Food and Bioprocess Technology

, Volume 5, Issue 4, pp 1121–1142

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

DOI: 10.1007/s11947-011-0725-1

Cite this article as:
Lorente, D., Aleixos, N., Gómez-Sanchis, J. et al. Food Bioprocess Technol (2012) 5: 1121. doi:10.1007/s11947-011-0725-1

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 visionFruitsVegetablesQualityNon-destructive inspectionImage analysisHyperspectral imagingMultispectral 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

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