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

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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.

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Abbreviations

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

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Acknowledgement

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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Authors and Affiliations

  1. Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias, Cra. Moncada-Náquera Km. 5, 46113, Moncada, Spain

    D. Lorente, S. Cubero, O. L. García-Navarrete & J. Blasco

  2. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain

    N. Aleixos

  3. Intelligent Data Analysis Laboratory, IDAL, Electronic Engineering Department, Universitat de València, Dr. Moliner 50, 46100, Burjassot (Valencia), Spain

    J. Gómez-Sanchis

  4. Departamento de Ingeniería Civil y Agrícola, Universidad Nacional de Colombia-Sede Bogotá, Carrera 30 No. 45-03, Edificio 214, Oficina 206, Bogotá, Colombia

    O. L. García-Navarrete

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  1. D. Lorente
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  2. N. Aleixos
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  3. J. Gómez-Sanchis
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  4. S. Cubero
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  5. O. L. García-Navarrete
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  6. J. Blasco
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Correspondence to J. Blasco.

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Lorente, D., Aleixos, N., Gómez-Sanchis, J. et al. Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment. Food Bioprocess Technol 5, 1121–1142 (2012). https://doi.org/10.1007/s11947-011-0725-1

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  • DOI: https://doi.org/10.1007/s11947-011-0725-1

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