This article does not contain any studies with human or animal subjects.

References

  1. Baranowski P, Mazurek W, Wozniak J, Majewska U (2012) Detection of early bruises in apples using hyperspectral data and thermal imaging. J Food Eng 110(3):345–355CrossRefGoogle Scholar
  2. Barbin DF, ElMasry G, Sun D-W, Allen P (2012) Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Anal Chim Acta 719:30–42CrossRefGoogle Scholar
  3. Bernhardt P (1995) Direct reconstruction methods for hyperspectral imaging with rotational spectrotomography. J Opt Soc Am A 12(9):1884–1901CrossRefGoogle Scholar
  4. Blakey RJ, Bower JP, Bertling I (2009) Influence of water and ABA supply on the ripening pattern of avocado (Persea americana Mill.) fruit and the prediction of water content using Near Infrared Spectroscopy. Postharvest Biol Technol 53(1–2):72–76CrossRefGoogle Scholar
  5. Blasco J, Aleixos N, Gómez J, Moltó E (2007) Citrus sorting by identification of the most common defects using multispectral computer vision. J Food Eng 83(3):384–393CrossRefGoogle Scholar
  6. Cayuela JA (2008) Vis/NIR soluble solids prediction in intact oranges (Citrus sinensis L.) cv. Valencia Late by reflectance. Postharvest Biol Technol 47(1):75–80CrossRefGoogle Scholar
  7. Cayuela JA, Weiland C (2010) Intact orange quality prediction with a NIR hyperspectral imaging system. Postharvest Biol Technol 58(2):113–120CrossRefGoogle Scholar
  8. Cen HY, Lu RF, Ariana DP, Mendoza F (2014) Hyperspectral imaging-based classification and wavebands selection for internal defect detection of pickling cucumbers. Food Bioprocess Technol 7(6):1689--1700Google Scholar
  9. Cen HY, Lu RF, Mendoza FA, Ariana DP (2011) Peach maturity/quality assessment using hyperspectral imaging-based spatially resolved technique. Proc SPIE - Int Soc Opt Eng 8027:80215–80221Google Scholar
  10. Chia KS, Abdul Rahim H, Abdul Rahim R (2012) Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network. Biosyst Eng 113(2):158–165CrossRefGoogle Scholar
  11. Cubero S, Aleixos N, Molt E, Gómez-Sanchis J, Blasco J (2011) Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol 4(4):487--504Google Scholar
  12. Delgado AE, Sun D-W (2002a) Desorption isotherms for cooked and cured beef and pork. J Food Eng 51(2):163–170CrossRefGoogle Scholar
  13. Delgado AE, Sun D-W (2002b) Desorption isotherms and glass transition temperature for chicken meat. J Food Eng 55(1):1–8CrossRefGoogle Scholar
  14. Dvash L, Afik O, Shafir S, Schaffer A, Yeselson Y, Dag A, Landau S (2002) Determination by near-infrared spectroscopy of perseitol used as a marker for the botanical origin of avocado (Persea americana Mill.) honey. J Agric Food Chem 50(19):5283–5287CrossRefGoogle Scholar
  15. ElMasry G, Wang N, ElSayed A, Ngadi M (2007) Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J Food Eng 81(1):98–107CrossRefGoogle Scholar
  16. ElMasry G, Sun D-W, Allen P (2011a) Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Res Int 44(9):2624–2633Google Scholar
  17. ElMasry G, Abdullah I, Sun D-W, Allen P (2011b) Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system. J Food Eng 103(3):333–344Google Scholar
  18. 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. Crc Crit Rev Food Sci Nutr 52(11):999–1023Google Scholar
  19. ElMasry G, Sun D-W, Allen P (2012b) Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. J Food Eng 110(1):127–140CrossRefGoogle Scholar
