Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment
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.
KeywordsComputer 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
Fisher’s linear discriminant
Full width at half-maximum
Genetic algorithm based on LDA
Liquid crystal tunable filters
Linear discriminant analysis
Principal component analysis
Partial least square
PLS discriminant analysis
Red, green, blue colour space
Red, green, blue, infrared
Spectral angle mapper
Spectral information divergence
Soluble solids content
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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