Tomato sorting using independent component analysis on spectral images

G. Polder; G.W.A.M. van der Heijden; I.T. Young

doi:10.1016/j.rti.2003.09.008

Tomato sorting using independent component analysis on spectral images

G. Polder, G.W.A.M. van der Heijden, I.T. Young

Research output: Contribution to journal › Article › Academic › peer-review

55 Citations (Scopus)

Abstract

Independent Component Analysis is one of the most widely used methods for blind source separation. In this paper we use this technique to estimate the most important compounds which play a role in the ripening of tomatoes. Spectral images of tomatoes were analyzed. Two main independent components were found. These components resemble the actual absorption spectra of lycopene and chlorophyll. Concentration images of these compounds show increase of one compound and decrease of the other during ripening. The method can be implemented in an unsupervised real time sorting machine, using the total compound concentrations and the spatial distribution of the concentrations as criteria

Original language	English
Pages (from-to)	253-259
Journal	Real-Time Imaging
Volume	9
Issue number	4
DOIs	https://doi.org/10.1016/j.rti.2003.09.008
Publication status	Published - 2003

Keywords

algorithms
lycopene
products

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10.1016/j.rti.2003.09.008

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title = "Tomato sorting using independent component analysis on spectral images",

abstract = "Independent Component Analysis is one of the most widely used methods for blind source separation. In this paper we use this technique to estimate the most important compounds which play a role in the ripening of tomatoes. Spectral images of tomatoes were analyzed. Two main independent components were found. These components resemble the actual absorption spectra of lycopene and chlorophyll. Concentration images of these compounds show increase of one compound and decrease of the other during ripening. The method can be implemented in an unsupervised real time sorting machine, using the total compound concentrations and the spatial distribution of the concentrations as criteria",

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author = "G. Polder and {van der Heijden}, G.W.A.M. and I.T. Young",

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AU - Polder, G.

AU - van der Heijden, G.W.A.M.

AU - Young, I.T.

PY - 2003

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N2 - Independent Component Analysis is one of the most widely used methods for blind source separation. In this paper we use this technique to estimate the most important compounds which play a role in the ripening of tomatoes. Spectral images of tomatoes were analyzed. Two main independent components were found. These components resemble the actual absorption spectra of lycopene and chlorophyll. Concentration images of these compounds show increase of one compound and decrease of the other during ripening. The method can be implemented in an unsupervised real time sorting machine, using the total compound concentrations and the spatial distribution of the concentrations as criteria

AB - Independent Component Analysis is one of the most widely used methods for blind source separation. In this paper we use this technique to estimate the most important compounds which play a role in the ripening of tomatoes. Spectral images of tomatoes were analyzed. Two main independent components were found. These components resemble the actual absorption spectra of lycopene and chlorophyll. Concentration images of these compounds show increase of one compound and decrease of the other during ripening. The method can be implemented in an unsupervised real time sorting machine, using the total compound concentrations and the spatial distribution of the concentrations as criteria

KW - algorithms

KW - lycopene

KW - products

U2 - 10.1016/j.rti.2003.09.008

DO - 10.1016/j.rti.2003.09.008

M3 - Article

SN - 1077-2014

VL - 9

SP - 253

EP - 259

JO - Real-Time Imaging

JF - Real-Time Imaging

IS - 4

ER -

Tomato sorting using independent component analysis on spectral images

Abstract

Keywords

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