  20. Feng YZ, Sun D-W (2012) Application of hyperspectral imaging in food safety inspection and control: a review. Crc Crit Rev Food Sci Nutr 52(11):1039–1058Google Scholar
  21. Gao H, Zhu F, & Cai J (2010) A review of non-destructive detection for fruit quality. In D. Li (Ed.), Comput Comput Technol Agric, 3, 133–140Google Scholar
  22. Gómez-Sanchis J, Lorente D, Soria-Olivas E, Aleixos N, Cubero S, Blasco J (2014) Development of a hyperspectral computer vision system based on two liquid crystal tuneable filters for fruit inspection. Application to detect citrus fruits decay. Food Bioprocess Technol 7(4):1047--1056Google Scholar
  23. Gómez-Sanchis J, Gómez-Chova L, Aleixos N, Camps-Valls G, Montesinos-Herrero C, Moltó E, Blasco J (2008a) Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. J Food Eng 89(1):80–86CrossRefGoogle Scholar
  24. Gómez-Sanchis J, Moltó E, Camps-Valls G, Gómez-Chova L, Aleixos N, Blasco J (2008b) Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. J Food Eng 85(2):191–200CrossRefGoogle Scholar
  25. Huang L, Wu D, Jin H, Zhang J, He Y, Lou C (2011) Internal quality determination of fruit with bumpy surface using visible and near infrared spectroscopy and chemometrics: a case study with mulberry fruit. Biosyst Eng 109(4):377–384CrossRefGoogle Scholar
  26. Jackman P, Sun D-W, Du C-J, Allen P (2008) Prediction of beef eating quality from colour, marbling and wavelet texture features. Meat Sci 80(4):1273–1281CrossRefGoogle Scholar
  27. Jackman P, Sun D-W, Du C-J, Allen P (2009) Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment. Pattern Recogn 42(5):751–763CrossRefGoogle Scholar
  28. Jha SN, Chopra S, Kingsly ARP (2005) Determination of Sweetness of Intact Mango using Visual Spectral Analysis. Biosyst Eng 91(2):157–161CrossRefGoogle Scholar
  29. Jha SN, Chopra S, Kingsly ARP (2007) Modeling of color values for nondestructive evaluation of maturity of mango. J Food Eng 78(1):22–26CrossRefGoogle Scholar
  30. Kamruzzaman M, ElMasry G, Sun D-W, Allen P (2011) Application of NIR hyperspectral imaging for discrimination of lamb muscles. J Food Eng 104(3):332–340CrossRefGoogle Scholar
  31. Kamruzzaman M, ElMasry G, Sun D-W, Allen P (2012) Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Anal Chim Acta 714:57–67Google Scholar
  32. Kiani H, Sun D-W (2011) Water crystallization and its importance to freezing of foods: A review. Trends Food Sci Technol 22(8):407–426CrossRefGoogle Scholar
  33. Kim MS, Chen Y-R, Cho B-K, Chao K, Yang C-C, Lefcourt AM, Chan D (2007) Hyperspectral reflectance and fluorescence line-scan imaging for online defect and fecal contamination inspection of apples. Sens & Instrumen Food Qual 1(3):151–159CrossRefGoogle Scholar
  34. Kondo N, Van Beers R, Aernouts B, De Baerdemaeker J, Saeys W (2013) Apple ripeness detection using hyperspectral laser scatter imaging. Sensing Technol Biomater Food Agric 8881:88810–88815Google Scholar
  35. Li J, Rao X, Ying Y (2011) Detection of common defects on oranges using hyperspectral reflectance imaging. Comput Electron Agric 78:38--48Google Scholar
  36. Liu Y, Ying Y (2005) Use of FT-NIR spectrometry in non-invasive measurements of internal quality of ‘Fuji’ apples. Postharvest Biol Technol 37(1):65–71CrossRefGoogle Scholar
  37. Liu Y, Chen X, Ouyang A (2008) Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry. LWT Food Sci Technol 41(9):1720–1725CrossRefGoogle Scholar
  38. Liu Y, Sun X, Ouyang A (2010) Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN. LWT Food Sci Technol 43(4):602–607CrossRefGoogle Scholar
