<b>Image analysis for composition monitoring. Commercial blends of olive and soybean oil</b> - doi: 10.4025/actascitechnol.v35i2.15216

Autores

  • Jessica Kehrig Fernandes Universidade Federal do Parana
  • Tiemi Umebara Universidade Federal do Parana
  • Marcelo Kaminski Lenzi Universidade Federal do Parana
  • Ediely Teixeira Silva Universidade Federal do Parana

DOI:

https://doi.org/10.4025/actascitechnol.v35i2.15216

Palavras-chave:

edible oils, mixture, sensor, spectroscopy, RGB

Resumo

Olive oil represents an important component of a healthy and balanced dietary. Due to commercial features, characterization of pure olive oil and commercial mixtures represents an important challenge. Reported techniques can successfully quantify components in concentrations lower than 1%, but may present long delays, too many purification steps or use expensive equipment. Image analysis represents an important characterization technique for food science and technology. By coupling image and UV-VIS spectroscopy analysis, models with linear dependence on parameters were developed and could successfully describe the mixture concentration in the range of 0-100% in mass of olive oil content.
A validation sample, containing 25% in mass of olive oil, not used for parameter estimation, was also used for testing the proposed procedure, leading to a prediction of 24.8 ± 0.6. Due to image analysis results,  3-parameter-based models considering only R and G components were developed for olive oil content prediction in mixtures with up to 70% in mass of olive oil, the same test sample was used and its concentration was predicted as 24.5 ± 1.2. These results show that image analysis represents a promising technique for on-line/in-line monitoring of blending process of olive soybean oil for commercial mixtures.

 

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Publicado

2012-12-20

Como Citar

Fernandes, J. K., Umebara, T., Lenzi, M. K., & Silva, E. T. (2012). <b>Image analysis for composition monitoring. Commercial blends of olive and soybean oil</b> - doi: 10.4025/actascitechnol.v35i2.15216. Acta Scientiarum. Technology, 35(2), 317–324. https://doi.org/10.4025/actascitechnol.v35i2.15216

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Seção

Engenharia Quí­mica

 

0.8
2019CiteScore
 
 
36th percentile
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0.8
2019CiteScore
 
 
36th percentile
Powered by  Scopus

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