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Qualitative properties of roasting defect beans and development of its classification methods by hyperspectral imaging technology.
Cho, Jeong-Seok; Bae, Hyung-Jin; Cho, Byoung-Kwan; Moon, Kwang-Deog.
Afiliação
  • Cho JS; Department of Food Science and Technology, Kyungpook National University, 80 Daehak-ro, Daegu 702-701, South Korea.
  • Bae HJ; Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 305-764, South Korea.
  • Cho BK; Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 305-764, South Korea.
  • Moon KD; Department of Food Science and Technology, Kyungpook National University, 80 Daehak-ro, Daegu 702-701, South Korea. Electronic address: kdmoon@knu.ac.kr.
Food Chem ; 220: 505-509, 2017 Apr 01.
Article em En | MEDLINE | ID: mdl-27855931
ABSTRACT
Qualitative properties of roasting defect coffee beans and their classification methods were studied using hyperspectral imaging (HSI). The roasting defect beans were divided into 5 groups medium roasting (Cont), under developed (RD-1), over roasting (RD-2), interior under developed (RD-3), and interior scorching (RD-4). The following qualitative properties were assayed browning index (BI), moisture content (MC), chlorogenic acid (CA), trigonelline (TG), and caffeine (CF) content. Their HSI spectra (1000-1700nm) were also analysed to develop the classification methods of roasting defect beans. RD-2 showed the highest BI and the lowest MC, CA, and TG content. The accuracy of classification model of partial least-squares discriminant was 86.2%. The most powerful wavelength to classify the defective beans was approximately 1420nm (related to OH bond). The HSI reflectance values at 1420nm showed similar tendency with MC, enabling the use of this technology to classify the roasting defect beans.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Culinária / Coffea Tipo de estudo: Prognostic_studies / Qualitative_research Idioma: En Revista: Food Chem Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Culinária / Coffea Tipo de estudo: Prognostic_studies / Qualitative_research Idioma: En Revista: Food Chem Ano de publicação: 2017 Tipo de documento: Article