  39. Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, Blasco J (2013) Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food Bioprocess Technol 6(2):530--541Google Scholar
  40. Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL, Blasco J (2012) Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol 5:1121–1142CrossRefGoogle Scholar
  41. Louw ED, Theron KI (2010) Robust prediction models for quality parameters in Japanese plums (Prunus salicina L.) using NIR spectroscopy. Postharvest Biol Technol 58(3):176–184CrossRefGoogle Scholar
  42. Lu RF, Peng YK (2006) Hyperspectral Scattering for assessing Peach Fruit Firmness. Biosyst Eng 93(2):161–171CrossRefGoogle Scholar
  43. Martinsen P, Schaare P (1998) Measuring soluble solids distribution in kikifruit using near-infrared imaging spectroscopy. Postharvest Biol Technol 14(1998):271–281CrossRefGoogle Scholar
  44. McDonald K, Sun D-W (2001) The formation of pores and their effects in a cooked beef product on the efficiency of vacuum cooling. J Food Eng 47(3):175–183CrossRefGoogle Scholar
  45. McDonald K, Sun D-W, Kenny T (2001) The effect of injection level on the quality of a rapid vacuum cooled cooked beef product. J Food Eng 47(2):139–147CrossRefGoogle Scholar
  46. McGlone VA, Kawano S (1998) Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biol Technol 13(1998):131–141CrossRefGoogle Scholar
  47. Moreda GP, Ortiz-Cañavate J, García-Ramos FJ, Ruiz-Altisent M (2009) Non-destructive technologies for fruit and vegetable size determination–a review. J Food Eng 92(2):119–136CrossRefGoogle Scholar
  48. Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46(2):99–118CrossRefGoogle Scholar
  49. Noh HK, Peng Y, Lu R (2007) Integration of hyperspectral reflectance and fluorescence imaging for assessing apple maturity. Trans Asabe 50(3):963–971CrossRefGoogle Scholar
  50. Peirs A, Scheerlinck N, Nicolaı̈ BM (2003) Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents. Postharvest Biol Technol 30(3):233–248CrossRefGoogle Scholar
  51. Peng Y, Lu R (2008) Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biol Technol 48(1):52–62CrossRefGoogle Scholar
  52. Rajkumar P, Wang N, Eimasry G, Raghavan GSV, Gariepy Y (2012) Studies on banana fruit quality and maturity stages using hyperspectral imaging. J Food Eng 108(1):194–200CrossRefGoogle Scholar
  53. Rasmussen M, Krolner R, Klepp KI, Lytle L, Brug J, Bere E, Due P (2006) Determinants of fruit and vegetable consumption among children and adolescents: a review of the literature. Part I: Quantitative studies. Int J Behav Nutr Phys Act 3:22CrossRefGoogle Scholar
  54. Rico D, Martín-Diana AB, Barat JM, Barry-Ryan C (2007) Extending and measuring the quality of fresh-cut fruit and vegetables: a review. Trends Food Sci Technol 18(7):373–386CrossRefGoogle Scholar
  55. Saranwong S, Sornsrivichai J, Kawano S (2004) Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy. Postharvest Biol Technol 31(2):137–145CrossRefGoogle Scholar
  56. Sirisomboon P, Tanaka M, Fujita S, Kojima T (2007) Evaluation of pectin constituents of Japanese pear by near infrared spectroscopy. J Food Eng 78(2):701–707CrossRefGoogle Scholar
  57. Slaughter DC, Thompson JF, Tan ES (2003) Nondestructive determination of total and soluble solids in fresh prune using near infrared spectroscopy. Postharvest Biol Technol 28(3):437–444CrossRefGoogle Scholar
