Tomato sorting using independent component analysis on spectral images

被引：45

作者：

Polder, G

van der Heijden, GWAM

Young, IT

机构：

[1] Wageningen UR, Biometris, NL-6700 AC Wageningen, Netherlands

[2] Delft Univ Technol, Dept Imaging Sci & Technol, Pattern Recognit Grp, NL-2628 CJ Delft, Netherlands

来源：

REAL-TIME IMAGING | 2003年 / 9卷 / 04期

关键词：

D O I：

10.1016/j.rti.2003.09.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2003 Elsevier Ltd. All rights reserved.

引用

页码：253 / 259

页数：7

共 13 条

[1] Correlation of lycopene measured by HPLC with the L*, a* b* color readings of a hydroponic tomato and the relationship of maturity with color and lycopene content [J].