Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data

Sara Sharifzadeh, Line Katrine Harder Clemmensen, Bjarne Kjær Ersbøll, Mabel Virginia Martinez Vega

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Abstract

Quality monitoring of the food items by spectroscopy provides information in a large number of wavelengths including highly correlated and redundant information. Although increasing the information, the increase in the number of wavelengths causes the vision set-up to be more complex and expensive. In this paper, three sparse regression methods; lasso, elastic-net and fused lasso are employed for estimation of the chemical and physical characteristics of one apple cultivar using their high dimensional spectroscopic measurements. The use of sparse regression reduces the number of required wavelengths for prediction and thus, simplifies the required vision set-up. It is shown that, considering a tradeoff between the number of selected bands and the corresponding validation performance during the training step can result in a significant reduction in the number of bands at a small price in the test performance. Furthermore, appropriate regression methods for different number of bands and spectrophotometer design are determined.
Original languageEnglish
Title of host publication2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP)
PublisherIEEE
Publication date2013
Pages11-14
ISBN (Print)978-1-4799-0941-4
DOIs
Publication statusPublished - 2013
Event20th International Conference on Systems, Signals and Image Processing (IWSSIP 2013) - Bucharest, Romania
Duration: 7 Jul 20139 Jul 2013
http://iwssip.dsp.pub.ro/

Conference

Conference20th International Conference on Systems, Signals and Image Processing (IWSSIP 2013)
CountryRomania
CityBucharest
Period07/07/201309/07/2013
Internet address

Keywords

  • Sparse regression
  • Spectroscopy
  • Lasso
  • Elastic-net
  • Fused lasso

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