Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks

ElMasry, Gamal; Wang, Ning; Vigneault, Clément

doi:10.1016/j.postharvbio.2008.11.008

Cited by 225 publications

(97 citation statements)

References 18 publications

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“…In this paper, 9 abnormal samples are removed by using Monte Carlo sampling (MCS) algorithm [13,14] . To weaken or eliminate the impact on the spectra from all kinds of aimless factors such as baseline drift, scattering, it is necessary to preprocess the spectra collected by the hyperspectral imager.…”

Section: Data Preprocessingmentioning

confidence: 99%

Hyperspectral Determination of Reducing Sugar in Potatoes Based on CARS

Jiang¹,

Fang²,

Wang³

et al. 2016

IJHIT

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Section: Data Preprocessingmentioning

confidence: 99%

Hyperspectral Determination of Reducing Sugar in Potatoes Based on CARS

Jiang¹,

Fang²,

Wang³

et al. 2016

IJHIT

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“…By modelling the evolution of the cooking front over time, the optimal cooking time could be predicted with less than 10% relative error. ElMasry et al (2009) detected chilling injury and predicted firmness in Red Delicious apples using a hyperspectral imaging system (400-1000 nm) and ANN techniques. Experimental results demonstrated that a spectral imaging system associated with ANN could successfully distinguish between chillinginjured apples and normal apples (98.4% accuracy), as well as detect changes in firmness (r = 0.92).…”

Section: Estimation Of Internal Quality Parametersmentioning

confidence: 99%

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

Lorente

Aleixos

Gómez-Sanchís

et al. 2011

Food Bioprocess Technol

451

190

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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 data sets 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, nondestructive 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 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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“…In order to reduce the interference of temperature change of light source on the image, when a total of acquisition image arrived at 20 each time, it must be gathered a whole white calibration image and a whole black calibration image, and the hyperspectral image was obtained and calibrated according to eqn. (2) [11][12][13][14][15].…”

Section: Hyperspectral Image Correctionmentioning

confidence: 99%

Section: The Establishment Of Regression Modelmentioning

confidence: 99%