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


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.


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



Artificial neural networks


Analysis of variance


Acousto-optic tunable filters


Bitmap image format


Band sequential


Charge-coupled device


Fisher’s linear discriminant


Full width at half-maximum


Genetic algorithm based on LDA


Liquid crystal tunable filters


Lorentzian distribution


Linear discriminant analysis


Moisture content


Mahalanobis distance


Near infrared


Principal component analysis


Partial least square


PLS discriminant analysis


PLS regression




Red, green, blue colour space


Red, green, blue, infrared


Spectral angle mapper


Spectral information divergence


Soluble solids content


Titratable acid


Tagged image file format





This work was partially funded by the Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria de España (INIA) through research project RTA2009-00118-C02-01 and by the Ministerio de Ciencia e Innovación de España (MICINN) through research project DPI2010-19457, both projects with the support of European FEDER funds. This work was also been partially funded by the Universitat de València through project UV-INV-AE11-41271.


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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

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