  58. Subedi PP, Walsh KB, Owens G (2007) Prediction of mango eating quality at harvest using hyperspectral imaging. Postharvest Biol Technol 43(3):326–334CrossRefGoogle Scholar
  59. Sun D-W (1997a) Thermodynamic design data and optimum design maps for absorption refrigeration systems. Appl Therm Eng 17(3):211–221CrossRefGoogle Scholar
  60. Sun D-W (1997b) Solar powered combined ejector vapour compression cycle for air conditioning and refrigeration. Energy Convers Manag 38(5):479–491CrossRefGoogle Scholar
  61. Sun D-W (2004) Computer vision - An objective, rapid and non-contact quality evaluation tool for the food industry. J Food Eng 61(1):1–2CrossRefGoogle Scholar
  62. Sun D-W, Brosnan T (2003) Pizza quality evaluation using computer vision-part1-Pizza base and sauce spread. J Food Eng 57(1):81–89CrossRefGoogle Scholar
  63. Sun D-W, Byrne C (1998) Selection of EMC/ERH isotherm equations for rapeseed. J Agric Eng Res 69(4):307–315CrossRefGoogle Scholar
  64. Sun D-W, Woods JL (1997) Simulation of the heat and moisture transfer process during drying in deep grain beds. Dry Technol 15(10):2479–2508CrossRefGoogle Scholar
  65. Sun D-W, Eames IW, Aphornratana S (1996) Evaluation of a novel combined ejector-absorption refrigeration cycle .1. Computer simulation. Int J Refrig-Rev Int Du Froid 19(3):172–180CrossRefGoogle Scholar
  66. Valous NA, Mendoza F, Sun D-W, Allen P (2009) Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. Meat Sci 81(1):132–141CrossRefGoogle Scholar
  67. Wang HH, Sun D-W (2002) Melting characteristics of cheese: analysis of effect of cheese dimensions using computer vision techniques. J Food Eng 52(3):279–284CrossRefGoogle Scholar
  68. Wang J, Nakano K, Ohashi S (2011) Nondestructive evaluation of jujube quality by visible and near-infrared spectroscopy. LWT Food Sci Technol 44(4):1119–1125CrossRefGoogle Scholar
  69. Wei X, Liu F, Qiu Z, Shao Y, He Y (2014) Ripeness classification of astringent persimmon using hyperspectral imaging technique. Food Bioprocess Technol 7(5):1371--1380Google Scholar
  70. Wu D, Sun D-W, He Y (2012) Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet. Innov Food Sci Emerg Technol 16:361–372CrossRefGoogle Scholar
  71. Xing J, Landahl S, Lammertyn J, Vrindts E, Baerdemaeker JD (2003) Effects of bruise type on discrimination of bruised and non-bruised ‘Golden Delicious’ apples by VIS/NIR spectroscopy. Postharvest Biol Technol 30(3):249–258CrossRefGoogle Scholar
  72. Xing J, Van Linden V, Vanzeebroeck M, De Baerdemaeker J (2005) Bruise detection on Jonagold apples by visible and near-infrared spectroscopy. Food Control 16(4):357–361CrossRefGoogle Scholar
  73. Xu SY, Chen XF, Sun D-W (2001) Preservation of kiwifruit coated with an edible film at ambient temperature. J Food Eng 50(4):211–216CrossRefGoogle Scholar
  74. Xu H, Qi B, Sun T, Fu X, Ying Y (2012) Variable selection in visible and near-infrared spectra: application to on-line determination of sugar content in pears. J Food Eng 109(1):142–147CrossRefGoogle Scholar
  75. Zhou Z, Li XY, Gao HL, Tao HL, Li P, Wen DD (2012) Comparison of different variable selection methods on pear dry matter detection by hyperspectral imaging technology. Trans Chin Soc Agric Machinery 43(2):128–133Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of Light Industry and Food SciencesSouth China University of TechnologyGuangzhouChina
  2. 2.Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science CentreUniversity College Dublin, National University of IrelandDublin 4Ireland

Personalised recommendations